Thu Feb 17 06:07:51 EST 2022 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61 setenv SUBJECTS_DIR /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/bin/recon-all -subjid sub-LALM61_ses-LALM61 -i /autofs/vast/bandlab/studies/kyoto_preliminary/data/rawdata/sub-LALM61/sub-LALM61_ses-LALM61/anat/sub-LALM61_ses-LALM61_rec-uni-images_MP2RAGE.nii.gz -all subjid sub-LALM61_ses-LALM61 setenv SUBJECTS_DIR /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer FREESURFER_HOME /autofs/cluster/freesurfer/centos7_x86_64/7.2.0 Actual FREESURFER_HOME /autofs/cluster/freesurfer/centos7_x86_64/7.2.0 build-stamp.txt: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b Linux erso.nmr.mgh.harvard.edu 4.18.0-365.el8.x86_64 #1 SMP Thu Feb 10 16:11:23 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux cputime unlimited filesize unlimited datasize unlimited stacksize unlimited coredumpsize 0 kbytes memoryuse unlimited vmemoryuse unlimited descriptors 131070 memorylocked 64 kbytes maxproc 62607 maxlocks unlimited maxsignal 62607 maxmessage 819200 maxnice 0 maxrtprio 0 maxrttime unlimited total used free shared buff/cache available Mem: 15Gi 3.8Gi 6.1Gi 120Mi 5.4Gi 11Gi Swap: 15Gi 1.4Gi 14Gi ######################################## program versions used 7.2.0 (freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b) 7.2.0 ProgramName: lta_convert ProgramArguments: lta_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_and ProgramArguments: mri_and -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_annotation2label ProgramArguments: mri_annotation2label -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_aparc2aseg ProgramArguments: mri_aparc2aseg -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_surf2volseg ProgramArguments: mri_surf2volseg -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_binarize ProgramArguments: mri_binarize -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_ca_label ProgramArguments: mri_ca_label -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_ca_normalize ProgramArguments: mri_ca_normalize -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_ca_register ProgramArguments: mri_ca_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_cc ProgramArguments: mri_cc -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_compute_overlap ProgramArguments: mri_compute_overlap -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_compute_seg_overlap ProgramArguments: mri_compute_seg_overlap -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_concat ProgramArguments: mri_concat -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_concatenate_lta ProgramArguments: mri_concatenate_lta -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 mri_convert -all-info ProgramName: mri_convert ProgramArguments: mri_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_diff ProgramArguments: mri_diff -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_edit_wm_with_aseg ProgramArguments: mri_edit_wm_with_aseg -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_em_register ProgramArguments: mri_em_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_fill ProgramArguments: mri_fill -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_fuse_segmentations ProgramArguments: mri_fuse_segmentations -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_fwhm ProgramArguments: mri_fwhm -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_gcut ProgramArguments: mri_gcut -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_info ProgramArguments: mri_info -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_label2label ProgramArguments: mri_label2label -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_label2vol ProgramArguments: mri_label2vol -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_log_likelihood ProgramArguments: mri_log_likelihood -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_mask ProgramArguments: mri_mask -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_matrix_multiply ProgramArguments: mri_matrix_multiply -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_normalize ProgramArguments: mri_normalize -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_normalize_tp2 ProgramArguments: mri_normalize_tp2 -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_or ProgramArguments: mri_or -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_relabel_hypointensities ProgramArguments: mri_relabel_hypointensities -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_relabel_nonwm_hypos ProgramArguments: mri_relabel_nonwm_hypos -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_remove_neck ProgramArguments: mri_remove_neck -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:52-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 7.2.0 ProgramName: mri_robust_register ProgramArguments: mri_robust_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 7.2.0 ProgramName: mri_robust_template ProgramArguments: mri_robust_template -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_anatomical_stats ProgramArguments: mris_anatomical_stats -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_ca_label ProgramArguments: mris_ca_label -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_calc ProgramArguments: mris_calc -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_convert ProgramArguments: mris_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_curvature ProgramArguments: mris_curvature -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_curvature_stats ProgramArguments: mris_curvature_stats -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_diff ProgramArguments: mris_diff -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_divide_parcellation ProgramArguments: mris_divide_parcellation -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_segment ProgramArguments: mri_segment -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_segstats ProgramArguments: mri_segstats -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_euler_number ProgramArguments: mris_euler_number -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_fix_topology ProgramArguments: mris_fix_topology -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_topo_fixer ProgramArguments: mris_topo_fixer -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_jacobian ProgramArguments: mris_jacobian -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_label2annot ProgramArguments: mris_label2annot -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_left_right_register ProgramArguments: mris_left_right_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_place_surface ProgramArguments: mris_place_surface -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mrisp_paint ProgramArguments: mrisp_paint -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_register ProgramArguments: mris_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_smooth ProgramArguments: mris_smooth -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_sphere ProgramArguments: mris_sphere -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_surface_stats ProgramArguments: mris_surface_stats -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_stats2seg ProgramArguments: mri_stats2seg -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_thickness ProgramArguments: mris_thickness -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_thickness_diff ProgramArguments: mris_thickness_diff -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_topo_fixer ProgramArguments: mris_topo_fixer -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_surf2surf ProgramArguments: mri_surf2surf -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_surf2vol ProgramArguments: mri_surf2vol -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_surfcluster ProgramArguments: mri_surfcluster -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_volmask ProgramArguments: mris_volmask -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_tessellate ProgramArguments: mri_tessellate -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_vol2surf ProgramArguments: mri_vol2surf -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_vol2vol ProgramArguments: mri_vol2vol -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_voldiff ProgramArguments: mri_voldiff -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_watershed ProgramArguments: mri_watershed -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: tkregister2 ProgramArguments: tkregister2_cmdl -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 mri_motion_correct.fsl 7.2.0 mri_convert -all-info ProgramName: mri_convert ProgramArguments: mri_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/02/17-11:07:53-GMT BuildTime: Jul 20 2021 18:45:50 BuildStamp: freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b User: jj1006 Machine: erso.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 4.18.0-365.el8.x86_64 CompilerName: GCC CompilerVersion: 4.8.5 Program nu_correct, built from: Package MNI N3, version 1.12.0, compiled by nicks@terrier (x86_64-unknown-linux-gnu) on 2015-06-19 at 01:25:34 ####################################### GCADIR /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average GCA RB_all_2020-01-02.gca GCASkull RB_all_withskull_2020_01_02.gca AvgCurvTif folding.atlas.acfb40.noaparc.i12.2016-08-02.tif GCSDIR /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average GCS DKaparc.atlas.acfb40.noaparc.i12.2016-08-02.gcs ####################################### /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61 mri_convert /autofs/vast/bandlab/studies/kyoto_preliminary/data/rawdata/sub-LALM61/sub-LALM61_ses-LALM61/anat/sub-LALM61_ses-LALM61_rec-uni-images_MP2RAGE.nii.gz /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/orig/001.mgz mri_convert /autofs/vast/bandlab/studies/kyoto_preliminary/data/rawdata/sub-LALM61/sub-LALM61_ses-LALM61/anat/sub-LALM61_ses-LALM61_rec-uni-images_MP2RAGE.nii.gz /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/orig/001.mgz reading from /autofs/vast/bandlab/studies/kyoto_preliminary/data/rawdata/sub-LALM61/sub-LALM61_ses-LALM61/anat/sub-LALM61_ses-LALM61_rec-uni-images_MP2RAGE.nii.gz... TR=4300.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (0.995476, 0.0202984, 0.0928144) j_ras = (0.00127575, 0.973966, -0.226688) k_ras = (-0.0949996, 0.225781, 0.969535) writing to /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/orig/001.mgz... @#@FSTIME 2022:02:17:06:07:54 mri_convert N 2 e 1.83 S 0.01 U 1.38 P 76% M 28416 F 0 R 693 W 0 c 15 w 1014 I 34384 O 34600 L 1.10 1.10 1.09 @#@FSLOADPOST 2022:02:17:06:07:56 mri_convert N 2 1.10 1.10 1.09 #-------------------------------------------- #@# MotionCor Thu Feb 17 06:07:56 EST 2022 Found 1 runs /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/orig/001.mgz Checking for (invalid) multi-frame inputs... Only one run found so motion correction will not be performed. I'll copy the run to rawavg and continue. cp /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/orig/001.mgz /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/rawavg.mgz /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61 mri_convert /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/rawavg.mgz /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/orig.mgz --conform mri_convert /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/rawavg.mgz /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/orig.mgz --conform reading from /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/rawavg.mgz... TR=4300.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (0.995476, 0.0202984, 0.0928144) j_ras = (0.00127575, 0.973966, -0.226688) k_ras = (-0.0949996, 0.225781, 0.969535) changing data type from short to uchar (noscale = 0)... MRIchangeType: Building histogram 0 4091 1000, flo=0, fhi=0.999, dest_type=0 Reslicing using trilinear interpolation writing to /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/orig.mgz... @#@FSTIME 2022:02:17:06:08:00 mri_convert N 3 e 2.56 S 0.01 U 2.44 P 95% M 39304 F 0 R 6504 W 0 c 50 w 318 I 0 O 13424 L 1.01 1.09 1.08 @#@FSLOADPOST 2022:02:17:06:08:02 mri_convert N 3 1.01 1.09 1.08 mri_add_xform_to_header -c /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/transforms/talairach.xfm /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/orig.mgz /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/orig.mgz INFO: extension is mgz @#@FSTIME 2022:02:17:06:08:02 mri_add_xform_to_header N 4 e 0.70 S 0.00 U 0.60 P 86% M 22860 F 2 R 962 W 0 c 26 w 297 I 304 O 13424 L 1.01 1.09 1.08 @#@FSLOADPOST 2022:02:17:06:08:03 mri_add_xform_to_header N 4 1.01 1.08 1.08 #-------------------------------------------- #@# Talairach Thu Feb 17 06:08:03 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_nu_correct.mni --no-rescale --i orig.mgz --o orig_nu.mgz --ants-n4 --n 1 --proto-iters 1000 --distance 50 /usr/bin/bc /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/bin/mri_nu_correct.mni --no-rescale --i orig.mgz --o orig_nu.mgz --ants-n4 --n 1 --proto-iters 1000 --distance 50 nIters 1 mri_nu_correct.mni 7.2.0 Linux erso.nmr.mgh.harvard.edu 4.18.0-365.el8.x86_64 #1 SMP Thu Feb 10 16:11:23 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux Thu Feb 17 06:08:03 EST 2022 tmpdir is ./tmp.mri_nu_correct.mni.2102749 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri AntsN4BiasFieldCorrectionFs -i orig.mgz -o ./tmp.mri_nu_correct.mni.2102749/nu0.mgz --dtype uchar AntsN4BiasFieldCorrectionFs done mri_convert ./tmp.mri_nu_correct.mni.2102749/nu0.mgz orig_nu.mgz --like orig.mgz --conform mri_convert ./tmp.mri_nu_correct.mni.2102749/nu0.mgz orig_nu.mgz --like orig.mgz --conform reading from ./tmp.mri_nu_correct.mni.2102749/nu0.mgz... TR=4300.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 1.49012e-08, -1) k_ras = (0, 1, 0) INFO: transform src into the like-volume: orig.mgz writing to orig_nu.mgz... Thu Feb 17 06:10:52 EST 2022 mri_nu_correct.mni done @#@FSTIME 2022:02:17:06:08:03 mri_nu_correct.mni N 12 e 169.22 S 0.13 U 168.45 P 99% M 519880 F 3 R 76844 W 0 c 2404 w 865 I 13696 O 26664 L 1.01 1.08 1.08 @#@FSLOADPOST 2022:02:17:06:10:52 mri_nu_correct.mni N 12 1.00 1.04 1.06 talairach_avi --i orig_nu.mgz --xfm transforms/talairach.auto.xfm talairach_avi log file is transforms/talairach_avi.log... Started at Thu Feb 17 06:10:52 EST 2022 Ended at Thu Feb 17 06:11:17 EST 2022 talairach_avi done @#@FSTIME 2022:02:17:06:10:52 talairach_avi N 4 e 24.47 S 0.38 U 15.55 P 65% M 255512 F 12 R 32732 W 0 c 236 w 7419 I 2240 O 296040 L 1.00 1.04 1.06 @#@FSLOADPOST 2022:02:17:06:11:17 talairach_avi N 4 1.00 1.04 1.06 cp transforms/talairach.auto.xfm transforms/talairach.xfm lta_convert --src orig.mgz --trg /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/mni305.cor.mgz --inxfm transforms/talairach.xfm --outlta transforms/talairach.xfm.lta --subject fsaverage --ltavox2vox 7.2.0 --src: orig.mgz src image (geometry). --trg: /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/mni305.cor.mgz trg image (geometry). --inmni: transforms/talairach.xfm input MNI/XFM transform. --outlta: transforms/talairach.xfm.lta output LTA. --s: fsaverage subject name --ltavox2vox: output LTA as VOX_TO_VOX transform. LTA read, type : 1 1.21360 -0.00721 0.10664 3.19257; 0.02324 1.11171 0.13918 -27.75854; -0.09636 -0.05190 1.16285 55.70420; 0.00000 0.00000 0.00000 1.00000; setting subject to fsaverage Writing LTA to file transforms/talairach.xfm.lta... lta_convert successful. #-------------------------------------------- #@# Talairach Failure Detection Thu Feb 17 06:11:19 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri talairach_afd -T 0.005 -xfm transforms/talairach.xfm talairach_afd: Talairach Transform: transforms/talairach.xfm OK (p=0.7228, pval=0.4932 >= threshold=0.0050) @#@FSTIME 2022:02:17:06:11:19 talairach_afd N 4 e 0.06 S 0.00 U 0.00 P 4% M 5272 F 10 R 196 W 0 c 0 w 24 I 2416 O 0 L 0.92 1.02 1.06 @#@FSLOADPOST 2022:02:17:06:11:19 talairach_afd N 4 0.92 1.02 1.06 awk -f /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/bin/extract_talairach_avi_QA.awk /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/transforms/talairach_avi.log tal_QC_AZS /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/transforms/talairach_avi.log TalAviQA: 0.96365 z-score: -3 #-------------------------------------------- #@# Nu Intensity Correction Thu Feb 17 06:11:19 EST 2022 mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --n 2 --ants-n4 /usr/bin/bc /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/bin/mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --n 2 --ants-n4 nIters 2 mri_nu_correct.mni 7.2.0 Linux erso.nmr.mgh.harvard.edu 4.18.0-365.el8.x86_64 #1 SMP Thu Feb 10 16:11:23 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux Thu Feb 17 06:11:19 EST 2022 tmpdir is ./tmp.mri_nu_correct.mni.2103074 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri AntsN4BiasFieldCorrectionFs -i orig.mgz -o ./tmp.mri_nu_correct.mni.2103074/nu0.mgz --dtype uchar AntsN4BiasFieldCorrectionFs done mri_binarize --i ./tmp.mri_nu_correct.mni.2103074/nu0.mgz --min -1 --o ./tmp.mri_nu_correct.mni.2103074/ones.mgz 7.2.0 cwd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri cmdline mri_binarize --i ./tmp.mri_nu_correct.mni.2103074/nu0.mgz --min -1 --o ./tmp.mri_nu_correct.mni.2103074/ones.mgz sysname Linux hostname erso.nmr.mgh.harvard.edu machine x86_64 user jj1006 input ./tmp.mri_nu_correct.mni.2103074/nu0.mgz frame 0 nErode3d 0 nErode2d 0 output ./tmp.mri_nu_correct.mni.2103074/ones.mgz Binarizing based on threshold min -1 max +infinity binval 1 binvalnot 0 fstart = 0, fend = 0, nframes = 1 Starting parallel 1 Found 16777216 values in range Counting number of voxels in first frame Found 16777215 voxels in final mask Writing output to ./tmp.mri_nu_correct.mni.2103074/ones.mgz Count: 16777215 16777215.000000 16777216 99.999994 mri_binarize done mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.2103074/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.2103074/sum.junk --avgwf ./tmp.mri_nu_correct.mni.2103074/input.mean.dat 7.2.0 cwd cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.2103074/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.2103074/sum.junk --avgwf ./tmp.mri_nu_correct.mni.2103074/input.mean.dat sysname Linux hostname erso.nmr.mgh.harvard.edu machine x86_64 user jj1006 whitesurfname white UseRobust 0 Loading ./tmp.mri_nu_correct.mni.2103074/ones.mgz Loading orig.mgz Voxel Volume is 1 mm^3 Generating list of segmentation ids Found 1 segmentations Computing statistics for each segmentation Reporting on 1 segmentations Using PrintSegStat Computing spatial average of each frame 0 Writing to ./tmp.mri_nu_correct.mni.2103074/input.mean.dat mri_segstats done mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.2103074/ones.mgz --i ./tmp.mri_nu_correct.mni.2103074/nu0.mgz --sum ./tmp.mri_nu_correct.mni.2103074/sum.junk --avgwf ./tmp.mri_nu_correct.mni.2103074/output.mean.dat 7.2.0 cwd cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.2103074/ones.mgz --i ./tmp.mri_nu_correct.mni.2103074/nu0.mgz --sum ./tmp.mri_nu_correct.mni.2103074/sum.junk --avgwf ./tmp.mri_nu_correct.mni.2103074/output.mean.dat sysname Linux hostname erso.nmr.mgh.harvard.edu machine x86_64 user jj1006 whitesurfname white UseRobust 0 Loading ./tmp.mri_nu_correct.mni.2103074/ones.mgz Loading ./tmp.mri_nu_correct.mni.2103074/nu0.mgz Voxel Volume is 1 mm^3 Generating list of segmentation ids Found 1 segmentations Computing statistics for each segmentation Reporting on 1 segmentations Using PrintSegStat Computing spatial average of each frame 0 Writing to ./tmp.mri_nu_correct.mni.2103074/output.mean.dat mri_segstats done mris_calc -o ./tmp.mri_nu_correct.mni.2103074/nu0.mgz ./tmp.mri_nu_correct.mni.2103074/nu0.mgz mul 1.01381753279869637647 Saving result to './tmp.mri_nu_correct.mni.2103074/nu0.mgz' (type = MGH ) [ ok ] mri_convert ./tmp.mri_nu_correct.mni.2103074/nu0.mgz nu.mgz --like orig.mgz mri_convert ./tmp.mri_nu_correct.mni.2103074/nu0.mgz nu.mgz --like orig.mgz reading from ./tmp.mri_nu_correct.mni.2103074/nu0.mgz... TR=4300.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 1.49012e-08, -1) k_ras = (0, 1, 0) INFO: transform src into the like-volume: orig.mgz writing to nu.mgz... mri_make_uchar nu.mgz transforms/talairach.xfm nu.mgz type change took 0 minutes and 5 seconds. mapping ( 3, 155) to ( 3, 110) Thu Feb 17 06:14:27 EST 2022 mri_nu_correct.mni done @#@FSTIME 2022:02:17:06:11:19 mri_nu_correct.mni N 9 e 187.96 S 0.39 U 186.27 P 99% M 613444 F 28 R 247591 W 0 c 3019 w 2210 I 27296 O 76968 L 0.92 1.02 1.06 @#@FSLOADPOST 2022:02:17:06:14:27 mri_nu_correct.mni N 9 1.02 1.03 1.05 mri_add_xform_to_header -c /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/transforms/talairach.xfm nu.mgz nu.mgz INFO: extension is mgz @#@FSTIME 2022:02:17:06:14:27 mri_add_xform_to_header N 4 e 0.80 S 0.01 U 0.61 P 78% M 22964 F 1 R 964 W 0 c 10 w 372 I 12544 O 12296 L 1.02 1.03 1.05 @#@FSLOADPOST 2022:02:17:06:14:28 mri_add_xform_to_header N 4 1.02 1.03 1.05 #-------------------------------------------- #@# Intensity Normalization Thu Feb 17 06:14:28 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_normalize -g 1 -seed 1234 -mprage nu.mgz T1.mgz using max gradient = 1.000 setting seed for random number genererator to 1234 assuming input volume is MGH (Van der Kouwe) MP-RAGE reading mri_src from nu.mgz... normalizing image... NOT doing gentle normalization with control points/label talairach transform 1.21360 -0.00721 0.10664 3.19257; 0.02324 1.11171 0.13918 -27.75854; -0.09636 -0.05190 1.16285 55.70420; 0.00000 0.00000 0.00000 1.00000; processing without aseg, no1d=0 MRInormInit(): INFO: Modifying talairach volume c_(r,a,s) based on average_305 MRInormalize(): MRIsplineNormalize(): npeaks = 17 Starting OpenSpline(): npoints = 17 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Iterating 2 times --------------------------------- 3d normalization pass 1 of 2 white matter peak found at 110 white matter peak found at 98 gm peak at 74 (73), valley at 19 (18) csf peak at 37, setting threshold to 61 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... --------------------------------- 3d normalization pass 2 of 2 white matter peak found at 110 white matter peak found at 95 gm peak at 72 (71), valley at 18 (17) csf peak at 36, setting threshold to 60 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Done iterating --------------------------------- writing output to T1.mgz 3D bias adjustment took 1 minutes and 44 seconds. @#@FSTIME 2022:02:17:06:14:28 mri_normalize N 7 e 105.02 S 0.22 U 104.45 P 99% M 584004 F 0 R 195429 W 0 c 1585 w 385 I 12296 O 12072 L 1.02 1.03 1.05 @#@FSLOADPOST 2022:02:17:06:16:13 mri_normalize N 7 1.02 1.03 1.04 #-------------------------------------------- #@# Skull Stripping Thu Feb 17 06:16:13 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_em_register -skull nu.mgz /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_withskull_2020_01_02.gca transforms/talairach_with_skull.lta aligning to atlas containing skull, setting unknown_nbr_spacing = 5 == Number of threads available to mri_em_register for OpenMP = 1 == reading 1 input volumes... logging results to talairach_with_skull.log reading '/autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_withskull_2020_01_02.gca'... GCAread took 0 minutes and 1 seconds. average std = 23.0 using min determinant for regularization = 52.8 0 singular and 9205 ill-conditioned covariance matrices regularized reading 'nu.mgz'... freeing gibbs priors...done. accounting for voxel sizes in initial transform bounding unknown intensity as < 8.9 or > 556.0 total sample mean = 77.3 (1403 zeros) ************************************************ spacing=8, using 3292 sample points, tol=1.00e-05... ************************************************ register_mri: find_optimal_transform find_optimal_transform: nsamples 3292, passno 0, spacing 8 resetting wm mean[0]: 100 --> 108 resetting gm mean[0]: 61 --> 61 input volume #1 is the most T1-like using real data threshold=4.0 skull bounding box = (29, 21, 22) --> (228, 234, 231) finding center of left hemi white matter using (95, 92, 127) as brain centroid of Right_Cerebral_White_Matter... MRImask(): AllowDiffGeom = 1 mean wm in atlas = 108, using box (70,66,101) --> (119, 118,152) to find MRI wm before smoothing, mri peak at 103 robust fit to distribution - 103 +- 5.0 after smoothing, mri peak at 103, scaling input intensities by 1.049 scaling channel 0 by 1.04854 initial log_p = -4.607 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.658711 @ (0.000, 0.000, 0.000) max log p = -4.577228 @ (-5.263, -5.263, -5.263) max log p = -4.490862 @ (2.632, 2.632, -7.895) max log p = -4.490862 @ (0.000, 0.000, 0.000) max log p = -4.487232 @ (-0.658, -0.658, -0.658) max log p = -4.487232 @ (0.000, 0.000, 0.000) max log p = -4.487232 @ (0.000, 0.000, 0.000) max log p = -4.487232 @ (0.000, 0.000, 0.000) Found translation: (-3.3, -3.3, -13.8): log p = -4.487 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.247, old_max_log_p =-4.487 (thresh=-4.5) 1.22567 0.16136 0.00000 -48.22629; -0.16136 1.22567 0.00000 -7.88463; 0.00000 0.00000 1.07500 -23.18954; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 22 seconds. **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.247, old_max_log_p =-4.247 (thresh=-4.2) 1.22567 0.16136 0.00000 -48.22629; -0.16136 1.22567 0.00000 -7.88463; 0.00000 0.00000 1.07500 -23.18954; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 iteration took 1 minutes and 21 seconds. **************************************** Nine parameter search. iteration 2 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.159, old_max_log_p =-4.247 (thresh=-4.2) 1.22919 0.12525 -0.03598 -42.90461; -0.12613 1.29761 0.07539 -31.49179; 0.05146 -0.07586 1.05111 -16.15393; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 15 seconds. **************************************** Nine parameter search. iteration 3 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.157, old_max_log_p =-4.159 (thresh=-4.2) 1.22919 0.12525 -0.03598 -42.90461; -0.12438 1.29444 0.10974 -33.68968; 0.05660 -0.12049 1.06774 -14.56015; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 iteration took 1 minutes and 15 seconds. **************************************** Nine parameter search. iteration 4 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.138, old_max_log_p =-4.157 (thresh=-4.2) 1.22567 0.12248 -0.01847 -44.76237; -0.12359 1.29380 0.12735 -36.86758; 0.03842 -0.14335 1.06374 -7.68268; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 7 seconds. **************************************** Nine parameter search. iteration 5 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.131, old_max_log_p =-4.138 (thresh=-4.1) 1.22423 0.12234 -0.01845 -44.56284; -0.12330 1.29077 0.12705 -36.44810; 0.03842 -0.14335 1.06374 -7.68268; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 7 seconds. **************************************** Nine parameter search. iteration 6 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.131, old_max_log_p =-4.131 (thresh=-4.1) 1.22423 0.12234 -0.01845 -44.56284; -0.12330 1.29077 0.12705 -36.44810; 0.03842 -0.14335 1.06374 -7.68268; 0.00000 0.00000 0.00000 1.00000; min search scale 0.025000 reached *********************************************** Computing MAP estimate using 3292 samples... *********************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-05 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.22423 0.12234 -0.01845 -44.56284; -0.12330 1.29077 0.12705 -36.44810; 0.03842 -0.14335 1.06374 -7.68268; 0.00000 0.00000 0.00000 1.00000; nsamples 3292 Quasinewton: input matrix 1.22423 0.12234 -0.01845 -44.56284; -0.12330 1.29077 0.12705 -36.44810; 0.03842 -0.14335 1.06374 -7.68268; 0.00000 0.00000 0.00000 1.00000; IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 3 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 009: -log(p) = -0.0 tol 0.000010 Resulting transform: 1.22423 0.12234 -0.01845 -44.56284; -0.12330 1.29077 0.12705 -36.44810; 0.03842 -0.14335 1.06374 -7.68268; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -4.131 (old=-4.607) transform before final EM align: 1.22423 0.12234 -0.01845 -44.56284; -0.12330 1.29077 0.12705 -36.44810; 0.03842 -0.14335 1.06374 -7.68268; 0.00000 0.00000 0.00000 1.00000; ************************************************** EM alignment process ... Computing final MAP estimate using 364986 samples. ************************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-07 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.22423 0.12234 -0.01845 -44.56284; -0.12330 1.29077 0.12705 -36.44810; 0.03842 -0.14335 1.06374 -7.68268; 0.00000 0.00000 0.00000 1.00000; nsamples 364986 Quasinewton: input matrix 1.22423 0.12234 -0.01845 -44.56284; -0.12330 1.29077 0.12705 -36.44810; 0.03842 -0.14335 1.06374 -7.68268; 0.00000 0.00000 0.00000 1.00000; IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 6 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 011: -log(p) = 4.6 tol 0.000000 final transform: 1.22423 0.12234 -0.01845 -44.56284; -0.12330 1.29077 0.12705 -36.44810; 0.03842 -0.14335 1.06374 -7.68268; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach_with_skull.lta... #VMPC# mri_em_register VmPeak 799936 FSRUNTIME@ mri_em_register 0.1592 hours 1 threads registration took 9 minutes and 33 seconds. @#@FSTIME 2022:02:17:06:16:13 mri_em_register N 4 e 573.31 S 0.63 U 571.70 P 99% M 628684 F 0 R 117340 W 0 c 8223 w 70 I 344 O 24 L 1.02 1.03 1.04 @#@FSLOADPOST 2022:02:17:06:25:47 mri_em_register N 4 1.01 1.03 1.02 mri_watershed -T1 -brain_atlas /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_withskull_2020_01_02.gca transforms/talairach_with_skull.lta T1.mgz brainmask.auto.mgz Mode: T1 normalized volume Mode: Use the information of atlas (default parms, --help for details) ********************************************************* The input file is T1.mgz The output file is brainmask.auto.mgz Weighting the input with atlas information before watershed *************************WATERSHED************************** Sorting... first estimation of the COG coord: x=130 y=116 z=125 r=97 first estimation of the main basin volume: 3915793 voxels Looking for seedpoints 2 found in the cerebellum 14 found in the rest of the brain global maximum in x=145, y=109, z=85, Imax=255 CSF=14, WM_intensity=110, WM_VARIANCE=5 WM_MIN=110, WM_HALF_MIN=110, WM_HALF_MAX=110, WM_MAX=110 preflooding height equal to 10 percent done. Analyze... main basin size=1840816178 voxels, voxel volume =1.000 = 1840816178 mmm3 = 1840816.128 cm3 done. PostAnalyze...Basin Prior 95 basins merged thanks to atlas ***** 0 basin(s) merged in 1 iteration(s) ***** 0 voxel(s) added to the main basin done. Weighting the input with prior template ****************TEMPLATE DEFORMATION**************** second estimation of the COG coord: x=139,y=174, z=143, r=117133 iterations ^^^^^^^^ couldn't find WM with original limits - expanding ^^^^^^ (2) Problem with the least square interpolation for CSF_MAX GLOBAL CSF_MIN=0, CSF_intensity=3, CSF_MAX=5 , nb = 16452 RIGHT_CER CSF_MIN=5, CSF_intensity=37, CSF_MAX=47 , nb = 198 LEFT_CER CSF_MIN=5, CSF_intensity=33, CSF_MAX=48 , nb = 126 RIGHT_BRAIN CSF_MIN=0, CSF_intensity=5, CSF_MAX=43 , nb = 4392 LEFT_BRAIN CSF_MIN=0, CSF_intensity=5, CSF_MAX=178 , nb = 5094 OTHER CSF_MIN=20, CSF_intensity=39, CSF_MAX=49 , nb = 1188 Problem with the least square interpolation in GM_MIN calculation. (2) Problem with the least square interpolation in GM_MIN calculation. Problem with the least square interpolation in GM_MIN calculation. (2) Problem with the least square interpolation in GM_MIN calculation. CSF_MAX TRANSITION GM_MIN GM GLOBAL before analyzing : 5, 7, 34, 58 after analyzing : 5, 25, 34, 33 RIGHT_CER before analyzing : 47, 11, 0, 3 after analyzing : 11, 20, 25, 21 LEFT_CER before analyzing : 48, 8, 0, 3 after analyzing : 8, 19, 25, 20 RIGHT_BRAIN before analyzing : 43, 23, 22, 24 after analyzing : 23, 24, 25, 24 LEFT_BRAIN before analyzing : 178, 45, 37, 48 after analyzing : 27, 45, 45, 45 OTHER before analyzing : 49, 76, 85, 88 after analyzing : 49, 82, 85, 83 mri_strip_skull: done peeling brain highly tesselated surface with 10242 vertices matching...97 iterations *********************VALIDATION********************* curvature mean = -0.012, std = 0.011 curvature mean = 72.816, std = 14.640 No Rigid alignment: -atlas Mode Off (basic atlas / no registration) before rotation: sse = 37.54, sigma = 120.27 after rotation: sse = 37.54, sigma = 120.27 Localization of inacurate regions: Erosion-Dilation steps the sse mean is 50.03, its var is 95.50 before Erosion-Dilatation 44.88% of inacurate vertices after Erosion-Dilatation 0.00% of inacurate vertices Validation of the shape of the surface done. Scaling of atlas fields onto current surface fields ********FINAL ITERATIVE TEMPLATE DEFORMATION******** Compute Local values csf/gray Fine Segmentation...47 iterations mri_strip_skull: done peeling brain Brain Size = 2065981 voxels, voxel volume = 1.000 mm3 = 2065981 mmm3 = 2065.981 cm3 ****************************** Saving brainmask.auto.mgz done mri_watershed utimesec 16.377127 mri_watershed stimesec 0.184695 mri_watershed ru_maxrss 862168 mri_watershed ru_ixrss 0 mri_watershed ru_idrss 0 mri_watershed ru_isrss 0 mri_watershed ru_minflt 199836 mri_watershed ru_majflt 11 mri_watershed ru_nswap 0 mri_watershed ru_inblock 8376 mri_watershed ru_oublock 3240 mri_watershed ru_msgsnd 0 mri_watershed ru_msgrcv 0 mri_watershed ru_nsignals 0 mri_watershed ru_nvcsw 141 mri_watershed ru_nivcsw 347 mri_watershed done @#@FSTIME 2022:02:17:06:25:47 mri_watershed N 6 e 16.84 S 0.21 U 16.37 P 98% M 862168 F 11 R 199838 W 0 c 348 w 142 I 8376 O 3240 L 1.01 1.03 1.02 @#@FSLOADPOST 2022:02:17:06:26:04 mri_watershed N 6 1.01 1.02 1.02 cp brainmask.auto.mgz brainmask.mgz #------------------------------------- #@# EM Registration Thu Feb 17 06:26:05 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_em_register -uns 3 -mask brainmask.mgz nu.mgz /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_2020-01-02.gca transforms/talairach.lta setting unknown_nbr_spacing = 3 using MR volume brainmask.mgz to mask input volume... == Number of threads available to mri_em_register for OpenMP = 1 == reading 1 input volumes... logging results to talairach.log reading '/autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_2020-01-02.gca'... GCAread took 0 minutes and 1 seconds. average std = 7.2 using min determinant for regularization = 5.2 0 singular and 884 ill-conditioned covariance matrices regularized reading 'nu.mgz'... MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 freeing gibbs priors...done. accounting for voxel sizes in initial transform bounding unknown intensity as < 5.9 or > 519.0 total sample mean = 79.1 (1017 zeros) ************************************************ spacing=8, using 2841 sample points, tol=1.00e-05... ************************************************ register_mri: find_optimal_transform find_optimal_transform: nsamples 2841, passno 0, spacing 8 resetting wm mean[0]: 98 --> 107 resetting gm mean[0]: 61 --> 61 input volume #1 is the most T1-like using real data threshold=28.1 skull bounding box = (63, 67, 59) --> (190, 233, 186) finding center of left hemi white matter using (105, 122, 123) as brain centroid of Right_Cerebral_White_Matter... MRImask(): AllowDiffGeom = 1 mean wm in atlas = 107, using box (89,102,107) --> (120, 142,138) to find MRI wm before smoothing, mri peak at 103 robust fit to distribution - 102 +- 9.4 after smoothing, mri peak at 103, scaling input intensities by 1.039 scaling channel 0 by 1.03883 initial log_p = -4.157 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.187119 @ (0.000, 0.000, 0.000) max log p = -4.012898 @ (-5.263, -5.263, -15.789) max log p = -4.010502 @ (2.632, 2.632, 7.895) max log p = -3.967590 @ (1.316, -3.947, -1.316) max log p = -3.936298 @ (0.658, 1.974, 0.658) max log p = -3.936298 @ (0.000, 0.000, 0.000) max log p = -3.936298 @ (0.000, 0.000, 0.000) max log p = -3.936298 @ (0.000, 0.000, 0.000) Found translation: (-0.7, -4.6, -8.6): log p = -3.936 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.867, old_max_log_p =-3.936 (thresh=-3.9) 1.06580 0.13912 0.01831 -28.36835; -0.14032 1.05669 0.13912 -9.59914; 0.00000 -0.13053 0.99144 8.59403; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 11 seconds. **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.846, old_max_log_p =-3.867 (thresh=-3.9) 1.14574 0.14955 0.01969 -40.00615; -0.14955 1.14453 0.00915 3.00583; -0.01831 0.00852 1.00112 -7.40105; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 11 seconds. **************************************** Nine parameter search. iteration 2 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.820, old_max_log_p =-3.846 (thresh=-3.8) 1.14574 0.14955 0.01969 -40.00615; -0.16077 1.23037 0.00984 -5.71551; -0.01831 0.00852 1.00112 -7.40105; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 11 seconds. **************************************** Nine parameter search. iteration 3 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.820, old_max_log_p =-3.820 (thresh=-3.8) 1.14574 0.14955 0.01969 -40.00615; -0.16077 1.23037 0.00984 -5.71551; -0.01831 0.00852 1.00112 -7.40105; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 iteration took 1 minutes and 11 seconds. **************************************** Nine parameter search. iteration 4 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.714, old_max_log_p =-3.820 (thresh=-3.8) 1.13217 0.06747 0.01865 -28.50557; -0.08126 1.16842 0.04142 -17.91371; -0.01521 -0.03136 0.98076 -4.48841; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 6 seconds. **************************************** Nine parameter search. iteration 5 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.710, old_max_log_p =-3.714 (thresh=-3.7) 1.11094 0.06620 0.01830 -25.59355; -0.08126 1.16842 0.04142 -17.91371; -0.01521 -0.03136 0.98076 -4.48841; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 6 seconds. **************************************** Nine parameter search. iteration 6 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.710, old_max_log_p =-3.710 (thresh=-3.7) 1.11094 0.06620 0.01830 -25.59355; -0.08126 1.16842 0.04142 -17.91371; -0.01521 -0.03136 0.98076 -4.48841; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 iteration took 1 minutes and 6 seconds. **************************************** Nine parameter search. iteration 7 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.694, old_max_log_p =-3.710 (thresh=-3.7) 1.11103 0.06687 0.00222 -24.25764; -0.08129 1.16675 0.04942 -18.65030; 0.00360 -0.03988 0.98286 -6.11667; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 59 seconds. **************************************** Nine parameter search. iteration 8 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.688, old_max_log_p =-3.694 (thresh=-3.7) 1.11162 0.07617 0.01069 -26.00131; -0.09048 1.16753 0.04940 -18.03133; -0.00548 -0.04052 0.98511 -5.13851; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 58 seconds. **************************************** Nine parameter search. iteration 9 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.686, old_max_log_p =-3.688 (thresh=-3.7) 1.11214 0.08582 0.01111 -27.76877; -0.09969 1.16824 0.04936 -16.93608; -0.00547 -0.04047 0.98396 -5.00895; 0.00000 0.00000 0.00000 1.00000; min search scale 0.025000 reached *********************************************** Computing MAP estimate using 2841 samples... *********************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-05 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.11214 0.08582 0.01111 -27.76877; -0.09969 1.16824 0.04936 -16.93608; -0.00547 -0.04047 0.98396 -5.00895; 0.00000 0.00000 0.00000 1.00000; nsamples 2841 Quasinewton: input matrix 1.11214 0.08582 0.01111 -27.76877; -0.09969 1.16824 0.04936 -16.93608; -0.00547 -0.04047 0.98396 -5.00895; 0.00000 0.00000 0.00000 1.00000; IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 3 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 012: -log(p) = -0.0 tol 0.000010 Resulting transform: 1.11214 0.08582 0.01111 -27.76877; -0.09969 1.16824 0.04936 -16.93608; -0.00547 -0.04047 0.98396 -5.00895; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -3.686 (old=-4.157) transform before final EM align: 1.11214 0.08582 0.01111 -27.76877; -0.09969 1.16824 0.04936 -16.93608; -0.00547 -0.04047 0.98396 -5.00895; 0.00000 0.00000 0.00000 1.00000; ************************************************** EM alignment process ... Computing final MAP estimate using 315638 samples. ************************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-07 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.11214 0.08582 0.01111 -27.76877; -0.09969 1.16824 0.04936 -16.93608; -0.00547 -0.04047 0.98396 -5.00895; 0.00000 0.00000 0.00000 1.00000; nsamples 315638 Quasinewton: input matrix 1.11214 0.08582 0.01111 -27.76877; -0.09969 1.16824 0.04936 -16.93608; -0.00547 -0.04047 0.98396 -5.00895; 0.00000 0.00000 0.00000 1.00000; IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 6 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 014: -log(p) = 4.2 tol 0.000000 final transform: 1.11214 0.08582 0.01111 -27.76877; -0.09969 1.16824 0.04936 -16.93608; -0.00547 -0.04047 0.98396 -5.00895; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach.lta... #VMPC# mri_em_register VmPeak 787388 FSRUNTIME@ mri_em_register 0.1962 hours 1 threads registration took 11 minutes and 46 seconds. @#@FSTIME 2022:02:17:06:26:05 mri_em_register N 7 e 706.39 S 0.78 U 704.47 P 99% M 616116 F 0 R 114362 W 0 c 10231 w 41 I 24 O 32 L 1.01 1.02 1.02 @#@FSLOADPOST 2022:02:17:06:37:52 mri_em_register N 7 1.07 1.05 1.02 #-------------------------------------- #@# CA Normalize Thu Feb 17 06:37:52 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_ca_normalize -c ctrl_pts.mgz -mask brainmask.mgz nu.mgz /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_2020-01-02.gca transforms/talairach.lta norm.mgz writing control point volume to ctrl_pts.mgz using MR volume brainmask.mgz to mask input volume... reading 1 input volume reading atlas from '/autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_2020-01-02.gca'... reading transform from 'transforms/talairach.lta'... reading input volume from nu.mgz... resetting wm mean[0]: 98 --> 107 resetting gm mean[0]: 61 --> 61 input volume #1 is the most T1-like using real data threshold=28.1 skull bounding box = (63, 67, 59) --> (190, 233, 186) finding center of left hemi white matter using (105, 122, 123) as brain centroid of Right_Cerebral_White_Matter... mean wm in atlas = 107, using box (89,102,107) --> (120, 142,138) to find MRI wm before smoothing, mri peak at 103 robust fit to distribution - 102 +- 9.4 after smoothing, mri peak at 103, scaling input intensities by 1.039 scaling channel 0 by 1.03883 using 246437 sample points... INFO: compute sample coordinates transform 1.11214 0.08582 0.01111 -27.76877; -0.09969 1.16824 0.04936 -16.93608; -0.00547 -0.04047 0.98396 -5.00895; 0.00000 0.00000 0.00000 1.00000; INFO: transform used finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (126, 66, 38) --> (188, 168, 208) Left_Cerebral_White_Matter: limiting intensities to 96.0 --> 132.0 0 of 6984 (0.0%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (69, 65, 37) --> (131, 164, 208) Right_Cerebral_White_Matter: limiting intensities to 96.0 --> 132.0 1 of 7031 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (127, 137, 61) --> (169, 179, 116) Left_Cerebellum_White_Matter: limiting intensities to 90.0 --> 132.0 0 of 15 (0.0%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (85, 136, 59) --> (127, 174, 118) Right_Cerebellum_White_Matter: limiting intensities to 104.0 --> 132.0 7 of 25 (28.0%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (110, 131, 98) --> (142, 191, 128) Brain_Stem: limiting intensities to 104.0 --> 132.0 2 of 64 (3.1%) samples deleted using 14119 total control points for intensity normalization... bias field = 0.968 +- 0.052 44 of 14109 control points discarded finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (126, 66, 38) --> (188, 168, 208) Left_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 2 of 7216 (0.0%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (69, 65, 37) --> (131, 164, 208) Right_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 3 of 7464 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (127, 137, 61) --> (169, 179, 116) Left_Cerebellum_White_Matter: limiting intensities to 106.0 --> 132.0 39 of 59 (66.1%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (85, 136, 59) --> (127, 174, 118) Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 7 of 79 (8.9%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (110, 131, 98) --> (142, 191, 128) Brain_Stem: limiting intensities to 89.0 --> 132.0 47 of 178 (26.4%) samples deleted using 14996 total control points for intensity normalization... bias field = 1.071 +- 0.058 53 of 14809 control points discarded finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (126, 66, 38) --> (188, 168, 208) Left_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 1 of 7341 (0.0%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (69, 65, 37) --> (131, 164, 208) Right_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 4 of 7516 (0.1%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (127, 137, 61) --> (169, 179, 116) Left_Cerebellum_White_Matter: limiting intensities to 102.0 --> 132.0 97 of 118 (82.2%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (85, 136, 59) --> (127, 174, 118) Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 25 of 91 (27.5%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (110, 131, 98) --> (142, 191, 128) Brain_Stem: limiting intensities to 88.0 --> 132.0 59 of 229 (25.8%) samples deleted using 15295 total control points for intensity normalization... bias field = 1.065 +- 0.053 40 of 14901 control points discarded writing normalized volume to norm.mgz... writing control points to ctrl_pts.mgz freeing GCA...done. normalization took 1 minutes and 0 seconds. @#@FSTIME 2022:02:17:06:37:52 mri_ca_normalize N 8 e 59.84 S 0.25 U 59.35 P 99% M 839152 F 11 R 240796 W 0 c 758 w 119 I 1504 O 4696 L 1.07 1.05 1.02 @#@FSLOADPOST 2022:02:17:06:38:52 mri_ca_normalize N 8 1.12 1.07 1.02 #-------------------------------------- #@# CA Reg Thu Feb 17 06:38:52 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_ca_register -nobigventricles -T transforms/talairach.lta -align-after -mask brainmask.mgz norm.mgz /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_2020-01-02.gca transforms/talairach.m3z not handling expanded ventricles... using previously computed transform transforms/talairach.lta renormalizing sequences with structure alignment, equivalent to: -renormalize -regularize_mean 0.500 -regularize 0.500 using MR volume brainmask.mgz to mask input volume... == Number of threads available to mri_ca_register for OpenMP = 1 == reading 1 input volumes... logging results to talairach.log reading input volume 'norm.mgz'... reading GCA '/autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_2020-01-02.gca'... label assignment complete, 0 changed (0.00%) freeing gibbs priors...done. average std[0] = 5.0 Starting GCAMregister() label assignment complete, 0 changed (0.00%) npasses = 1, nlevels = 6 #pass# 1 of 1 ************************ enabling zero nodes setting smoothness cost coefficient to 0.156 #GCAMreg# pass 0 level1 5 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.16 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.943536 #FOTS# QuadFit found better minimum quadopt=(dt=282.923,rms=0.870968) vs oldopt=(dt=369.92,rms=0.877293) #GCMRL# 0 dt 282.923351 rms 0.871 7.691% neg 0 invalid 762 tFOTS 11.7200 tGradient 5.7170 tsec 18.3080 #FOTS# QuadFit found better minimum quadopt=(dt=208,rms=0.853057) vs oldopt=(dt=92.48,rms=0.859127) #GCMRL# 1 dt 208.000000 rms 0.853 2.056% neg 0 invalid 762 tFOTS 11.6920 tGradient 5.8170 tsec 18.3520 #FOTS# QuadFit found better minimum quadopt=(dt=238.44,rms=0.843322) vs oldopt=(dt=369.92,rms=0.845387) #GCMRL# 2 dt 238.440433 rms 0.843 1.141% neg 0 invalid 762 tFOTS 11.7560 tGradient 5.8090 tsec 18.4100 #FOTS# QuadFit found better minimum quadopt=(dt=150.557,rms=0.837455) vs oldopt=(dt=92.48,rms=0.83867) #GCMRL# 3 dt 150.557377 rms 0.837 0.696% neg 0 invalid 762 tFOTS 11.7420 tGradient 5.7990 tsec 18.3820 #FOTS# QuadFit found better minimum quadopt=(dt=443.904,rms=0.831845) vs oldopt=(dt=369.92,rms=0.831849) #GCMRL# 4 dt 443.904000 rms 0.832 0.670% neg 0 invalid 762 tFOTS 11.7470 tGradient 5.8680 tsec 18.4560 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.826924) vs oldopt=(dt=92.48,rms=0.82735) #GCMRL# 5 dt 129.472000 rms 0.827 0.592% neg 0 invalid 762 tFOTS 11.6920 tGradient 5.8010 tsec 18.3290 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.8235) vs oldopt=(dt=369.92,rms=0.823767) #GCMRL# 6 dt 517.888000 rms 0.824 0.414% neg 0 invalid 762 tFOTS 11.7340 tGradient 5.7440 tsec 18.3160 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.820068) vs oldopt=(dt=92.48,rms=0.820328) #GCMRL# 7 dt 129.472000 rms 0.820 0.417% neg 0 invalid 762 tFOTS 12.3790 tGradient 5.6400 tsec 18.8630 #GCMRL# 8 dt 369.920000 rms 0.818 0.266% neg 0 invalid 762 tFOTS 11.7090 tGradient 5.9440 tsec 18.4920 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.816269) vs oldopt=(dt=92.48,rms=0.816468) #GCMRL# 9 dt 129.472000 rms 0.816 0.000% neg 0 invalid 762 tFOTS 11.7340 tGradient 5.5190 tsec 18.1140 #GCMRL# 10 dt 129.472000 rms 0.815 0.105% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.8150 tsec 6.6640 #GCMRL# 11 dt 129.472000 rms 0.814 0.176% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.7280 tsec 6.5690 #GCMRL# 12 dt 129.472000 rms 0.812 0.245% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.7850 tsec 6.6230 #GCMRL# 13 dt 129.472000 rms 0.810 0.297% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.9520 tsec 6.8000 #GCMRL# 14 dt 129.472000 rms 0.807 0.315% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.7870 tsec 6.6370 #GCMRL# 15 dt 129.472000 rms 0.805 0.302% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0580 tsec 6.9000 #GCMRL# 16 dt 129.472000 rms 0.802 0.286% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.7760 tsec 6.6170 #GCMRL# 17 dt 129.472000 rms 0.800 0.268% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.1390 tsec 6.9830 #GCMRL# 18 dt 129.472000 rms 0.798 0.230% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.9810 tsec 6.8280 #GCMRL# 19 dt 129.472000 rms 0.797 0.197% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6320 tsec 6.4860 #GCMRL# 20 dt 129.472000 rms 0.795 0.193% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.8410 tsec 6.6800 #GCMRL# 21 dt 129.472000 rms 0.794 0.173% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0200 tsec 6.8590 #GCMRL# 22 dt 129.472000 rms 0.792 0.176% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.2430 tsec 7.1610 #GCMRL# 23 dt 129.472000 rms 0.791 0.169% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.1690 tsec 7.0670 #GCMRL# 24 dt 129.472000 rms 0.790 0.160% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.2520 tsec 7.1740 #GCMRL# 25 dt 129.472000 rms 0.789 0.152% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.9030 tsec 6.7450 #GCMRL# 26 dt 129.472000 rms 0.787 0.141% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.7250 tsec 6.5720 #GCMRL# 27 dt 129.472000 rms 0.786 0.130% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.9510 tsec 6.7940 #GCMRL# 28 dt 129.472000 rms 0.785 0.124% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.9670 tsec 6.8030 #GCMRL# 29 dt 129.472000 rms 0.785 0.122% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.8620 tsec 6.7230 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.783933) vs oldopt=(dt=369.92,rms=0.784011) #GCMRL# 30 dt 517.888000 rms 0.784 0.000% neg 0 invalid 762 tFOTS 12.4120 tGradient 5.7150 tsec 18.9820 #GCAMreg# pass 0 level1 5 level2 1 tsec 349.952 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.16 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.784464 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.7834) vs oldopt=(dt=92.48,rms=0.783406) #GCMRL# 32 dt 110.976000 rms 0.783 0.136% neg 0 invalid 762 tFOTS 11.7610 tGradient 5.9530 tsec 18.5530 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.78305) vs oldopt=(dt=369.92,rms=0.783076) #GCMRL# 33 dt 295.936000 rms 0.783 0.000% neg 0 invalid 762 tFOTS 11.6930 tGradient 5.8660 tsec 18.4130 #GCMRL# 34 dt 295.936000 rms 0.782 0.127% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0020 tsec 6.8380 #GCMRL# 35 dt 295.936000 rms 0.782 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.8570 tsec 6.6940 #GCMRL# 36 dt 295.936000 rms 0.782 -0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.8220 tsec 7.4500 setting smoothness cost coefficient to 0.615 #GCAMreg# pass 0 level1 4 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.62 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.797756 #FOTS# QuadFit found better minimum quadopt=(dt=68.8421,rms=0.794704) vs oldopt=(dt=103.68,rms=0.795398) #GCMRL# 38 dt 68.842105 rms 0.795 0.383% neg 0 invalid 762 tFOTS 11.7040 tGradient 4.1640 tsec 16.7110 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.79048) vs oldopt=(dt=103.68,rms=0.791141) #GCMRL# 39 dt 145.152000 rms 0.790 0.531% neg 0 invalid 762 tFOTS 11.0210 tGradient 4.2830 tsec 16.1430 #FOTS# QuadFit found better minimum quadopt=(dt=580.608,rms=0.775991) vs oldopt=(dt=414.72,rms=0.777899) #GCMRL# 40 dt 580.608000 rms 0.776 1.833% neg 0 invalid 762 tFOTS 11.7500 tGradient 4.1090 tsec 16.7090 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.773447) vs oldopt=(dt=25.92,rms=0.773892) #GCMRL# 41 dt 36.288000 rms 0.773 0.328% neg 0 invalid 762 tFOTS 11.7270 tGradient 4.0210 tsec 16.5950 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.773182) vs oldopt=(dt=25.92,rms=0.773213) #GCMRL# 42 dt 36.288000 rms 0.773 0.000% neg 0 invalid 762 tFOTS 12.3930 tGradient 3.9710 tsec 17.2150 #GCMRL# 43 dt 36.288000 rms 0.773 0.012% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9700 tsec 4.8090 #GCMRL# 44 dt 36.288000 rms 0.773 0.008% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9670 tsec 4.8050 #GCMRL# 45 dt 36.288000 rms 0.773 0.015% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9620 tsec 4.7990 #GCMRL# 46 dt 36.288000 rms 0.772 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8940 tsec 4.7370 #GCMRL# 47 dt 36.288000 rms 0.772 0.124% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9310 tsec 4.7730 #GCMRL# 48 dt 36.288000 rms 0.770 0.173% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.1140 tsec 4.9530 #GCMRL# 49 dt 36.288000 rms 0.769 0.203% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9830 tsec 4.8240 #GCMRL# 50 dt 36.288000 rms 0.767 0.207% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.1870 tsec 5.0240 #GCMRL# 51 dt 36.288000 rms 0.766 0.183% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.2400 tsec 5.0830 #GCMRL# 52 dt 36.288000 rms 0.764 0.150% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9720 tsec 4.8080 #GCMRL# 53 dt 36.288000 rms 0.764 0.116% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9250 tsec 4.7660 #GCMRL# 54 dt 36.288000 rms 0.763 0.094% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.0190 tsec 4.8870 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.762287) vs oldopt=(dt=103.68,rms=0.7624) #GCMRL# 55 dt 145.152000 rms 0.762 0.000% neg 0 invalid 762 tFOTS 11.6970 tGradient 3.9790 tsec 16.5300 #GCAMreg# pass 0 level1 4 level2 1 tsec 167.444 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.62 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.762781 #GCMRL# 57 dt 0.000000 rms 0.762 0.065% neg 0 invalid 762 tFOTS 11.8050 tGradient 3.9910 tsec 16.6380 setting smoothness cost coefficient to 2.353 #GCAMreg# pass 0 level1 3 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.35 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.796557 #GCMRL# 59 dt 0.000000 rms 0.796 0.060% neg 0 invalid 762 tFOTS 10.3630 tGradient 3.4560 tsec 14.6600 #GCAMreg# pass 0 level1 3 level2 1 tsec 33.763 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.35 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.796557 #GCMRL# 61 dt 0.000000 rms 0.796 0.060% neg 0 invalid 762 tFOTS 10.3260 tGradient 3.4440 tsec 14.6060 setting smoothness cost coefficient to 8.000 #GCAMreg# pass 0 level1 2 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=8.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.897595 #FOTS# QuadFit found better minimum quadopt=(dt=2.74973,rms=0.869737) vs oldopt=(dt=2.88,rms=0.869807) #GCMRL# 63 dt 2.749734 rms 0.870 3.104% neg 0 invalid 762 tFOTS 11.0310 tGradient 3.1430 tsec 15.0090 #FOTS# QuadFit found better minimum quadopt=(dt=2.28519,rms=0.864465) vs oldopt=(dt=2.88,rms=0.864828) #GCMRL# 64 dt 2.285192 rms 0.864 0.606% neg 0 invalid 762 tFOTS 11.0530 tGradient 3.2470 tsec 15.1430 #FOTS# QuadFit found better minimum quadopt=(dt=2.29464,rms=0.862111) vs oldopt=(dt=2.88,rms=0.862272) #GCMRL# 65 dt 2.294643 rms 0.862 0.272% neg 0 invalid 762 tFOTS 11.0220 tGradient 3.0860 tsec 14.9480 #FOTS# QuadFit found better minimum quadopt=(dt=1.008,rms=0.861883) vs oldopt=(dt=0.72,rms=0.861906) #GCMRL# 66 dt 1.008000 rms 0.862 0.000% neg 0 invalid 762 tFOTS 11.0390 tGradient 3.2270 tsec 15.1290 #GCMRL# 67 dt 1.008000 rms 0.862 0.017% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2300 tsec 4.0690 #GCAMreg# pass 0 level1 2 level2 1 tsec 72.833 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=8.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.862199 #GCMRL# 69 dt 0.000000 rms 0.862 0.053% neg 0 invalid 762 tFOTS 10.4570 tGradient 3.2210 tsec 14.5200 setting smoothness cost coefficient to 20.000 #GCAMreg# pass 0 level1 1 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=20.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.920791 #GCMRL# 71 dt 0.080000 rms 0.920 0.055% neg 0 invalid 762 tFOTS 11.0520 tGradient 3.0520 tsec 14.9590 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.920159) vs oldopt=(dt=0.08,rms=0.920181) #GCMRL# 72 dt 0.112000 rms 0.920 0.000% neg 0 invalid 762 tFOTS 11.0440 tGradient 3.0590 tsec 14.9590 #GCMRL# 73 dt 0.112000 rms 0.920 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1050 tsec 3.9420 #GCMRL# 74 dt 0.112000 rms 0.919 0.057% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0860 tsec 3.9260 #GCMRL# 75 dt 0.112000 rms 0.919 0.090% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1230 tsec 3.9660 #GCMRL# 76 dt 0.112000 rms 0.917 0.135% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1170 tsec 3.9580 #GCMRL# 77 dt 0.112000 rms 0.916 0.181% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0660 tsec 3.9090 #GCMRL# 78 dt 0.112000 rms 0.914 0.201% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9620 tsec 3.8020 #GCMRL# 79 dt 0.112000 rms 0.912 0.192% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0100 tsec 3.8550 #GCMRL# 80 dt 0.112000 rms 0.911 0.160% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9200 tsec 3.7660 #GCMRL# 81 dt 0.112000 rms 0.909 0.122% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9810 tsec 3.8220 #GCMRL# 82 dt 0.112000 rms 0.909 0.093% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9400 tsec 3.8010 #FOTS# QuadFit found better minimum quadopt=(dt=1.792,rms=0.905422) vs oldopt=(dt=1.28,rms=0.905631) #GCMRL# 83 dt 1.792000 rms 0.905 0.352% neg 0 invalid 762 tFOTS 11.0630 tGradient 2.8710 tsec 14.7740 #GCMRL# 84 dt 0.320000 rms 0.904 0.000% neg 0 invalid 762 tFOTS 11.1060 tGradient 2.9510 tsec 14.9170 #GCMRL# 85 dt 0.320000 rms 0.902 0.186% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9920 tsec 3.8310 #GCMRL# 86 dt 0.320000 rms 0.902 0.064% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8620 tsec 3.7040 #GCMRL# 87 dt 0.320000 rms 0.902 -0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9220 tsec 4.5490 #GCAMreg# pass 0 level1 1 level2 1 tsec 128.99 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=20.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.902173 #FOTS# QuadFit found better minimum quadopt=(dt=0.048,rms=0.90171) vs oldopt=(dt=0.08,rms=0.901715) #GCMRL# 89 dt 0.048000 rms 0.902 0.051% neg 0 invalid 762 tFOTS 11.0710 tGradient 2.8880 tsec 14.8100 #FOTS# QuadFit found better minimum quadopt=(dt=0.028,rms=0.901701) vs oldopt=(dt=0.02,rms=0.901702) #GCMRL# 90 dt 0.028000 rms 0.902 0.000% neg 0 invalid 762 tFOTS 11.1020 tGradient 2.9290 tsec 14.8950 #GCMRL# 91 dt 0.028000 rms 0.902 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9460 tsec 3.7870 resetting metric properties... setting smoothness cost coefficient to 40.000 #GCAMreg# pass 0 level1 0 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=40.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.862731 #FOTS# QuadFit found better minimum quadopt=(dt=0.260456,rms=0.855199) vs oldopt=(dt=0.32,rms=0.855394) #GCMRL# 93 dt 0.260456 rms 0.855 0.873% neg 0 invalid 762 tFOTS 11.0710 tGradient 2.4850 tsec 14.3920 #FOTS# QuadFit found better minimum quadopt=(dt=0.024,rms=0.854945) vs oldopt=(dt=0.02,rms=0.854949) #GCMRL# 94 dt 0.024000 rms 0.855 0.000% neg 0 invalid 762 tFOTS 11.0500 tGradient 2.3760 tsec 14.2790 #GCAMreg# pass 0 level1 0 level2 1 tsec 36.352 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=40.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.855417 #FOTS# QuadFit found better minimum quadopt=(dt=0.007,rms=0.854911) vs oldopt=(dt=0.005,rms=0.854917) #GCMRL# 96 dt 0.007000 rms 0.855 0.059% neg 0 invalid 762 tFOTS 11.0710 tGradient 2.3850 tsec 14.2970 #FOTS# QuadFit found better minimum quadopt=(dt=0.004,rms=0.854906) vs oldopt=(dt=0.005,rms=0.854907) #GCMRL# 97 dt 0.004000 rms 0.855 0.000% neg 0 invalid 762 tFOTS 11.0430 tGradient 2.4310 tsec 14.3290 GCAMregister done in 17.4531 min Starting GCAmapRenormalizeWithAlignment() without scales renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.10253 (16) mri peak = 0.59177 ( 6) Left_Lateral_Ventricle (4): linear fit = 0.31 x + 0.0 (602 voxels, overlap=0.004) Left_Lateral_Ventricle (4): linear fit = 0.40 x + 0.0 (602 voxels, peak = 5), gca=6.4 gca peak = 0.17690 (16) mri peak = 0.65583 ( 6) Right_Lateral_Ventricle (43): linear fit = 0.31 x + 0.0 (925 voxels, overlap=0.004) Right_Lateral_Ventricle (43): linear fit = 0.40 x + 0.0 (925 voxels, peak = 5), gca=6.4 gca peak = 0.28275 (96) mri peak = 0.10710 (106) Right_Pallidum (52): linear fit = 1.10 x + 0.0 (824 voxels, overlap=0.017) Right_Pallidum (52): linear fit = 1.10 x + 0.0 (824 voxels, peak = 105), gca=105.1 gca peak = 0.18948 (93) mri peak = 0.09104 (109) Left_Pallidum (13): linear fit = 1.13 x + 0.0 (699 voxels, overlap=0.215) Left_Pallidum (13): linear fit = 1.13 x + 0.0 (699 voxels, peak = 106), gca=105.6 gca peak = 0.20755 (55) mri peak = 0.09910 (56) Right_Hippocampus (53): linear fit = 0.92 x + 0.0 (788 voxels, overlap=0.986) Right_Hippocampus (53): linear fit = 0.92 x + 0.0 (788 voxels, peak = 50), gca=50.3 gca peak = 0.31831 (58) mri peak = 0.12215 (52) Left_Hippocampus (17): linear fit = 0.89 x + 0.0 (684 voxels, overlap=0.994) Left_Hippocampus (17): linear fit = 0.89 x + 0.0 (684 voxels, peak = 52), gca=51.9 gca peak = 0.11957 (102) mri peak = 0.12116 (100) Right_Cerebral_White_Matter (41): linear fit = 1.01 x + 0.0 (55663 voxels, overlap=0.827) Right_Cerebral_White_Matter (41): linear fit = 1.01 x + 0.0 (55663 voxels, peak = 104), gca=103.5 gca peak = 0.11429 (102) mri peak = 0.11822 (100) Left_Cerebral_White_Matter (2): linear fit = 1.01 x + 0.0 (58276 voxels, overlap=0.838) Left_Cerebral_White_Matter (2): linear fit = 1.01 x + 0.0 (58276 voxels, peak = 104), gca=103.5 gca peak = 0.14521 (59) mri peak = 0.02884 (66) Left_Cerebral_Cortex (3): linear fit = 1.14 x + 0.0 (15484 voxels, overlap=0.050) Left_Cerebral_Cortex (3): linear fit = 1.14 x + 0.0 (15484 voxels, peak = 68), gca=67.6 gca peak = 0.14336 (58) mri peak = 0.02785 (56) Right_Cerebral_Cortex (42): linear fit = 1.23 x + 0.0 (15375 voxels, overlap=0.139) Right_Cerebral_Cortex (42): linear fit = 1.23 x + 0.0 (15375 voxels, peak = 71), gca=71.1 gca peak = 0.13305 (70) mri peak = 0.12121 (78) Right_Caudate (50): linear fit = 1.10 x + 0.0 (566 voxels, overlap=0.555) Right_Caudate (50): linear fit = 1.10 x + 0.0 (566 voxels, peak = 77), gca=76.7 gca peak = 0.15761 (71) mri peak = 0.08605 (77) Left_Caudate (11): linear fit = 1.07 x + 0.0 (965 voxels, overlap=0.738) Left_Caudate (11): linear fit = 1.07 x + 0.0 (965 voxels, peak = 76), gca=75.6 gca peak = 0.13537 (57) mri peak = 0.02554 (85) Left_Cerebellum_Cortex (8): linear fit = 1.58 x + 0.0 (15974 voxels, overlap=0.000) Left_Cerebellum_Cortex (8): linear fit = 1.58 x + 0.0 (15974 voxels, peak = 90), gca=89.8 gca peak = 0.13487 (56) mri peak = 0.02583 (85) Right_Cerebellum_Cortex (47): linear fit = 1.49 x + 0.0 (19142 voxels, overlap=0.000) Right_Cerebellum_Cortex (47): linear fit = 1.49 x + 0.0 (19142 voxels, peak = 83), gca=83.2 gca peak = 0.19040 (84) mri peak = 0.04310 (88) Left_Cerebellum_White_Matter (7): linear fit = 1.05 x + 0.0 (7589 voxels, overlap=0.720) Left_Cerebellum_White_Matter (7): linear fit = 1.05 x + 0.0 (7589 voxels, peak = 89), gca=88.6 gca peak = 0.18871 (83) mri peak = 0.06390 (85) Right_Cerebellum_White_Matter (46): linear fit = 1.05 x + 0.0 (6898 voxels, overlap=0.873) Right_Cerebellum_White_Matter (46): linear fit = 1.05 x + 0.0 (6898 voxels, peak = 88), gca=87.6 gca peak = 0.24248 (57) mri peak = 0.09888 (61) Left_Amygdala (18): linear fit = 1.04 x + 0.0 (446 voxels, overlap=0.991) Left_Amygdala (18): linear fit = 1.04 x + 0.0 (446 voxels, peak = 60), gca=59.6 gca peak = 0.35833 (56) mri peak = 0.10742 (56) Right_Amygdala (54): linear fit = 0.99 x + 0.0 (519 voxels, overlap=0.978) Right_Amygdala (54): linear fit = 0.99 x + 0.0 (519 voxels, peak = 55), gca=55.2 gca peak = 0.12897 (85) mri peak = 0.05931 (92) Left_Thalamus (10): linear fit = 1.07 x + 0.0 (5090 voxels, overlap=0.728) Left_Thalamus (10): linear fit = 1.07 x + 0.0 (5090 voxels, peak = 91), gca=90.5 gca peak = 0.13127 (83) mri peak = 0.06996 (87) Right_Thalamus (49): linear fit = 1.08 x + 0.0 (4085 voxels, overlap=0.818) Right_Thalamus (49): linear fit = 1.08 x + 0.0 (4085 voxels, peak = 89), gca=89.2 gca peak = 0.12974 (78) mri peak = 0.07859 (92) Left_Putamen (12): linear fit = 1.13 x + 0.0 (2219 voxels, overlap=0.482) Left_Putamen (12): linear fit = 1.13 x + 0.0 (2219 voxels, peak = 89), gca=88.5 gca peak = 0.17796 (79) mri peak = 0.06371 (81) Right_Putamen (51): linear fit = 1.10 x + 0.0 (2276 voxels, overlap=0.862) Right_Putamen (51): linear fit = 1.10 x + 0.0 (2276 voxels, peak = 87), gca=86.5 gca peak = 0.10999 (80) mri peak = 0.06635 (94) Brain_Stem (16): linear fit = 1.16 x + 0.0 (10569 voxels, overlap=0.417) Brain_Stem (16): linear fit = 1.16 x + 0.0 (10569 voxels, peak = 93), gca=93.2 gca peak = 0.13215 (88) mri peak = 0.08617 (102) Right_VentralDC (60): linear fit = 1.18 x + 0.0 (973 voxels, overlap=0.016) Right_VentralDC (60): linear fit = 1.18 x + 0.0 (973 voxels, peak = 104), gca=104.3 gca peak = 0.11941 (89) mri peak = 0.08824 (104) Left_VentralDC (28): linear fit = 1.20 x + 0.0 (1195 voxels, overlap=0.015) Left_VentralDC (28): linear fit = 1.20 x + 0.0 (1195 voxels, peak = 106), gca=106.4 gca peak = 0.20775 (25) mri peak = 0.07337 ( 6) gca peak = 0.13297 (21) mri peak = 0.08337 (55) Fourth_Ventricle (15): linear fit = 2.56 x + 0.0 (244 voxels, overlap=0.041) Fourth_Ventricle (15): linear fit = 2.56 x + 0.0 (244 voxels, peak = 54), gca=53.7 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.19087 (28) gca peak Left_Cerebellum_Cortex = 0.13537 (57) gca peak Third_Ventricle = 0.20775 (25) gca peak Fourth_Ventricle = 0.13297 (21) gca peak CSF = 0.16821 (33) gca peak Left_Accumbens_area = 0.32850 (63) gca peak Left_undetermined = 0.98480 (28) gca peak Left_vessel = 0.40887 (53) gca peak Left_choroid_plexus = 0.10898 (46) gca peak Right_Inf_Lat_Vent = 0.17798 (26) gca peak Right_Accumbens_area = 0.30137 (64) gca peak Right_vessel = 0.47828 (52) gca peak Right_choroid_plexus = 0.11612 (45) gca peak Fifth_Ventricle = 0.59466 (35) gca peak WM_hypointensities = 0.10053 (78) gca peak non_WM_hypointensities = 0.07253 (60) gca peak Optic_Chiasm = 0.25330 (73) not using caudate to estimate GM means setting label Left_Cerebellum_Cortex based on Right_Cerebellum_Cortex = 1.49 x + 0: 83 estimating mean gm scale to be 1.03 x + 0.0 estimating mean wm scale to be 1.01 x + 0.0 estimating mean csf scale to be 0.40 x + 0.0 Left_Pallidum too bright - rescaling by 0.951 (from 1.135) to 100.4 (was 105.6) Right_Pallidum too bright - rescaling by 0.955 (from 1.095) to 100.4 (was 105.1) saving intensity scales to talairach.label_intensities.txt GCAmapRenormalizeWithAlignment() took 3.11735 min noneg pre Starting GCAMregister() label assignment complete, 0 changed (0.00%) npasses = 1, nlevels = 6 #pass# 1 of 1 ************************ enabling zero nodes setting smoothness cost coefficient to 0.008 #GCAMreg# pass 0 level1 5 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.01 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.887508 #FOTS# QuadFit found better minimum quadopt=(dt=242.068,rms=0.855139) vs oldopt=(dt=92.48,rms=0.865221) #GCMRL# 99 dt 242.068027 rms 0.855 3.647% neg 0 invalid 762 tFOTS 11.7330 tGradient 5.4120 tsec 17.9830 #FOTS# QuadFit found better minimum quadopt=(dt=261.552,rms=0.835753) vs oldopt=(dt=369.92,rms=0.838625) #GCMRL# 100 dt 261.552377 rms 0.836 2.267% neg 0 invalid 762 tFOTS 11.6970 tGradient 5.4590 tsec 17.9980 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.831122) vs oldopt=(dt=92.48,rms=0.831385) #GCMRL# 101 dt 129.472000 rms 0.831 0.554% neg 0 invalid 762 tFOTS 11.7220 tGradient 5.5180 tsec 18.0800 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.822793) vs oldopt=(dt=369.92,rms=0.823607) #GCMRL# 102 dt 517.888000 rms 0.823 1.002% neg 0 invalid 762 tFOTS 11.0100 tGradient 5.5080 tsec 17.3560 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.818397) vs oldopt=(dt=92.48,rms=0.8186) #GCMRL# 103 dt 110.976000 rms 0.818 0.534% neg 0 invalid 762 tFOTS 11.7520 tGradient 5.4570 tsec 18.0510 #FOTS# QuadFit found better minimum quadopt=(dt=1183.74,rms=0.806953) vs oldopt=(dt=1479.68,rms=0.807633) #GCMRL# 104 dt 1183.744000 rms 0.807 1.398% neg 0 invalid 762 tFOTS 11.7110 tGradient 5.3590 tsec 17.9070 #FOTS# QuadFit found better minimum quadopt=(dt=94.4158,rms=0.801682) vs oldopt=(dt=92.48,rms=0.801696) #GCMRL# 105 dt 94.415842 rms 0.802 0.653% neg 0 invalid 762 tFOTS 11.6980 tGradient 5.4840 tsec 18.0210 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.799529) vs oldopt=(dt=369.92,rms=0.799745) #GCMRL# 106 dt 517.888000 rms 0.800 0.268% neg 0 invalid 762 tFOTS 11.7100 tGradient 5.6670 tsec 18.2180 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.795947) vs oldopt=(dt=369.92,rms=0.796542) #GCMRL# 107 dt 295.936000 rms 0.796 0.448% neg 0 invalid 762 tFOTS 11.0040 tGradient 5.6220 tsec 17.4630 #GCMRL# 108 dt 92.480000 rms 0.795 0.169% neg 0 invalid 762 tFOTS 11.6820 tGradient 5.5920 tsec 18.1150 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.792508) vs oldopt=(dt=369.92,rms=0.792914) #GCMRL# 109 dt 517.888000 rms 0.793 0.263% neg 0 invalid 762 tFOTS 11.6780 tGradient 5.6610 tsec 18.1750 #GCMRL# 110 dt 369.920000 rms 0.789 0.385% neg 0 invalid 762 tFOTS 11.7510 tGradient 5.5660 tsec 18.1560 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.788613) vs oldopt=(dt=92.48,rms=0.788655) #GCMRL# 111 dt 129.472000 rms 0.789 0.107% neg 0 invalid 762 tFOTS 11.7000 tGradient 5.5660 tsec 18.1130 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.786377) vs oldopt=(dt=369.92,rms=0.786812) #GCMRL# 112 dt 517.888000 rms 0.786 0.284% neg 0 invalid 762 tFOTS 11.7040 tGradient 5.6010 tsec 18.1500 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.785409) vs oldopt=(dt=92.48,rms=0.78551) #GCMRL# 113 dt 129.472000 rms 0.785 0.123% neg 0 invalid 762 tFOTS 11.7870 tGradient 5.5800 tsec 18.2050 #FOTS# QuadFit found better minimum quadopt=(dt=443.904,rms=0.783661) vs oldopt=(dt=369.92,rms=0.783745) #GCMRL# 114 dt 443.904000 rms 0.784 0.223% neg 0 invalid 762 tFOTS 11.7380 tGradient 5.5070 tsec 18.0900 #GCMRL# 115 dt 92.480000 rms 0.783 0.111% neg 0 invalid 762 tFOTS 11.7250 tGradient 5.5700 tsec 18.1350 #FOTS# QuadFit found better minimum quadopt=(dt=4734.98,rms=0.772054) vs oldopt=(dt=5918.72,rms=0.772625) #GCMRL# 116 dt 4734.976000 rms 0.772 1.372% neg 0 invalid 762 tFOTS 11.8010 tGradient 5.5030 tsec 18.1460 #FOTS# QuadFit found better minimum quadopt=(dt=131.429,rms=0.767862) vs oldopt=(dt=92.48,rms=0.768419) #GCMRL# 117 dt 131.428571 rms 0.768 0.543% neg 0 invalid 762 tFOTS 11.7590 tGradient 5.6270 tsec 18.2300 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.766948) vs oldopt=(dt=369.92,rms=0.767145) #GCMRL# 118 dt 221.952000 rms 0.767 0.119% neg 0 invalid 762 tFOTS 11.7210 tGradient 5.5270 tsec 18.0850 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.766533) vs oldopt=(dt=92.48,rms=0.766585) #GCMRL# 119 dt 129.472000 rms 0.767 0.054% neg 0 invalid 762 tFOTS 11.7140 tGradient 5.5090 tsec 18.0590 #FOTS# QuadFit found better minimum quadopt=(dt=443.904,rms=0.765763) vs oldopt=(dt=369.92,rms=0.765793) #GCMRL# 120 dt 443.904000 rms 0.766 0.100% neg 0 invalid 762 tFOTS 11.7640 tGradient 5.5580 tsec 18.1550 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.76515) vs oldopt=(dt=92.48,rms=0.765174) #GCMRL# 121 dt 110.976000 rms 0.765 0.080% neg 0 invalid 762 tFOTS 11.7810 tGradient 5.4670 tsec 18.0830 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.76451) vs oldopt=(dt=369.92,rms=0.764579) #GCMRL# 122 dt 517.888000 rms 0.765 0.084% neg 0 invalid 762 tFOTS 11.6840 tGradient 5.5260 tsec 18.0470 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.763888) vs oldopt=(dt=92.48,rms=0.763937) #GCMRL# 123 dt 129.472000 rms 0.764 0.081% neg 0 invalid 762 tFOTS 11.7670 tGradient 5.5210 tsec 18.1250 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.763565) vs oldopt=(dt=369.92,rms=0.763634) #GCMRL# 124 dt 221.952000 rms 0.764 0.000% neg 0 invalid 762 tFOTS 11.6990 tGradient 5.5330 tsec 18.0850 #GCMRL# 125 dt 221.952000 rms 0.763 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5000 tsec 6.3400 #GCMRL# 126 dt 221.952000 rms 0.762 0.122% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5090 tsec 6.3500 #GCMRL# 127 dt 221.952000 rms 0.762 0.108% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5330 tsec 6.3730 #GCMRL# 128 dt 221.952000 rms 0.760 0.166% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5110 tsec 6.3540 #GCMRL# 129 dt 221.952000 rms 0.759 0.173% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6200 tsec 6.4550 #GCMRL# 130 dt 221.952000 rms 0.757 0.212% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5860 tsec 6.4330 #GCMRL# 131 dt 221.952000 rms 0.756 0.215% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5820 tsec 6.4200 #GCMRL# 132 dt 221.952000 rms 0.754 0.196% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5480 tsec 6.3850 #GCMRL# 133 dt 221.952000 rms 0.753 0.221% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5760 tsec 6.4150 #GCMRL# 134 dt 221.952000 rms 0.751 0.181% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5910 tsec 6.4310 #GCMRL# 135 dt 221.952000 rms 0.750 0.198% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5480 tsec 6.3880 #GCMRL# 136 dt 221.952000 rms 0.748 0.196% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5720 tsec 6.4110 #GCMRL# 137 dt 221.952000 rms 0.747 0.157% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5350 tsec 6.3750 #GCMRL# 138 dt 221.952000 rms 0.746 0.150% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.4360 tsec 6.2720 #GCMRL# 139 dt 221.952000 rms 0.745 0.142% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5080 tsec 6.3450 #GCMRL# 140 dt 221.952000 rms 0.744 0.164% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5970 tsec 6.4390 #GCMRL# 141 dt 221.952000 rms 0.743 0.124% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5510 tsec 6.3870 #GCMRL# 142 dt 221.952000 rms 0.742 0.099% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5650 tsec 6.4060 #GCMRL# 143 dt 221.952000 rms 0.742 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5540 tsec 6.7320 #GCMRL# 144 dt 221.952000 rms 0.742 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5270 tsec 6.7150 #GCMRL# 145 dt 221.952000 rms 0.741 0.060% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5710 tsec 6.4100 #GCMRL# 146 dt 221.952000 rms 0.741 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5650 tsec 6.4010 #GCMRL# 147 dt 221.952000 rms 0.740 0.077% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5900 tsec 6.4260 #GCMRL# 148 dt 221.952000 rms 0.740 0.062% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5360 tsec 6.3780 #GCMRL# 149 dt 221.952000 rms 0.739 0.086% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5550 tsec 6.4050 #GCMRL# 150 dt 221.952000 rms 0.738 0.097% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.4660 tsec 6.3060 #GCMRL# 151 dt 221.952000 rms 0.738 0.073% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5340 tsec 6.3690 #GCMRL# 152 dt 221.952000 rms 0.737 0.104% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6550 tsec 6.4910 #GCMRL# 153 dt 221.952000 rms 0.736 0.100% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5170 tsec 6.3530 #GCMRL# 154 dt 221.952000 rms 0.736 0.107% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.4950 tsec 6.3330 #GCMRL# 155 dt 221.952000 rms 0.735 0.099% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5360 tsec 6.3840 #GCMRL# 156 dt 221.952000 rms 0.734 0.089% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.4680 tsec 6.3040 #GCMRL# 157 dt 221.952000 rms 0.733 0.107% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5150 tsec 6.3550 #GCMRL# 158 dt 221.952000 rms 0.733 0.100% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.4950 tsec 6.3500 #GCMRL# 159 dt 221.952000 rms 0.732 0.098% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6220 tsec 6.4620 #GCMRL# 160 dt 221.952000 rms 0.731 0.087% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6070 tsec 6.4490 #GCMRL# 161 dt 221.952000 rms 0.731 0.084% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5720 tsec 6.4080 #GCMRL# 162 dt 221.952000 rms 0.730 0.089% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6580 tsec 6.4960 #GCMRL# 163 dt 221.952000 rms 0.730 0.082% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6300 tsec 6.4710 #GCMRL# 164 dt 221.952000 rms 0.729 0.084% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5250 tsec 6.3690 #GCMRL# 165 dt 221.952000 rms 0.728 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6470 tsec 6.4910 #GCMRL# 166 dt 221.952000 rms 0.728 0.063% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5930 tsec 6.4290 #GCMRL# 167 dt 221.952000 rms 0.727 0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6220 tsec 6.4590 #GCMRL# 168 dt 221.952000 rms 0.727 0.070% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6010 tsec 6.4390 #GCMRL# 169 dt 221.952000 rms 0.726 0.069% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5470 tsec 6.3860 #GCMRL# 170 dt 221.952000 rms 0.726 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5350 tsec 6.3780 #GCMRL# 171 dt 221.952000 rms 0.726 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5750 tsec 6.4150 #GCMRL# 172 dt 221.952000 rms 0.725 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5720 tsec 6.4190 #GCMRL# 173 dt 221.952000 rms 0.725 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5500 tsec 6.3910 #GCMRL# 174 dt 110.976000 rms 0.725 -0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5510 tsec 7.8560 #FOTS# QuadFit found better minimum quadopt=(dt=32.368,rms=0.724859) vs oldopt=(dt=23.12,rms=0.72486) #GCMRL# 175 dt 32.368000 rms 0.725 0.001% neg 0 invalid 762 tFOTS 10.3370 tGradient 5.5440 tsec 16.7220 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.724853) vs oldopt=(dt=92.48,rms=0.724853) #GCMRL# 176 dt 110.976000 rms 0.725 0.001% neg 0 invalid 762 tFOTS 9.6360 tGradient 5.5890 tsec 16.0660 #FOTS# QuadFit found better minimum quadopt=(dt=2071.55,rms=0.724612) vs oldopt=(dt=1479.68,rms=0.724658) #GCMRL# 177 dt 2071.552000 rms 0.725 0.033% neg 0 invalid 762 tFOTS 12.3730 tGradient 5.5210 tsec 18.7290 #GCAMreg# pass 0 level1 5 level2 1 tsec 862.515 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.01 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.725323 #GCMRL# 179 dt 369.920000 rms 0.723 0.269% neg 0 invalid 762 tFOTS 11.7390 tGradient 5.6440 tsec 18.2280 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.722747) vs oldopt=(dt=369.92,rms=0.722771) #GCMRL# 180 dt 295.936000 rms 0.723 0.086% neg 0 invalid 762 tFOTS 11.7220 tGradient 5.6350 tsec 18.1940 #FOTS# QuadFit found better minimum quadopt=(dt=73.984,rms=0.722688) vs oldopt=(dt=92.48,rms=0.722691) #GCMRL# 181 dt 73.984000 rms 0.723 0.000% neg 0 invalid 762 tFOTS 12.4370 tGradient 5.5120 tsec 18.8100 #GCMRL# 182 dt 73.984000 rms 0.723 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5900 tsec 6.4280 #GCMRL# 183 dt 73.984000 rms 0.723 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6190 tsec 6.4600 #GCMRL# 184 dt 73.984000 rms 0.723 0.010% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.4410 tsec 6.2810 #GCMRL# 185 dt 73.984000 rms 0.722 0.021% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6620 tsec 6.5000 #GCMRL# 186 dt 73.984000 rms 0.722 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5580 tsec 6.3940 #GCMRL# 187 dt 73.984000 rms 0.722 0.021% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5670 tsec 6.4120 #GCMRL# 188 dt 73.984000 rms 0.722 0.011% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5550 tsec 6.4130 setting smoothness cost coefficient to 0.031 #GCAMreg# pass 0 level1 4 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.03 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.726517 #FOTS# QuadFit found better minimum quadopt=(dt=230.086,rms=0.71799) vs oldopt=(dt=103.68,rms=0.719925) #GCMRL# 190 dt 230.085890 rms 0.718 1.174% neg 0 invalid 762 tFOTS 11.7390 tGradient 4.1560 tsec 16.7320 #FOTS# QuadFit found better minimum quadopt=(dt=161.228,rms=0.707714) vs oldopt=(dt=103.68,rms=0.708983) #GCMRL# 191 dt 161.227991 rms 0.708 1.431% neg 0 invalid 762 tFOTS 11.7570 tGradient 4.1360 tsec 16.7400 #FOTS# QuadFit found better minimum quadopt=(dt=73.8038,rms=0.703937) vs oldopt=(dt=103.68,rms=0.704423) #GCMRL# 192 dt 73.803787 rms 0.704 0.534% neg 0 invalid 762 tFOTS 11.0080 tGradient 4.0650 tsec 15.9100 #GCMRL# 193 dt 414.720000 rms 0.696 1.087% neg 0 invalid 762 tFOTS 11.0860 tGradient 4.1750 tsec 16.1050 #FOTS# QuadFit found better minimum quadopt=(dt=62.5508,rms=0.690238) vs oldopt=(dt=103.68,rms=0.692361) #GCMRL# 194 dt 62.550767 rms 0.690 0.868% neg 0 invalid 762 tFOTS 11.6890 tGradient 3.8830 tsec 16.4060 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.687919) vs oldopt=(dt=103.68,rms=0.688286) #GCMRL# 195 dt 145.152000 rms 0.688 0.336% neg 0 invalid 762 tFOTS 11.0120 tGradient 3.9240 tsec 15.7740 #FOTS# QuadFit found better minimum quadopt=(dt=97.5507,rms=0.685607) vs oldopt=(dt=103.68,rms=0.68562) #GCMRL# 196 dt 97.550661 rms 0.686 0.336% neg 0 invalid 762 tFOTS 11.6940 tGradient 3.8560 tsec 16.3860 #FOTS# QuadFit found better minimum quadopt=(dt=82.101,rms=0.684137) vs oldopt=(dt=103.68,rms=0.684231) #GCMRL# 197 dt 82.101010 rms 0.684 0.214% neg 0 invalid 762 tFOTS 11.7610 tGradient 3.8960 tsec 16.4960 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.682108) vs oldopt=(dt=103.68,rms=0.682273) #GCMRL# 198 dt 145.152000 rms 0.682 0.297% neg 0 invalid 762 tFOTS 11.0110 tGradient 3.8140 tsec 15.6660 #FOTS# QuadFit found better minimum quadopt=(dt=65.9632,rms=0.680541) vs oldopt=(dt=103.68,rms=0.680973) #GCMRL# 199 dt 65.963190 rms 0.681 0.230% neg 0 invalid 762 tFOTS 11.7220 tGradient 3.8670 tsec 16.4310 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.678447) vs oldopt=(dt=103.68,rms=0.678861) #GCMRL# 200 dt 145.152000 rms 0.678 0.308% neg 0 invalid 762 tFOTS 11.0280 tGradient 3.8380 tsec 15.7050 #FOTS# QuadFit found better minimum quadopt=(dt=88.3101,rms=0.677323) vs oldopt=(dt=103.68,rms=0.677361) #GCMRL# 201 dt 88.310078 rms 0.677 0.166% neg 0 invalid 762 tFOTS 11.7290 tGradient 3.8620 tsec 16.4290 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.675735) vs oldopt=(dt=103.68,rms=0.675785) #GCMRL# 202 dt 124.416000 rms 0.676 0.234% neg 0 invalid 762 tFOTS 10.9850 tGradient 3.8260 tsec 15.6490 #FOTS# QuadFit found better minimum quadopt=(dt=66.8657,rms=0.674668) vs oldopt=(dt=103.68,rms=0.674932) #GCMRL# 203 dt 66.865672 rms 0.675 0.158% neg 0 invalid 762 tFOTS 11.6800 tGradient 3.8420 tsec 16.3620 #FOTS# QuadFit found better minimum quadopt=(dt=248.832,rms=0.672371) vs oldopt=(dt=414.72,rms=0.672937) #GCMRL# 204 dt 248.832000 rms 0.672 0.341% neg 0 invalid 762 tFOTS 11.0420 tGradient 3.8430 tsec 15.7460 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.671073) vs oldopt=(dt=25.92,rms=0.671316) #GCMRL# 205 dt 36.288000 rms 0.671 0.193% neg 0 invalid 762 tFOTS 11.7130 tGradient 3.9040 tsec 16.4770 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.669948) vs oldopt=(dt=103.68,rms=0.670114) #GCMRL# 206 dt 145.152000 rms 0.670 0.168% neg 0 invalid 762 tFOTS 11.0080 tGradient 3.8300 tsec 15.6750 #GCMRL# 207 dt 103.680000 rms 0.669 0.171% neg 0 invalid 762 tFOTS 10.9850 tGradient 3.8480 tsec 15.6690 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.66801) vs oldopt=(dt=103.68,rms=0.668124) #GCMRL# 208 dt 82.944000 rms 0.668 0.118% neg 0 invalid 762 tFOTS 11.0460 tGradient 3.8550 tsec 15.7380 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.666919) vs oldopt=(dt=103.68,rms=0.666943) #GCMRL# 209 dt 124.416000 rms 0.667 0.163% neg 0 invalid 762 tFOTS 11.6790 tGradient 3.8560 tsec 16.3740 #FOTS# QuadFit found better minimum quadopt=(dt=62.208,rms=0.666197) vs oldopt=(dt=103.68,rms=0.666422) #GCMRL# 210 dt 62.208000 rms 0.666 0.108% neg 0 invalid 762 tFOTS 11.6880 tGradient 3.9890 tsec 16.5150 #FOTS# QuadFit found better minimum quadopt=(dt=331.776,rms=0.664474) vs oldopt=(dt=414.72,rms=0.664543) #GCMRL# 211 dt 331.776000 rms 0.664 0.259% neg 0 invalid 762 tFOTS 11.7250 tGradient 3.8730 tsec 16.4400 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.663294) vs oldopt=(dt=25.92,rms=0.663499) #GCMRL# 212 dt 36.288000 rms 0.663 0.178% neg 0 invalid 762 tFOTS 11.8190 tGradient 3.8410 tsec 16.5010 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.662439) vs oldopt=(dt=103.68,rms=0.662557) #GCMRL# 213 dt 145.152000 rms 0.662 0.129% neg 0 invalid 762 tFOTS 11.7440 tGradient 3.8810 tsec 16.4640 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.661684) vs oldopt=(dt=103.68,rms=0.661741) #GCMRL# 214 dt 82.944000 rms 0.662 0.114% neg 0 invalid 762 tFOTS 10.9860 tGradient 3.8910 tsec 15.7150 #GCMRL# 215 dt 103.680000 rms 0.661 0.090% neg 0 invalid 762 tFOTS 11.6730 tGradient 3.9210 tsec 16.4300 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.660425) vs oldopt=(dt=103.68,rms=0.660426) #GCMRL# 216 dt 82.944000 rms 0.660 0.100% neg 0 invalid 762 tFOTS 11.7000 tGradient 3.9120 tsec 16.4470 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.659795) vs oldopt=(dt=103.68,rms=0.659831) #GCMRL# 217 dt 145.152000 rms 0.660 0.095% neg 0 invalid 762 tFOTS 11.6920 tGradient 3.9260 tsec 16.4630 #FOTS# QuadFit found better minimum quadopt=(dt=67.4729,rms=0.65906) vs oldopt=(dt=103.68,rms=0.659227) #GCMRL# 218 dt 67.472868 rms 0.659 0.111% neg 0 invalid 762 tFOTS 11.6770 tGradient 3.8580 tsec 16.3700 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.658583) vs oldopt=(dt=103.68,rms=0.658628) #GCMRL# 219 dt 145.152000 rms 0.659 0.072% neg 0 invalid 762 tFOTS 11.7090 tGradient 3.8740 tsec 16.4260 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.657841) vs oldopt=(dt=103.68,rms=0.657886) #GCMRL# 220 dt 82.944000 rms 0.658 0.113% neg 0 invalid 762 tFOTS 11.0100 tGradient 3.8700 tsec 15.7230 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.657417) vs oldopt=(dt=103.68,rms=0.657419) #GCMRL# 221 dt 82.944000 rms 0.657 0.064% neg 0 invalid 762 tFOTS 11.0560 tGradient 3.8360 tsec 15.7290 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.656745) vs oldopt=(dt=103.68,rms=0.656799) #GCMRL# 222 dt 145.152000 rms 0.657 0.102% neg 0 invalid 762 tFOTS 11.0110 tGradient 3.8370 tsec 15.6870 #FOTS# QuadFit found better minimum quadopt=(dt=62.208,rms=0.656197) vs oldopt=(dt=103.68,rms=0.656377) #GCMRL# 223 dt 62.208000 rms 0.656 0.083% neg 0 invalid 762 tFOTS 11.0500 tGradient 3.9500 tsec 15.8410 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.655473) vs oldopt=(dt=103.68,rms=0.655617) #GCMRL# 224 dt 145.152000 rms 0.655 0.110% neg 0 invalid 762 tFOTS 11.0560 tGradient 3.9260 tsec 15.8220 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.65503) vs oldopt=(dt=103.68,rms=0.655047) #GCMRL# 225 dt 82.944000 rms 0.655 0.068% neg 0 invalid 762 tFOTS 11.0530 tGradient 3.8440 tsec 15.7370 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.654465) vs oldopt=(dt=103.68,rms=0.654478) #GCMRL# 226 dt 124.416000 rms 0.654 0.086% neg 0 invalid 762 tFOTS 10.9990 tGradient 3.8570 tsec 15.6930 #FOTS# QuadFit found better minimum quadopt=(dt=62.208,rms=0.654073) vs oldopt=(dt=103.68,rms=0.65422) #GCMRL# 227 dt 62.208000 rms 0.654 0.060% neg 0 invalid 762 tFOTS 11.0540 tGradient 3.8800 tsec 15.7700 #FOTS# QuadFit found better minimum quadopt=(dt=248.832,rms=0.653308) vs oldopt=(dt=414.72,rms=0.653545) #GCMRL# 228 dt 248.832000 rms 0.653 0.117% neg 0 invalid 762 tFOTS 11.0120 tGradient 3.8640 tsec 15.7130 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.652735) vs oldopt=(dt=25.92,rms=0.652855) #GCMRL# 229 dt 36.288000 rms 0.653 0.088% neg 0 invalid 762 tFOTS 11.0460 tGradient 3.9300 tsec 15.8160 #FOTS# QuadFit found better minimum quadopt=(dt=248.832,rms=0.651944) vs oldopt=(dt=414.72,rms=0.652109) #GCMRL# 230 dt 248.832000 rms 0.652 0.121% neg 0 invalid 762 tFOTS 11.0400 tGradient 3.9020 tsec 15.7860 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.651351) vs oldopt=(dt=25.92,rms=0.651458) #GCMRL# 231 dt 36.288000 rms 0.651 0.091% neg 0 invalid 762 tFOTS 11.0510 tGradient 3.9710 tsec 15.8690 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.650826) vs oldopt=(dt=103.68,rms=0.650929) #GCMRL# 232 dt 145.152000 rms 0.651 0.081% neg 0 invalid 762 tFOTS 11.0130 tGradient 3.8320 tsec 15.6870 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.650506) vs oldopt=(dt=103.68,rms=0.650529) #GCMRL# 233 dt 82.944000 rms 0.651 0.000% neg 0 invalid 762 tFOTS 11.0190 tGradient 3.8520 tsec 15.7300 #GCMRL# 234 dt 82.944000 rms 0.650 0.070% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8250 tsec 4.6640 #GCMRL# 235 dt 82.944000 rms 0.649 0.085% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8490 tsec 4.6860 #GCMRL# 236 dt 82.944000 rms 0.649 0.134% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9320 tsec 4.7680 #GCMRL# 237 dt 82.944000 rms 0.648 0.168% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9510 tsec 4.7920 #GCMRL# 238 dt 82.944000 rms 0.646 0.199% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9040 tsec 4.7490 #GCMRL# 239 dt 82.944000 rms 0.645 0.197% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8420 tsec 4.6860 #GCMRL# 240 dt 82.944000 rms 0.645 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8800 tsec 5.0580 #GCMRL# 241 dt 82.944000 rms 0.644 0.057% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8450 tsec 4.6810 #GCMRL# 242 dt 20.736000 rms 0.644 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8940 tsec 5.7500 #GCMRL# 243 dt 1.296000 rms 0.644 -0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8780 tsec 7.1760 #FOTS# QuadFit found better minimum quadopt=(dt=2.268,rms=0.644314) vs oldopt=(dt=1.62,rms=0.644317) #GCMRL# 244 dt 2.268000 rms 0.644 0.001% neg 0 invalid 762 tFOTS 8.9600 tGradient 3.8310 tsec 13.6290 #FOTS# QuadFit found better minimum quadopt=(dt=0.14175,rms=0.644314) vs oldopt=(dt=0.10125,rms=0.644314) #GCAMreg# pass 0 level1 4 level2 1 tsec 788.194 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.03 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.644908 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.641461) vs oldopt=(dt=103.68,rms=0.641796) #GCMRL# 246 dt 145.152000 rms 0.641 0.535% neg 0 invalid 762 tFOTS 11.0320 tGradient 3.8190 tsec 15.6900 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.641267) vs oldopt=(dt=25.92,rms=0.64131) #GCMRL# 247 dt 36.288000 rms 0.641 0.000% neg 0 invalid 762 tFOTS 11.6950 tGradient 3.8050 tsec 16.3600 #GCMRL# 248 dt 36.288000 rms 0.641 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8030 tsec 4.6370 #GCMRL# 249 dt 36.288000 rms 0.641 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8530 tsec 4.6920 #GCMRL# 250 dt 36.288000 rms 0.641 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8020 tsec 4.6400 #GCMRL# 251 dt 36.288000 rms 0.640 0.073% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8750 tsec 4.7080 #GCMRL# 252 dt 36.288000 rms 0.639 0.110% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8050 tsec 4.6520 #GCMRL# 253 dt 36.288000 rms 0.639 0.122% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8490 tsec 4.6910 #GCMRL# 254 dt 36.288000 rms 0.638 0.117% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8430 tsec 4.6830 #GCMRL# 255 dt 36.288000 rms 0.637 0.102% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9100 tsec 4.7490 #GCMRL# 256 dt 36.288000 rms 0.637 0.090% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9850 tsec 4.8200 #GCMRL# 257 dt 36.288000 rms 0.636 0.082% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8930 tsec 4.7300 #GCMRL# 258 dt 36.288000 rms 0.636 0.088% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9180 tsec 4.7540 #GCMRL# 259 dt 36.288000 rms 0.635 0.110% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9020 tsec 4.7370 #GCMRL# 260 dt 36.288000 rms 0.634 0.125% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9080 tsec 4.7440 #GCMRL# 261 dt 36.288000 rms 0.633 0.128% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9360 tsec 4.7720 #GCMRL# 262 dt 36.288000 rms 0.632 0.117% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8290 tsec 4.6670 #GCMRL# 263 dt 36.288000 rms 0.632 0.108% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8090 tsec 4.6460 #GCMRL# 264 dt 36.288000 rms 0.631 0.090% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7910 tsec 4.6310 #GCMRL# 265 dt 36.288000 rms 0.631 0.083% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8310 tsec 4.6670 #GCMRL# 266 dt 36.288000 rms 0.630 0.088% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8040 tsec 4.6400 #GCMRL# 267 dt 36.288000 rms 0.630 0.091% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8290 tsec 4.6670 #GCMRL# 268 dt 36.288000 rms 0.629 0.085% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9200 tsec 4.7540 #GCMRL# 269 dt 36.288000 rms 0.629 0.080% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8650 tsec 4.6990 #GCMRL# 270 dt 36.288000 rms 0.628 0.088% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9160 tsec 4.7530 #GCMRL# 271 dt 36.288000 rms 0.627 0.087% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8160 tsec 4.6550 #GCMRL# 272 dt 36.288000 rms 0.627 0.086% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8320 tsec 4.6740 #GCMRL# 273 dt 36.288000 rms 0.626 0.088% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7910 tsec 4.6330 #GCMRL# 274 dt 36.288000 rms 0.626 0.081% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8690 tsec 4.7090 #GCMRL# 275 dt 36.288000 rms 0.625 0.068% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8040 tsec 4.6440 #GCMRL# 276 dt 36.288000 rms 0.625 0.064% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8250 tsec 4.6640 #GCMRL# 277 dt 36.288000 rms 0.625 0.062% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8950 tsec 4.7440 #GCMRL# 278 dt 36.288000 rms 0.624 0.064% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8970 tsec 4.7390 #GCMRL# 279 dt 36.288000 rms 0.624 0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8230 tsec 4.6590 #GCMRL# 280 dt 36.288000 rms 0.623 0.069% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8300 tsec 4.6660 #GCMRL# 281 dt 36.288000 rms 0.623 0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8720 tsec 4.7120 #GCMRL# 282 dt 36.288000 rms 0.623 0.061% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8140 tsec 4.6510 #GCMRL# 283 dt 36.288000 rms 0.622 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8290 tsec 4.6660 #GCMRL# 284 dt 36.288000 rms 0.622 0.047% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7960 tsec 4.6430 #GCMRL# 285 dt 36.288000 rms 0.622 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8350 tsec 4.6760 #GCMRL# 286 dt 36.288000 rms 0.621 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8040 tsec 4.6460 #GCMRL# 287 dt 36.288000 rms 0.621 0.052% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9240 tsec 4.7710 #GCMRL# 288 dt 36.288000 rms 0.621 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8100 tsec 4.6450 #GCMRL# 289 dt 36.288000 rms 0.620 0.059% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8970 tsec 4.7340 #GCMRL# 290 dt 36.288000 rms 0.620 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8930 tsec 4.7280 #GCMRL# 291 dt 36.288000 rms 0.620 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8760 tsec 4.7170 #GCMRL# 292 dt 36.288000 rms 0.620 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8680 tsec 4.7630 #GCMRL# 293 dt 36.288000 rms 0.619 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9090 tsec 4.7520 #GCMRL# 294 dt 36.288000 rms 0.619 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8230 tsec 4.6580 #GCMRL# 295 dt 36.288000 rms 0.619 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8630 tsec 4.6990 #GCMRL# 296 dt 36.288000 rms 0.618 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8410 tsec 4.6760 #GCMRL# 297 dt 36.288000 rms 0.618 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9040 tsec 4.7410 #GCMRL# 298 dt 36.288000 rms 0.618 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9260 tsec 4.7680 #GCMRL# 299 dt 36.288000 rms 0.618 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8430 tsec 4.6810 #GCMRL# 300 dt 36.288000 rms 0.617 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8750 tsec 4.7200 #GCMRL# 301 dt 36.288000 rms 0.617 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8340 tsec 4.6750 #GCMRL# 302 dt 36.288000 rms 0.617 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8280 tsec 4.6700 #GCMRL# 303 dt 36.288000 rms 0.617 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8770 tsec 4.7180 #GCMRL# 304 dt 36.288000 rms 0.617 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8840 tsec 4.7250 #GCMRL# 305 dt 36.288000 rms 0.616 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8940 tsec 4.7340 #GCMRL# 306 dt 36.288000 rms 0.616 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9050 tsec 4.7430 #GCMRL# 307 dt 36.288000 rms 0.616 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8570 tsec 4.6950 #GCMRL# 308 dt 36.288000 rms 0.616 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8840 tsec 4.7220 #GCMRL# 309 dt 36.288000 rms 0.615 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8260 tsec 4.6670 #GCMRL# 310 dt 36.288000 rms 0.615 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8790 tsec 4.7200 #GCMRL# 311 dt 36.288000 rms 0.615 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8660 tsec 4.7100 #GCMRL# 312 dt 36.288000 rms 0.615 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8760 tsec 4.7150 #GCMRL# 313 dt 36.288000 rms 0.615 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8620 tsec 4.6970 #GCMRL# 314 dt 36.288000 rms 0.614 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8600 tsec 4.6960 #GCMRL# 315 dt 36.288000 rms 0.614 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8710 tsec 4.7110 #GCMRL# 316 dt 36.288000 rms 0.614 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8820 tsec 4.7340 #GCMRL# 317 dt 36.288000 rms 0.614 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8720 tsec 4.7090 #GCMRL# 318 dt 36.288000 rms 0.614 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8700 tsec 4.7130 #GCMRL# 319 dt 36.288000 rms 0.614 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8720 tsec 4.7160 #GCMRL# 320 dt 36.288000 rms 0.613 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8820 tsec 4.7210 #GCMRL# 321 dt 36.288000 rms 0.613 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8710 tsec 4.7200 #GCMRL# 322 dt 36.288000 rms 0.613 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8370 tsec 4.6770 #GCMRL# 323 dt 36.288000 rms 0.613 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8470 tsec 4.6850 #GCMRL# 324 dt 36.288000 rms 0.613 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8450 tsec 4.6810 #GCMRL# 325 dt 36.288000 rms 0.613 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8300 tsec 4.6660 #GCMRL# 326 dt 36.288000 rms 0.612 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8470 tsec 4.6870 #GCMRL# 327 dt 36.288000 rms 0.612 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8610 tsec 4.7040 #GCMRL# 328 dt 36.288000 rms 0.612 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8840 tsec 4.7290 #GCMRL# 329 dt 36.288000 rms 0.612 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8610 tsec 4.7010 #GCMRL# 330 dt 36.288000 rms 0.612 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8370 tsec 4.6700 #GCMRL# 331 dt 36.288000 rms 0.612 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8790 tsec 4.7230 #GCMRL# 332 dt 36.288000 rms 0.612 0.022% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9440 tsec 4.8070 #FOTS# QuadFit found better minimum quadopt=(dt=580.608,rms=0.611338) vs oldopt=(dt=414.72,rms=0.611383) #GCMRL# 333 dt 580.608000 rms 0.611 0.036% neg 0 invalid 762 tFOTS 12.4410 tGradient 3.8700 tsec 17.1510 #FOTS# QuadFit found better minimum quadopt=(dt=23.4667,rms=0.611345) vs oldopt=(dt=25.92,rms=0.611345) setting smoothness cost coefficient to 0.118 #GCAMreg# pass 0 level1 3 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.12 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.627603 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.626205) vs oldopt=(dt=8,rms=0.626353) #GCMRL# 335 dt 11.200000 rms 0.626 0.223% neg 0 invalid 762 tFOTS 11.0190 tGradient 3.3240 tsec 15.1780 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.625314) vs oldopt=(dt=32,rms=0.625397) #GCMRL# 336 dt 25.600000 rms 0.625 0.142% neg 0 invalid 762 tFOTS 11.0050 tGradient 3.3120 tsec 15.1570 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.622735) vs oldopt=(dt=32,rms=0.623305) #GCMRL# 337 dt 44.800000 rms 0.623 0.412% neg 0 invalid 762 tFOTS 11.0190 tGradient 3.2820 tsec 15.1410 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.61941) vs oldopt=(dt=32,rms=0.620291) #GCMRL# 338 dt 44.800000 rms 0.619 0.534% neg 0 invalid 762 tFOTS 10.3850 tGradient 3.1990 tsec 14.4220 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.616705) vs oldopt=(dt=32,rms=0.617414) #GCMRL# 339 dt 44.800000 rms 0.617 0.437% neg 0 invalid 762 tFOTS 10.3920 tGradient 3.1720 tsec 14.4090 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.614211) vs oldopt=(dt=32,rms=0.614556) #GCMRL# 340 dt 38.400000 rms 0.614 0.404% neg 0 invalid 762 tFOTS 11.0170 tGradient 3.1990 tsec 15.0540 #FOTS# QuadFit found better minimum quadopt=(dt=9.6,rms=0.613773) vs oldopt=(dt=8,rms=0.613851) #GCMRL# 341 dt 9.600000 rms 0.614 0.071% neg 0 invalid 762 tFOTS 10.3610 tGradient 3.1420 tsec 14.3410 #GCMRL# 342 dt 2.000000 rms 0.614 0.000% neg 0 invalid 762 tFOTS 9.6430 tGradient 3.1950 tsec 13.6930 #GCMRL# 343 dt 0.250000 rms 0.614 0.003% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1160 tsec 5.2850 #GCMRL# 344 dt 0.003906 rms 0.614 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1260 tsec 6.3060 #GCAMreg# pass 0 level1 3 level2 1 tsec 138.372 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.12 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.614114 #FOTS# QuadFit found better minimum quadopt=(dt=0.0015,rms=0.613609) vs oldopt=(dt=0.00125,rms=0.613609) #GCMRL# 346 dt 0.001500 rms 0.614 0.082% neg 0 invalid 762 tFOTS 8.9680 tGradient 3.1260 tsec 12.9340 #GCMRL# 347 dt 0.250000 rms 0.614 0.000% neg 0 invalid 762 tFOTS 4.8810 tGradient 3.1210 tsec 9.6290 #GCMRL# 348 dt 0.000061 rms 0.614 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1250 tsec 8.2890 #GCMRL# 349 dt 0.000031 rms 0.614 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1510 tsec 4.6690 setting smoothness cost coefficient to 0.400 #GCAMreg# pass 0 level1 2 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.40 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.654324 #GCMRL# 351 dt 11.520000 rms 0.648 0.977% neg 0 invalid 762 tFOTS 11.0110 tGradient 2.8710 tsec 14.7270 #FOTS# QuadFit found better minimum quadopt=(dt=4.032,rms=0.645487) vs oldopt=(dt=2.88,rms=0.646162) #GCMRL# 352 dt 4.032000 rms 0.645 0.377% neg 0 invalid 762 tFOTS 10.3230 tGradient 2.9330 tsec 14.0950 #GCMRL# 353 dt 11.520000 rms 0.640 0.782% neg 0 invalid 762 tFOTS 11.0580 tGradient 2.8950 tsec 14.7890 #FOTS# QuadFit found better minimum quadopt=(dt=4.032,rms=0.639315) vs oldopt=(dt=2.88,rms=0.639626) #GCMRL# 354 dt 4.032000 rms 0.639 0.175% neg 0 invalid 762 tFOTS 10.3180 tGradient 2.8660 tsec 14.0190 #GCMRL# 355 dt 11.520000 rms 0.637 0.399% neg 0 invalid 762 tFOTS 11.0610 tGradient 2.7910 tsec 14.6860 #FOTS# QuadFit found better minimum quadopt=(dt=4.032,rms=0.63609) vs oldopt=(dt=2.88,rms=0.636277) #GCMRL# 356 dt 4.032000 rms 0.636 0.106% neg 0 invalid 762 tFOTS 10.3270 tGradient 2.8610 tsec 14.0270 #FOTS# QuadFit found better minimum quadopt=(dt=4.032,rms=0.635528) vs oldopt=(dt=2.88,rms=0.635686) #GCMRL# 357 dt 4.032000 rms 0.636 0.088% neg 0 invalid 762 tFOTS 9.6530 tGradient 2.8130 tsec 13.3050 #GCMRL# 358 dt 11.520000 rms 0.634 0.226% neg 0 invalid 762 tFOTS 11.0400 tGradient 2.8180 tsec 14.6970 #FOTS# QuadFit found better minimum quadopt=(dt=4.032,rms=0.633794) vs oldopt=(dt=2.88,rms=0.63388) #GCMRL# 359 dt 4.032000 rms 0.634 0.000% neg 0 invalid 762 tFOTS 10.3750 tGradient 2.8850 tsec 14.1200 #GCMRL# 360 dt 4.032000 rms 0.633 0.060% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8690 tsec 3.7080 #GCMRL# 361 dt 4.032000 rms 0.633 0.093% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8690 tsec 3.7150 #GCMRL# 362 dt 4.032000 rms 0.632 0.119% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8720 tsec 3.7120 #GCMRL# 363 dt 4.032000 rms 0.631 0.146% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8910 tsec 3.7370 #GCMRL# 364 dt 4.032000 rms 0.631 0.019% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8670 tsec 4.0540 #GCMRL# 365 dt 4.032000 rms 0.631 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9470 tsec 3.7860 #GCMRL# 366 dt 4.032000 rms 0.630 0.065% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8420 tsec 3.6840 #GCMRL# 367 dt 4.032000 rms 0.630 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8670 tsec 4.0570 #GCMRL# 368 dt 4.032000 rms 0.630 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8170 tsec 3.9980 #GCMRL# 369 dt 4.032000 rms 0.630 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8190 tsec 3.6560 #GCMRL# 370 dt 4.032000 rms 0.630 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8680 tsec 4.0490 #GCMRL# 371 dt 4.032000 rms 0.629 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8300 tsec 3.6650 #GCMRL# 372 dt 4.032000 rms 0.629 0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8620 tsec 3.7010 #GCMRL# 373 dt 4.032000 rms 0.629 0.021% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8200 tsec 4.0120 #GCMRL# 374 dt 4.032000 rms 0.629 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8310 tsec 3.6710 #GCMRL# 375 dt 4.032000 rms 0.628 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8540 tsec 3.6940 #GCMRL# 376 dt 4.032000 rms 0.628 0.063% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8320 tsec 3.6780 #GCMRL# 377 dt 4.032000 rms 0.627 0.068% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8280 tsec 3.6730 #GCMRL# 378 dt 4.032000 rms 0.627 0.070% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8370 tsec 3.6770 #GCMRL# 379 dt 4.032000 rms 0.627 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8400 tsec 4.0260 #GCMRL# 380 dt 4.032000 rms 0.627 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8270 tsec 4.0360 #FOTS# QuadFit found better minimum quadopt=(dt=1.008,rms=0.626899) vs oldopt=(dt=0.72,rms=0.626901) #GCMRL# 381 dt 1.008000 rms 0.627 0.000% neg 0 invalid 762 tFOTS 9.6730 tGradient 2.8230 tsec 13.3530 #GCMRL# 382 dt 0.504000 rms 0.627 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8200 tsec 4.3280 #GCMRL# 383 dt 0.031500 rms 0.627 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8180 tsec 5.3240 #GCAMreg# pass 0 level1 2 level2 1 tsec 239.424 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.40 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.62748 #GCMRL# 385 dt 0.000000 rms 0.627 0.094% neg 0 invalid 762 tFOTS 10.3640 tGradient 2.8200 tsec 14.0200 #GCMRL# 386 dt 0.150000 rms 0.627 0.000% neg 0 invalid 762 tFOTS 10.3370 tGradient 2.8220 tsec 14.7830 setting smoothness cost coefficient to 1.000 #GCAMreg# pass 0 level1 1 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=1.00 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.666193 #GCMRL# 388 dt 0.000000 rms 0.666 0.083% neg 0 invalid 762 tFOTS 9.7390 tGradient 2.6630 tsec 13.2420 #GCMRL# 389 dt 0.100000 rms 0.666 0.000% neg 0 invalid 762 tFOTS 9.6900 tGradient 2.6620 tsec 13.9800 #GCAMreg# pass 0 level1 1 level2 1 tsec 35.18 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=1.00 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.666193 #GCMRL# 391 dt 0.000000 rms 0.666 0.083% neg 0 invalid 762 tFOTS 9.6990 tGradient 2.6810 tsec 13.2190 #GCMRL# 392 dt 0.100000 rms 0.666 0.000% neg 0 invalid 762 tFOTS 9.6630 tGradient 2.6920 tsec 13.9840 resetting metric properties... setting smoothness cost coefficient to 2.000 #GCAMreg# pass 0 level1 0 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.00 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=2.0,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.600287 #GCMRL# 394 dt 0.320000 rms 0.586 2.330% neg 0 invalid 762 tFOTS 10.4000 tGradient 2.1830 tsec 13.4220 #GCMRL# 395 dt 0.320000 rms 0.580 1.130% neg 0 invalid 762 tFOTS 10.3830 tGradient 2.1880 tsec 13.4170 #GCMRL# 396 dt 0.320000 rms 0.576 0.629% neg 0 invalid 762 tFOTS 10.3520 tGradient 2.1950 tsec 13.3870 #GCMRL# 397 dt 0.320000 rms 0.574 0.386% neg 0 invalid 762 tFOTS 10.3620 tGradient 2.1860 tsec 13.3860 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.573256) vs oldopt=(dt=0.08,rms=0.573419) #GCMRL# 398 dt 0.112000 rms 0.573 0.096% neg 0 invalid 762 tFOTS 9.6740 tGradient 2.1890 tsec 12.7030 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.57269) vs oldopt=(dt=0.08,rms=0.572845) #GCMRL# 399 dt 0.112000 rms 0.573 0.099% neg 0 invalid 762 tFOTS 9.6780 tGradient 2.1740 tsec 12.6930 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.572194) vs oldopt=(dt=0.08,rms=0.572338) #GCMRL# 400 dt 0.112000 rms 0.572 0.087% neg 0 invalid 762 tFOTS 9.6750 tGradient 2.1970 tsec 12.7100 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.571751) vs oldopt=(dt=0.08,rms=0.57188) #GCMRL# 401 dt 0.112000 rms 0.572 0.078% neg 0 invalid 762 tFOTS 9.6870 tGradient 2.1670 tsec 12.6930 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.571317) vs oldopt=(dt=0.08,rms=0.571436) #GCMRL# 402 dt 0.112000 rms 0.571 0.076% neg 0 invalid 762 tFOTS 9.6650 tGradient 2.1660 tsec 12.6670 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.570931) vs oldopt=(dt=0.08,rms=0.57104) #GCMRL# 403 dt 0.112000 rms 0.571 0.068% neg 0 invalid 762 tFOTS 9.6520 tGradient 2.1680 tsec 12.6580 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.570584) vs oldopt=(dt=0.08,rms=0.570685) #GCMRL# 404 dt 0.112000 rms 0.571 0.061% neg 0 invalid 762 tFOTS 9.6690 tGradient 2.1820 tsec 12.6930 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.570245) vs oldopt=(dt=0.08,rms=0.570339) #GCMRL# 405 dt 0.112000 rms 0.570 0.059% neg 0 invalid 762 tFOTS 9.6710 tGradient 2.2120 tsec 12.7230 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.569932) vs oldopt=(dt=0.08,rms=0.57002) #GCMRL# 406 dt 0.112000 rms 0.570 0.055% neg 0 invalid 762 tFOTS 9.6570 tGradient 2.1670 tsec 12.6630 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.56964) vs oldopt=(dt=0.08,rms=0.569723) #GCMRL# 407 dt 0.112000 rms 0.570 0.051% neg 0 invalid 762 tFOTS 9.6600 tGradient 2.1630 tsec 12.6620 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.569364) vs oldopt=(dt=0.08,rms=0.569442) #GCMRL# 408 dt 0.112000 rms 0.569 0.000% neg 0 invalid 762 tFOTS 9.6640 tGradient 2.1640 tsec 12.6910 #GCMRL# 409 dt 0.112000 rms 0.569 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1660 tsec 3.0090 #GCMRL# 410 dt 0.112000 rms 0.569 0.083% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1990 tsec 3.0440 #GCMRL# 411 dt 0.112000 rms 0.568 0.115% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2210 tsec 3.0630 #GCMRL# 412 dt 0.112000 rms 0.567 0.134% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1850 tsec 3.0290 #GCMRL# 413 dt 0.112000 rms 0.566 0.149% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1620 tsec 3.0070 #GCMRL# 414 dt 0.112000 rms 0.566 0.150% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1860 tsec 3.0240 #GCMRL# 415 dt 0.112000 rms 0.565 0.142% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2000 tsec 3.0370 #GCMRL# 416 dt 0.112000 rms 0.564 0.133% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1950 tsec 3.0310 #GCMRL# 417 dt 0.112000 rms 0.563 0.124% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1830 tsec 3.0190 #GCMRL# 418 dt 0.112000 rms 0.563 0.107% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1810 tsec 3.0200 #GCMRL# 419 dt 0.112000 rms 0.562 0.090% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1980 tsec 3.0350 #GCMRL# 420 dt 0.112000 rms 0.562 0.075% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2270 tsec 3.0680 #GCMRL# 421 dt 0.112000 rms 0.561 0.067% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1660 tsec 3.0070 #GCMRL# 422 dt 0.112000 rms 0.561 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1580 tsec 2.9990 #GCMRL# 423 dt 0.112000 rms 0.561 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1700 tsec 3.0110 #GCMRL# 424 dt 0.112000 rms 0.561 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1680 tsec 3.0070 #GCMRL# 425 dt 0.112000 rms 0.561 0.022% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2030 tsec 3.0430 #GCMRL# 426 dt 0.112000 rms 0.560 0.017% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1930 tsec 3.0510 #GCMRL# 427 dt 0.050000 rms 0.560 0.000% neg 0 invalid 762 tFOTS 8.9910 tGradient 2.1830 tsec 12.0540 #GCAMreg# pass 0 level1 0 level2 1 tsec 267.244 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.00 tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=0.5,type=2, relabel=0, neg=no blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.561109 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.558865) vs oldopt=(dt=0.08,rms=0.559308) #GCMRL# 429 dt 0.112000 rms 0.559 0.400% neg 0 invalid 762 tFOTS 9.6720 tGradient 2.2020 tsec 12.7100 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.557643) vs oldopt=(dt=0.08,rms=0.557995) #GCMRL# 430 dt 0.112000 rms 0.558 0.219% neg 0 invalid 762 tFOTS 9.6540 tGradient 2.1850 tsec 12.6770 #FOTS# QuadFit found better minimum quadopt=(dt=0.145833,rms=0.55636) vs oldopt=(dt=0.08,rms=0.556929) #GCMRL# 431 dt 0.145833 rms 0.556 0.230% neg 0 invalid 762 tFOTS 9.6960 tGradient 2.1800 tsec 12.7150 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.555655) vs oldopt=(dt=0.08,rms=0.555855) #GCMRL# 432 dt 0.112000 rms 0.556 0.127% neg 0 invalid 762 tFOTS 9.6410 tGradient 2.1840 tsec 12.6670 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.55509) vs oldopt=(dt=0.08,rms=0.555249) #GCMRL# 433 dt 0.112000 rms 0.555 0.102% neg 0 invalid 762 tFOTS 9.7000 tGradient 2.1970 tsec 12.7360 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.55465) vs oldopt=(dt=0.08,rms=0.554775) #GCMRL# 434 dt 0.112000 rms 0.555 0.079% neg 0 invalid 762 tFOTS 9.6710 tGradient 2.2070 tsec 12.7140 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.554304) vs oldopt=(dt=0.08,rms=0.554402) #GCMRL# 435 dt 0.112000 rms 0.554 0.062% neg 0 invalid 762 tFOTS 9.6590 tGradient 2.1890 tsec 12.6860 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.554037) vs oldopt=(dt=0.08,rms=0.554113) #GCMRL# 436 dt 0.112000 rms 0.554 0.000% neg 0 invalid 762 tFOTS 9.6520 tGradient 2.1910 tsec 12.7050 #GCMRL# 437 dt 0.112000 rms 0.554 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1820 tsec 3.0240 #GCMRL# 438 dt 0.112000 rms 0.553 0.061% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1830 tsec 3.0210 #GCMRL# 439 dt 0.112000 rms 0.553 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1900 tsec 3.0370 #GCMRL# 440 dt 0.112000 rms 0.553 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2690 tsec 3.1550 #GCMRL# 441 dt 0.112000 rms 0.553 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1880 tsec 3.0310 #GCMRL# 442 dt 0.112000 rms 0.553 -0.003% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1920 tsec 3.8240 GCAMregister done in 52.844 min ********************* ALLOWING NEGATIVE NODES IN DEFORMATION******************************** noneg post Starting GCAMregister() label assignment complete, 0 changed (0.00%) npasses = 1, nlevels = 6 #pass# 1 of 1 ************************ enabling zero nodes setting smoothness cost coefficient to 0.008 #GCAMreg# pass 0 level1 5 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.01 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.549991 #FOTS# QuadFit found better minimum quadopt=(dt=8.092,rms=0.549315) vs oldopt=(dt=5.78,rms=0.549316) #GCMRL# 444 dt 8.092000 rms 0.549 0.123% neg 0 invalid 762 tFOTS 13.1480 tGradient 5.0810 tsec 19.0650 #FOTS# QuadFit found better minimum quadopt=(dt=8.092,rms=0.549312) vs oldopt=(dt=5.78,rms=0.549312) #GCMRL# 445 dt 8.092000 rms 0.549 0.000% neg 0 invalid 762 tFOTS 13.1450 tGradient 5.0670 tsec 19.0700 #GCAMreg# pass 0 level1 5 level2 1 tsec 48.501 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.01 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.549984 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.547812) vs oldopt=(dt=369.92,rms=0.548164) #GCMRL# 447 dt 221.952000 rms 0.548 0.395% neg 0 invalid 762 tFOTS 12.4290 tGradient 5.0860 tsec 18.3530 #FOTS# QuadFit found better minimum quadopt=(dt=32.368,rms=0.54762) vs oldopt=(dt=23.12,rms=0.547658) #GCMRL# 448 dt 32.368000 rms 0.548 0.000% neg 0 invalid 762 tFOTS 13.1080 tGradient 5.0840 tsec 19.0470 #GCMRL# 449 dt 32.368000 rms 0.548 0.010% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.0780 tsec 5.9220 #GCMRL# 450 dt 32.368000 rms 0.548 0.006% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.0930 tsec 5.9690 setting smoothness cost coefficient to 0.031 #GCAMreg# pass 0 level1 4 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.03 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.548214 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.546718) vs oldopt=(dt=25.92,rms=0.546844) #GCMRL# 452 dt 36.288000 rms 0.547 0.273% neg 0 invalid 762 tFOTS 13.1240 tGradient 3.6950 tsec 17.6570 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.545548) vs oldopt=(dt=103.68,rms=0.545607) #GCMRL# 453 dt 82.944000 rms 0.546 0.000% neg 0 invalid 762 tFOTS 13.1030 tGradient 3.6980 tsec 17.6550 #GCMRL# 454 dt 82.944000 rms 0.544 0.228% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6650 tsec 4.5020 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 455 dt 82.944000 rms 0.544 0.091% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6670 tsec 5.1950 iter 0, gcam->neg = 3 after 6 iterations, nbhd size=0, neg = 0 #GCMRL# 456 dt 82.944000 rms 0.544 -0.119% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6740 tsec 8.1120 #FOTS# QuadFit found better minimum quadopt=(dt=62.208,rms=0.543639) vs oldopt=(dt=103.68,rms=0.543667) #GCMRL# 457 dt 62.208000 rms 0.544 0.030% neg 0 invalid 762 tFOTS 12.4230 tGradient 3.7110 tsec 16.9720 iter 0, gcam->neg = 1 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 458 dt 103.680000 rms 0.543 0.069% neg 0 invalid 762 tFOTS 12.4340 tGradient 3.6880 tsec 18.6900 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.543076) vs oldopt=(dt=25.92,rms=0.543087) iter 0, gcam->neg = 1 after 1 iterations, nbhd size=0, neg = 0 #GCAMreg# pass 0 level1 4 level2 1 tsec 110.435 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.03 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.543663 #FOTS# QuadFit found better minimum quadopt=(dt=129.593,rms=0.539183) vs oldopt=(dt=103.68,rms=0.539318) #GCMRL# 460 dt 129.593361 rms 0.539 0.824% neg 0 invalid 762 tFOTS 13.1360 tGradient 3.6770 tsec 17.6510 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.538572) vs oldopt=(dt=25.92,rms=0.538614) #GCMRL# 461 dt 36.288000 rms 0.539 0.000% neg 0 invalid 762 tFOTS 13.1000 tGradient 3.6960 tsec 17.6550 #GCMRL# 462 dt 36.288000 rms 0.538 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7270 tsec 4.5660 #GCMRL# 463 dt 36.288000 rms 0.538 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7090 tsec 4.5450 #GCMRL# 464 dt 36.288000 rms 0.538 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7360 tsec 4.5760 #GCMRL# 465 dt 36.288000 rms 0.537 0.098% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6900 tsec 4.5300 #GCMRL# 466 dt 36.288000 rms 0.537 0.140% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7030 tsec 4.5410 #GCMRL# 467 dt 36.288000 rms 0.536 0.130% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6950 tsec 4.5380 #GCMRL# 468 dt 36.288000 rms 0.536 0.083% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7030 tsec 4.5380 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 469 dt 36.288000 rms 0.535 0.072% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7080 tsec 5.2600 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.535092) vs oldopt=(dt=103.68,rms=0.535104) #GCMRL# 470 dt 82.944000 rms 0.535 0.000% neg 0 invalid 762 tFOTS 12.3740 tGradient 3.7120 tsec 16.9400 #GCMRL# 471 dt 82.944000 rms 0.535 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7100 tsec 4.5480 #GCMRL# 472 dt 82.944000 rms 0.534 0.076% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7130 tsec 4.5530 #GCMRL# 473 dt 82.944000 rms 0.534 0.070% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6960 tsec 4.5340 setting smoothness cost coefficient to 0.118 #GCAMreg# pass 0 level1 3 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.12 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.535063 #GCMRL# 475 dt 32.000000 rms 0.533 0.451% neg 0 invalid 762 tFOTS 13.1640 tGradient 3.0540 tsec 17.0550 #FOTS# QuadFit found better minimum quadopt=(dt=95.1233,rms=0.528787) vs oldopt=(dt=32,rms=0.530007) iter 0, gcam->neg = 75 after 25 iterations, nbhd size=1, neg = 0 #GCMRL# 476 dt 95.123288 rms 0.529 0.656% neg 0 invalid 762 tFOTS 13.1250 tGradient 3.0620 tsec 26.4020 #FOTS# QuadFit found better minimum quadopt=(dt=21.2559,rms=0.527528) vs oldopt=(dt=32,rms=0.528081) iter 0, gcam->neg = 25 after 15 iterations, nbhd size=0, neg = 0 #GCMRL# 477 dt 21.255898 rms 0.528 0.285% neg 0 invalid 762 tFOTS 13.1360 tGradient 3.1190 tsec 22.9590 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.525753) vs oldopt=(dt=32,rms=0.526088) iter 0, gcam->neg = 57 after 21 iterations, nbhd size=1, neg = 0 #GCMRL# 478 dt 44.800000 rms 0.526 0.276% neg 0 invalid 762 tFOTS 13.1730 tGradient 3.2930 tsec 25.3100 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.525566) vs oldopt=(dt=32,rms=0.525642) iter 0, gcam->neg = 67 after 12 iterations, nbhd size=0, neg = 0 #GCMRL# 479 dt 25.600000 rms 0.526 0.000% neg 0 invalid 762 tFOTS 13.0890 tGradient 3.2000 tsec 21.9820 iter 0, gcam->neg = 28 after 8 iterations, nbhd size=0, neg = 0 #GCMRL# 480 dt 25.600000 rms 0.525 0.137% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2460 tsec 7.5540 iter 0, gcam->neg = 133 after 20 iterations, nbhd size=1, neg = 0 #GCMRL# 481 dt 25.600000 rms 0.524 0.123% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1300 tsec 11.6470 iter 0, gcam->neg = 65 after 20 iterations, nbhd size=1, neg = 0 #GCMRL# 482 dt 25.600000 rms 0.523 0.174% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1390 tsec 11.6420 iter 0, gcam->neg = 112 after 24 iterations, nbhd size=1, neg = 0 #GCMRL# 483 dt 25.600000 rms 0.522 0.189% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2770 tsec 13.1990 iter 0, gcam->neg = 277 after 24 iterations, nbhd size=1, neg = 0 #GCMRL# 484 dt 25.600000 rms 0.521 0.189% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2790 tsec 13.1720 iter 0, gcam->neg = 482 after 42 iterations, nbhd size=1, neg = 0 #GCMRL# 485 dt 25.600000 rms 0.521 0.080% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1300 tsec 19.3130 iter 0, gcam->neg = 405 after 33 iterations, nbhd size=1, neg = 0 #GCMRL# 486 dt 25.600000 rms 0.521 -0.003% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0880 tsec 16.9520 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.520507) vs oldopt=(dt=32,rms=0.520513) iter 0, gcam->neg = 44 after 10 iterations, nbhd size=0, neg = 0 #GCMRL# 487 dt 25.600000 rms 0.521 0.060% neg 0 invalid 762 tFOTS 13.1330 tGradient 3.0880 tsec 21.2390 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.519935) vs oldopt=(dt=32,rms=0.520013) iter 0, gcam->neg = 52 after 17 iterations, nbhd size=1, neg = 0 #GCMRL# 488 dt 44.800000 rms 0.520 0.100% neg 0 invalid 762 tFOTS 13.1270 tGradient 3.1160 tsec 23.7320 #FOTS# QuadFit found better minimum quadopt=(dt=19.2,rms=0.519678) vs oldopt=(dt=32,rms=0.519746) iter 0, gcam->neg = 12 after 11 iterations, nbhd size=1, neg = 0 #GCAMreg# pass 0 level1 3 level2 1 tsec 277.426 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.12 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.520289 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.51607) vs oldopt=(dt=32,rms=0.516417) iter 0, gcam->neg = 15 after 14 iterations, nbhd size=1, neg = 0 #GCMRL# 490 dt 44.800000 rms 0.516 0.801% neg 0 invalid 762 tFOTS 13.1060 tGradient 3.1490 tsec 22.6050 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.514937) vs oldopt=(dt=32,rms=0.514972) iter 0, gcam->neg = 3 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 491 dt 25.600000 rms 0.515 0.000% neg 0 invalid 762 tFOTS 13.1100 tGradient 3.0780 tsec 18.4390 iter 0, gcam->neg = 2 after 7 iterations, nbhd size=1, neg = 0 #GCMRL# 492 dt 25.600000 rms 0.514 0.146% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2290 tsec 7.2080 iter 0, gcam->neg = 10 after 4 iterations, nbhd size=0, neg = 0 #GCMRL# 493 dt 25.600000 rms 0.513 0.244% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0910 tsec 6.0270 iter 0, gcam->neg = 3 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 494 dt 25.600000 rms 0.512 0.214% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1970 tsec 5.4270 iter 0, gcam->neg = 6 after 17 iterations, nbhd size=1, neg = 0 #GCMRL# 495 dt 25.600000 rms 0.511 0.112% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2560 tsec 10.6740 iter 0, gcam->neg = 7 after 11 iterations, nbhd size=1, neg = 0 #GCMRL# 496 dt 25.600000 rms 0.511 0.099% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2400 tsec 8.6020 iter 0, gcam->neg = 5 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 497 dt 32.000000 rms 0.510 0.177% neg 0 invalid 762 tFOTS 13.1000 tGradient 3.1000 tsec 18.8250 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.509598) vs oldopt=(dt=8,rms=0.509658) #GCMRL# 498 dt 11.200000 rms 0.510 0.000% neg 0 invalid 762 tFOTS 13.1300 tGradient 3.2590 tsec 17.2550 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 499 dt 11.200000 rms 0.509 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2960 tsec 4.8290 iter 0, gcam->neg = 3 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 500 dt 11.200000 rms 0.509 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1540 tsec 5.3740 iter 0, gcam->neg = 4 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 501 dt 11.200000 rms 0.509 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1210 tsec 5.3360 iter 0, gcam->neg = 5 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 502 dt 11.200000 rms 0.509 0.072% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1190 tsec 5.3450 iter 0, gcam->neg = 7 after 5 iterations, nbhd size=0, neg = 0 #GCMRL# 503 dt 11.200000 rms 0.508 0.060% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1580 tsec 6.4040 iter 0, gcam->neg = 5 after 13 iterations, nbhd size=1, neg = 0 setting smoothness cost coefficient to 0.400 #GCAMreg# pass 0 level1 2 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.40 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.516247 #FOTS# QuadFit found better minimum quadopt=(dt=0.000210938,rms=0.515624) vs oldopt=(dt=0.000175781,rms=0.515624) #GCMRL# 505 dt 0.000211 rms 0.516 0.121% neg 0 invalid 762 tFOTS 15.8710 tGradient 2.8660 tsec 19.5780 #GCAMreg# pass 0 level1 2 level2 1 tsec 40.211 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.40 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.516247 #FOTS# QuadFit found better minimum quadopt=(dt=2.304,rms=0.515523) vs oldopt=(dt=2.88,rms=0.51553) iter 0, gcam->neg = 2 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 507 dt 2.304000 rms 0.516 0.141% neg 0 invalid 762 tFOTS 13.1790 tGradient 2.8900 tsec 18.6540 #FOTS# QuadFit found better minimum quadopt=(dt=1.728,rms=0.515491) vs oldopt=(dt=2.88,rms=0.515506) #GCMRL# 508 dt 1.728000 rms 0.515 0.000% neg 0 invalid 762 tFOTS 13.1650 tGradient 2.8650 tsec 16.8850 iter 0, gcam->neg = 1 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 509 dt 1.728000 rms 0.515 0.009% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8640 tsec 5.0850 iter 0, gcam->neg = 1 after 3 iterations, nbhd size=0, neg = 0 setting smoothness cost coefficient to 1.000 #GCAMreg# pass 0 level1 1 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=1.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.529495 #FOTS# QuadFit found better minimum quadopt=(dt=1.024,rms=0.528391) vs oldopt=(dt=1.28,rms=0.528471) #GCMRL# 511 dt 1.024000 rms 0.528 0.209% neg 0 invalid 762 tFOTS 13.1600 tGradient 2.7110 tsec 16.7170 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.528342) vs oldopt=(dt=0.32,rms=0.528343) #GCMRL# 512 dt 0.384000 rms 0.528 0.000% neg 0 invalid 762 tFOTS 13.2970 tGradient 2.7360 tsec 16.8960 #GCAMreg# pass 0 level1 1 level2 1 tsec 41.605 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=1.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.528944 #FOTS# QuadFit found better minimum quadopt=(dt=1.792,rms=0.526844) vs oldopt=(dt=1.28,rms=0.527069) iter 0, gcam->neg = 5 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 514 dt 1.792000 rms 0.527 0.394% neg 0 invalid 762 tFOTS 13.1670 tGradient 2.7400 tsec 18.1900 #FOTS# QuadFit found better minimum quadopt=(dt=1.792,rms=0.526195) vs oldopt=(dt=1.28,rms=0.526294) iter 0, gcam->neg = 5 after 11 iterations, nbhd size=1, neg = 0 #GCMRL# 515 dt 1.792000 rms 0.526 0.000% neg 0 invalid 762 tFOTS 13.1600 tGradient 2.7080 tsec 21.2220 iter 0, gcam->neg = 10 after 15 iterations, nbhd size=1, neg = 0 #GCMRL# 516 dt 1.792000 rms 0.526 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7330 tsec 9.5920 iter 0, gcam->neg = 12 after 3 iterations, nbhd size=0, neg = 0 resetting metric properties... setting smoothness cost coefficient to 2.000 #GCAMreg# pass 0 level1 0 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.515647 #FOTS# QuadFit found better minimum quadopt=(dt=1.84185,rms=0.478961) vs oldopt=(dt=1.28,rms=0.482663) iter 0, gcam->neg = 2345 after 17 iterations, nbhd size=1, neg = 0 #GCMRL# 518 dt 1.841854 rms 0.482 6.438% neg 0 invalid 762 tFOTS 13.2170 tGradient 2.2620 tsec 23.0330 #GCMRL# 519 dt 0.000013 rms 0.482 0.000% neg 0 invalid 762 tFOTS 16.6560 tGradient 2.2110 tsec 19.7260 #GCAMreg# pass 0 level1 0 level2 1 tsec 50.269 sigma 0.5 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.483188 #FOTS# QuadFit found better minimum quadopt=(dt=0.064,rms=0.482276) vs oldopt=(dt=0.08,rms=0.482289) #GCMRL# 521 dt 0.064000 rms 0.482 0.189% neg 0 invalid 762 tFOTS 13.2980 tGradient 2.2130 tsec 16.3550 #FOTS# QuadFit found better minimum quadopt=(dt=9.375e-05,rms=0.482263) vs oldopt=(dt=7.8125e-05,rms=0.482263) #GCMRL# 522 dt 0.000094 rms 0.482 0.000% neg 0 invalid 762 tFOTS 13.1990 tGradient 2.2300 tsec 16.2880 label assignment complete, 0 changed (0.00%) GCAMregister done in 17.3042 min Starting GCAMcomputeMaxPriorLabels() Morphing with label term set to 0 ******************************* Starting GCAMregister() label assignment complete, 0 changed (0.00%) npasses = 1, nlevels = 6 #pass# 1 of 1 ************************ enabling zero nodes setting smoothness cost coefficient to 0.008 #GCAMreg# pass 0 level1 5 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.01 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.468086 #GCAMreg# pass 0 level1 5 level2 1 tsec 22.09 sigma 0.5 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.01 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.468086 #FOTS# QuadFit found better minimum quadopt=(dt=73.984,rms=0.46799) vs oldopt=(dt=92.48,rms=0.467998) #GCMRL# 525 dt 73.984000 rms 0.468 0.020% neg 0 invalid 762 tFOTS 12.2020 tGradient 4.6280 tsec 17.6580 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.46794) vs oldopt=(dt=92.48,rms=0.467944) #GCMRL# 526 dt 129.472000 rms 0.468 0.000% neg 0 invalid 762 tFOTS 12.2280 tGradient 4.6300 tsec 17.7060 #GCMRL# 527 dt 129.472000 rms 0.468 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6360 tsec 5.4670 #GCMRL# 528 dt 129.472000 rms 0.468 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6330 tsec 5.4640 setting smoothness cost coefficient to 0.031 #GCAMreg# pass 0 level1 4 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.03 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.46809 #GCMRL# 530 dt 6.480000 rms 0.468 0.005% neg 0 invalid 762 tFOTS 12.9460 tGradient 3.2930 tsec 17.0690 #FOTS# QuadFit found better minimum quadopt=(dt=1.944,rms=0.468064) vs oldopt=(dt=1.62,rms=0.468064) #GCMRL# 531 dt 1.944000 rms 0.468 0.000% neg 0 invalid 762 tFOTS 12.9470 tGradient 3.2590 tsec 17.0590 #GCAMreg# pass 0 level1 4 level2 1 tsec 42.602 sigma 0.5 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.03 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.468064 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.467067) vs oldopt=(dt=103.68,rms=0.467137) iter 0, gcam->neg = 1 after 7 iterations, nbhd size=1, neg = 0 #GCMRL# 533 dt 145.152000 rms 0.467 0.203% neg 0 invalid 762 tFOTS 12.2130 tGradient 3.2470 tsec 19.4470 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.466875) vs oldopt=(dt=25.92,rms=0.466908) #GCMRL# 534 dt 36.288000 rms 0.467 0.000% neg 0 invalid 762 tFOTS 12.2510 tGradient 3.2540 tsec 16.3560 #GCMRL# 535 dt 36.288000 rms 0.467 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2500 tsec 4.0790 #GCMRL# 536 dt 36.288000 rms 0.467 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2470 tsec 4.0790 #GCMRL# 537 dt 36.288000 rms 0.466 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2420 tsec 4.0720 iter 0, gcam->neg = 2 after 1 iterations, nbhd size=0, neg = 0 #GCMRL# 538 dt 36.288000 rms 0.466 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2490 tsec 5.1100 iter 0, gcam->neg = 1 after 1 iterations, nbhd size=0, neg = 0 setting smoothness cost coefficient to 0.118 #GCAMreg# pass 0 level1 3 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.12 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.466828 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.466035) vs oldopt=(dt=8,rms=0.466175) iter 0, gcam->neg = 5 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 540 dt 11.200000 rms 0.466 0.170% neg 0 invalid 762 tFOTS 12.8860 tGradient 2.6410 tsec 17.0560 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.465614) vs oldopt=(dt=8,rms=0.465697) iter 0, gcam->neg = 21 after 13 iterations, nbhd size=1, neg = 0 #GCMRL# 541 dt 11.200000 rms 0.466 0.000% neg 0 invalid 762 tFOTS 12.9480 tGradient 2.6560 tsec 21.6350 iter 0, gcam->neg = 13 after 8 iterations, nbhd size=1, neg = 0 #GCMRL# 542 dt 11.200000 rms 0.466 0.064% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6440 tsec 6.9180 iter 0, gcam->neg = 53 after 19 iterations, nbhd size=1, neg = 0 #GCAMreg# pass 0 level1 3 level2 1 tsec 60.694 sigma 0.5 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.12 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.46567 #FOTS# QuadFit found better minimum quadopt=(dt=50.4,rms=0.462069) vs oldopt=(dt=32,rms=0.462432) iter 0, gcam->neg = 43 after 14 iterations, nbhd size=1, neg = 0 #GCMRL# 544 dt 50.400000 rms 0.462 0.750% neg 0 invalid 762 tFOTS 12.9110 tGradient 2.6380 tsec 21.9550 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.460809) vs oldopt=(dt=32,rms=0.460901) iter 0, gcam->neg = 18 after 15 iterations, nbhd size=1, neg = 0 #GCMRL# 545 dt 25.600000 rms 0.461 0.288% neg 0 invalid 762 tFOTS 12.8860 tGradient 2.6370 tsec 22.2080 #FOTS# QuadFit found better minimum quadopt=(dt=97.1617,rms=0.459249) vs oldopt=(dt=128,rms=0.459442) iter 0, gcam->neg = 68 after 17 iterations, nbhd size=1, neg = 0 #GCMRL# 546 dt 97.161716 rms 0.460 0.264% neg 0 invalid 762 tFOTS 12.8710 tGradient 2.6410 tsec 22.9140 #FOTS# QuadFit found better minimum quadopt=(dt=22.1538,rms=0.458659) vs oldopt=(dt=32,rms=0.458943) iter 0, gcam->neg = 10 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 547 dt 22.153846 rms 0.459 0.000% neg 0 invalid 762 tFOTS 12.9120 tGradient 2.6430 tsec 18.1420 iter 0, gcam->neg = 5 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 548 dt 22.153846 rms 0.458 0.078% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6620 tsec 4.1900 iter 0, gcam->neg = 10 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 549 dt 22.153846 rms 0.458 0.112% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6460 tsec 5.2060 iter 0, gcam->neg = 27 after 11 iterations, nbhd size=1, neg = 0 #GCMRL# 550 dt 22.153846 rms 0.457 0.114% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6430 tsec 7.9650 iter 0, gcam->neg = 39 after 19 iterations, nbhd size=1, neg = 0 #GCMRL# 551 dt 22.153846 rms 0.457 0.106% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6550 tsec 10.7870 iter 0, gcam->neg = 29 after 20 iterations, nbhd size=1, neg = 0 #GCMRL# 552 dt 22.153846 rms 0.456 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6470 tsec 11.1080 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.456066) vs oldopt=(dt=32,rms=0.456139) iter 0, gcam->neg = 14 after 6 iterations, nbhd size=0, neg = 0 #GCMRL# 553 dt 44.800000 rms 0.456 0.081% neg 0 invalid 762 tFOTS 12.2200 tGradient 2.6430 tsec 18.4640 iter 0, gcam->neg = 5 after 12 iterations, nbhd size=1, neg = 0 setting smoothness cost coefficient to 0.400 #GCAMreg# pass 0 level1 2 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.40 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.460462 #FOTS# QuadFit found better minimum quadopt=(dt=4.72727,rms=0.459995) vs oldopt=(dt=2.88,rms=0.460076) iter 0, gcam->neg = 12 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 555 dt 4.727273 rms 0.460 0.100% neg 0 invalid 762 tFOTS 12.9370 tGradient 2.4060 tsec 17.9160 iter 0, gcam->neg = 7 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 556 dt 2.880000 rms 0.460 0.000% neg 0 invalid 762 tFOTS 12.9800 tGradient 2.3950 tsec 17.6210 iter 0, gcam->neg = 13 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 557 dt 2.880000 rms 0.460 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3970 tsec 4.9910 iter 0, gcam->neg = 31 after 14 iterations, nbhd size=1, neg = 0 #GCAMreg# pass 0 level1 2 level2 1 tsec 53.722 sigma 0.5 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.40 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.459923 #FOTS# QuadFit found better minimum quadopt=(dt=16.8496,rms=0.458048) vs oldopt=(dt=11.52,rms=0.458229) iter 0, gcam->neg = 56 after 15 iterations, nbhd size=1, neg = 0 #GCMRL# 559 dt 16.849558 rms 0.458 0.358% neg 0 invalid 762 tFOTS 12.9760 tGradient 2.3940 tsec 22.1160 #FOTS# QuadFit found better minimum quadopt=(dt=16.1311,rms=0.456549) vs oldopt=(dt=11.52,rms=0.456697) iter 0, gcam->neg = 171 after 17 iterations, nbhd size=1, neg = 0 #GCMRL# 560 dt 16.131148 rms 0.457 0.280% neg 0 invalid 762 tFOTS 12.9370 tGradient 2.3890 tsec 22.8200 #FOTS# QuadFit found better minimum quadopt=(dt=9.216,rms=0.456521) vs oldopt=(dt=11.52,rms=0.456545) iter 0, gcam->neg = 41 after 18 iterations, nbhd size=1, neg = 0 #GCMRL# 561 dt 9.216000 rms 0.457 0.000% neg 0 invalid 762 tFOTS 12.8720 tGradient 2.3850 tsec 23.0000 iter 0, gcam->neg = 42 after 13 iterations, nbhd size=1, neg = 0 #GCMRL# 562 dt 9.216000 rms 0.456 0.191% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3850 tsec 8.3870 iter 0, gcam->neg = 73 after 24 iterations, nbhd size=1, neg = 0 #GCMRL# 563 dt 9.216000 rms 0.455 0.162% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3850 tsec 12.1660 iter 0, gcam->neg = 125 after 24 iterations, nbhd size=1, neg = 0 #GCMRL# 564 dt 9.216000 rms 0.455 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3900 tsec 12.2740 iter 0, gcam->neg = 159 after 24 iterations, nbhd size=1, neg = 0 #GCMRL# 565 dt 9.216000 rms 0.454 0.155% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3860 tsec 12.2090 iter 0, gcam->neg = 239 after 29 iterations, nbhd size=1, neg = 0 #GCMRL# 566 dt 9.216000 rms 0.454 0.106% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3860 tsec 13.9500 iter 0, gcam->neg = 294 after 19 iterations, nbhd size=1, neg = 0 #GCMRL# 567 dt 9.216000 rms 0.454 0.064% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3980 tsec 10.5210 #FOTS# QuadFit found better minimum quadopt=(dt=9.26316,rms=0.453225) vs oldopt=(dt=11.52,rms=0.453247) iter 0, gcam->neg = 61 after 18 iterations, nbhd size=1, neg = 0 #GCMRL# 568 dt 9.263158 rms 0.453 0.000% neg 0 invalid 762 tFOTS 12.8580 tGradient 2.3850 tsec 23.0170 iter 0, gcam->neg = 44 after 16 iterations, nbhd size=1, neg = 0 #GCMRL# 569 dt 9.263158 rms 0.453 0.062% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3830 tsec 9.4260 iter 0, gcam->neg = 89 after 18 iterations, nbhd size=1, neg = 0 #GCMRL# 570 dt 9.263158 rms 0.453 0.047% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3970 tsec 10.1210 iter 0, gcam->neg = 117 after 26 iterations, nbhd size=1, neg = 0 setting smoothness cost coefficient to 1.000 #GCAMreg# pass 0 level1 1 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=1.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.460299 #GCMRL# 572 dt 0.000050 rms 0.460 0.000% neg 0 invalid 762 tFOTS 16.3460 tGradient 2.2240 tsec 19.4050 #GCAMreg# pass 0 level1 1 level2 1 tsec 39.239 sigma 0.5 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=1.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.460299 resetting metric properties... setting smoothness cost coefficient to 2.000 #GCAMreg# pass 0 level1 0 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=2.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.447787 #FOTS# QuadFit found better minimum quadopt=(dt=0.850218,rms=0.438509) vs oldopt=(dt=1.28,rms=0.441267) iter 0, gcam->neg = 1061 after 24 iterations, nbhd size=1, neg = 0 #GCMRL# 575 dt 0.850218 rms 0.442 1.397% neg 0 invalid 762 tFOTS 12.9660 tGradient 1.7600 tsec 24.6380 #FOTS# QuadFit found better minimum quadopt=(dt=0.048,rms=0.441472) vs oldopt=(dt=0.08,rms=0.441485) #GCMRL# 576 dt 0.048000 rms 0.441 0.000% neg 0 invalid 762 tFOTS 12.9200 tGradient 1.7530 tsec 15.5170 #GCMRL# 577 dt 0.048000 rms 0.441 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.7450 tsec 2.5740 #GCAMreg# pass 0 level1 0 level2 1 tsec 49.696 sigma 0.5 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=2.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=0.5,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=0.500... GCAMRegisterLevel(): init RMS 0.44145 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.438874) vs oldopt=(dt=0.32,rms=0.439192) iter 0, gcam->neg = 16 after 10 iterations, nbhd size=1, neg = 0 #GCMRL# 579 dt 0.448000 rms 0.439 0.582% neg 0 invalid 762 tFOTS 12.9150 tGradient 1.7440 tsec 19.6660 #FOTS# QuadFit found better minimum quadopt=(dt=0.462185,rms=0.436856) vs oldopt=(dt=0.32,rms=0.437185) iter 0, gcam->neg = 52 after 6 iterations, nbhd size=0, neg = 0 #GCMRL# 580 dt 0.462185 rms 0.437 0.448% neg 0 invalid 762 tFOTS 12.9170 tGradient 1.7470 tsec 18.2570 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.435989) vs oldopt=(dt=0.32,rms=0.435992) iter 0, gcam->neg = 9 after 1 iterations, nbhd size=0, neg = 0 #GCMRL# 581 dt 0.384000 rms 0.436 0.000% neg 0 invalid 762 tFOTS 12.9120 tGradient 1.7490 tsec 16.5400 iter 0, gcam->neg = 9 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 582 dt 0.384000 rms 0.435 0.152% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.7500 tsec 3.2720 iter 0, gcam->neg = 34 after 6 iterations, nbhd size=0, neg = 0 #GCMRL# 583 dt 0.384000 rms 0.434 0.216% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.7550 tsec 5.3610 iter 0, gcam->neg = 77 after 8 iterations, nbhd size=0, neg = 0 #GCMRL# 584 dt 0.384000 rms 0.434 0.112% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.7540 tsec 6.0380 iter 0, gcam->neg = 128 after 16 iterations, nbhd size=1, neg = 0 #GCMRL# 585 dt 0.384000 rms 0.434 -0.016% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.7550 tsec 9.6060 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.433633) vs oldopt=(dt=0.08,rms=0.433685) #GCMRL# 586 dt 0.112000 rms 0.434 0.067% neg 0 invalid 762 tFOTS 12.9160 tGradient 1.7490 tsec 15.4910 #FOTS# QuadFit found better minimum quadopt=(dt=0.256,rms=0.433344) vs oldopt=(dt=0.32,rms=0.433363) iter 0, gcam->neg = 3 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 587 dt 0.256000 rms 0.433 0.067% neg 0 invalid 762 tFOTS 12.9110 tGradient 1.7500 tsec 16.1910 #FOTS# QuadFit found better minimum quadopt=(dt=0.256,rms=0.433188) vs oldopt=(dt=0.32,rms=0.433216) GCAMregister done in 14.9158 min writing output transformation to transforms/talairach.m3z... GCAMwrite Calls to gcamLogLikelihoodEnergy 4897 tmin = 13.9768 Calls to gcamLabelEnergy 4181 tmin = 0.843417 Calls to gcamJacobianEnergy 4897 tmin = 11.5522 Calls to gcamSmoothnessEnergy 4897 tmin = 12.7383 Calls to gcamLogLikelihoodTerm 589 tmin = 3.30913 Calls to gcamLabelTerm 524 tmin = 5.33865 Calls to gcamJacobianTerm 589 tmin = 9.32535 Calls to gcamSmoothnessTerm 589 tmin = 2.72102 Calls to gcamComputeGradient 589 tmin = 36.9655 Calls to gcamComputeMetricProperties 7635 tmin = 16.5698 mri_ca_register took 1 hours, 45 minutes and 49 seconds. #VMPC# mri_ca_register VmPeak 2023500 FSRUNTIME@ mri_ca_register 1.7635 hours 1 threads @#@FSTIME 2022:02:17:06:38:52 mri_ca_register N 9 e 6348.78 S 2.39 U 6332.76 P 99% M 1320224 F 11 R 1431045 W 0 c 88643 w 924 I 1840 O 64480 L 1.12 1.07 1.02 @#@FSLOADPOST 2022:02:17:08:24:41 mri_ca_register N 9 1.03 1.05 1.06 #-------------------------------------- #@# SubCort Seg Thu Feb 17 08:24:41 EST 2022 mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz sysname Linux hostname erso.nmr.mgh.harvard.edu machine x86_64 setenv SUBJECTS_DIR /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz relabeling unlikely voxels with window_size = 9 and prior threshold 0.30 using Gibbs prior factor = 0.500 renormalizing sequences with structure alignment, equivalent to: -renormalize -renormalize_mean 0.500 -regularize 0.500 == Number of threads available to for OpenMP = 1 == reading 1 input volumes reading classifier array from /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_2020-01-02.gca reading input volume from norm.mgz average std[0] = 7.2 reading transform from transforms/talairach.m3z setting orig areas to linear transform determinant scaled 6.21 Atlas used for the 3D morph was /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/average/RB_all_2020-01-02.gca average std = 7.2 using min determinant for regularization = 5.2 0 singular and 0 ill-conditioned covariance matrices regularized labeling volume... renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.15521 (20) mri peak = 0.31458 ( 5) Left_Lateral_Ventricle (4): linear fit = 0.23 x + 0.0 (2810 voxels, overlap=0.005) Left_Lateral_Ventricle (4): linear fit = 0.40 x + 0.0 (2810 voxels, peak = 5), gca=8.0 gca peak = 0.20380 (13) mri peak = 0.35043 ( 5) Right_Lateral_Ventricle (43): linear fit = 0.31 x + 0.0 (3826 voxels, overlap=0.005) Right_Lateral_Ventricle (43): linear fit = 0.40 x + 0.0 (3826 voxels, peak = 4), gca=5.2 gca peak = 0.26283 (96) mri peak = 0.17545 (106) Right_Pallidum (52): linear fit = 1.11 x + 0.0 (675 voxels, overlap=0.021) Right_Pallidum (52): linear fit = 1.11 x + 0.0 (675 voxels, peak = 106), gca=106.1 gca peak = 0.15814 (97) mri peak = 0.18614 (107) Left_Pallidum (13): linear fit = 1.13 x + 0.0 (713 voxels, overlap=0.020) Left_Pallidum (13): linear fit = 1.13 x + 0.0 (713 voxels, peak = 110), gca=110.1 gca peak = 0.27624 (56) mri peak = 0.11429 (56) Right_Hippocampus (53): linear fit = 0.92 x + 0.0 (505 voxels, overlap=0.804) Right_Hippocampus (53): linear fit = 0.92 x + 0.0 (505 voxels, peak = 51), gca=51.2 gca peak = 0.28723 (59) mri peak = 0.10912 (55) Left_Hippocampus (17): linear fit = 0.92 x + 0.0 (700 voxels, overlap=1.006) Left_Hippocampus (17): linear fit = 0.92 x + 0.0 (700 voxels, peak = 54), gca=54.0 gca peak = 0.07623 (103) mri peak = 0.13041 (100) Right_Cerebral_White_Matter (41): linear fit = 0.99 x + 0.0 (32271 voxels, overlap=0.754) Right_Cerebral_White_Matter (41): linear fit = 0.99 x + 0.0 (32271 voxels, peak = 101), gca=101.5 gca peak = 0.07837 (105) mri peak = 0.12322 (100) Left_Cerebral_White_Matter (2): linear fit = 0.99 x + 0.0 (36213 voxels, overlap=0.800) Left_Cerebral_White_Matter (2): linear fit = 0.99 x + 0.0 (36213 voxels, peak = 103), gca=103.4 gca peak = 0.10165 (58) mri peak = 0.03177 (70) Left_Cerebral_Cortex (3): linear fit = 1.23 x + 0.0 (13363 voxels, overlap=0.169) Left_Cerebral_Cortex (3): linear fit = 1.23 x + 0.0 (13363 voxels, peak = 71), gca=71.1 gca peak = 0.11113 (58) mri peak = 0.02606 (54) Right_Cerebral_Cortex (42): linear fit = 1.29 x + 0.0 (15557 voxels, overlap=0.113) Right_Cerebral_Cortex (42): linear fit = 1.29 x + 0.0 (15557 voxels, peak = 75), gca=75.1 gca peak = 0.27796 (67) mri peak = 0.14951 (78) Right_Caudate (50): linear fit = 1.14 x + 0.0 (1221 voxels, overlap=0.234) Right_Caudate (50): linear fit = 1.14 x + 0.0 (1221 voxels, peak = 77), gca=76.7 gca peak = 0.14473 (69) mri peak = 0.13097 (80) Left_Caudate (11): linear fit = 1.04 x + 0.0 (1012 voxels, overlap=0.562) Left_Caudate (11): linear fit = 1.04 x + 0.0 (1012 voxels, peak = 72), gca=72.1 gca peak = 0.14301 (56) mri peak = 0.02956 (85) Left_Cerebellum_Cortex (8): linear fit = 1.50 x + 0.0 (15192 voxels, overlap=0.000) Left_Cerebellum_Cortex (8): linear fit = 1.50 x + 0.0 (15192 voxels, peak = 84), gca=84.3 gca peak = 0.14610 (55) mri peak = 0.03040 (86) Right_Cerebellum_Cortex (47): linear fit = 1.49 x + 0.0 (16996 voxels, overlap=0.001) Right_Cerebellum_Cortex (47): linear fit = 1.49 x + 0.0 (16996 voxels, peak = 82), gca=81.7 gca peak = 0.16309 (85) mri peak = 0.07461 (88) Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (2189 voxels, overlap=0.745) Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (2189 voxels, peak = 91), gca=90.5 gca peak = 0.15172 (84) mri peak = 0.08067 (89) Right_Cerebellum_White_Matter (46): linear fit = 1.07 x + 0.0 (1782 voxels, overlap=0.763) Right_Cerebellum_White_Matter (46): linear fit = 1.07 x + 0.0 (1782 voxels, peak = 89), gca=89.5 gca peak = 0.30461 (58) mri peak = 0.10976 (64) Left_Amygdala (18): linear fit = 1.09 x + 0.0 (239 voxels, overlap=1.032) Left_Amygdala (18): linear fit = 1.09 x + 0.0 (239 voxels, peak = 63), gca=62.9 gca peak = 0.32293 (57) mri peak = 0.09591 (63) Right_Amygdala (54): linear fit = 1.04 x + 0.0 (353 voxels, overlap=0.976) Right_Amygdala (54): linear fit = 1.04 x + 0.0 (353 voxels, peak = 60), gca=59.6 gca peak = 0.11083 (90) mri peak = 0.07475 (92) Left_Thalamus (10): linear fit = 1.01 x + 0.0 (3540 voxels, overlap=0.918) Left_Thalamus (10): linear fit = 1.01 x + 0.0 (3540 voxels, peak = 91), gca=91.3 gca peak = 0.11393 (83) mri peak = 0.08502 (87) Right_Thalamus (49): linear fit = 1.04 x + 0.0 (3391 voxels, overlap=0.725) Right_Thalamus (49): linear fit = 1.04 x + 0.0 (3391 voxels, peak = 87), gca=86.7 gca peak = 0.08575 (81) mri peak = 0.09686 (88) Left_Putamen (12): linear fit = 1.10 x + 0.0 (1466 voxels, overlap=0.498) Left_Putamen (12): linear fit = 1.10 x + 0.0 (1466 voxels, peak = 89), gca=88.7 gca peak = 0.08618 (78) mri peak = 0.09853 (87) Right_Putamen (51): linear fit = 1.08 x + 0.0 (1530 voxels, overlap=0.763) Right_Putamen (51): linear fit = 1.08 x + 0.0 (1530 voxels, peak = 84), gca=83.9 gca peak = 0.08005 (78) mri peak = 0.06659 (94) Brain_Stem (16): linear fit = 1.16 x + 0.0 (7812 voxels, overlap=0.493) Brain_Stem (16): linear fit = 1.16 x + 0.0 (7812 voxels, peak = 91), gca=90.9 gca peak = 0.12854 (88) mri peak = 0.08795 (105) Right_VentralDC (60): linear fit = 1.18 x + 0.0 (993 voxels, overlap=0.016) Right_VentralDC (60): linear fit = 1.18 x + 0.0 (993 voxels, peak = 104), gca=104.3 gca peak = 0.15703 (87) mri peak = 0.10287 (104) Left_VentralDC (28): linear fit = 1.20 x + 0.0 (1035 voxels, overlap=0.013) Left_VentralDC (28): linear fit = 1.20 x + 0.0 (1035 voxels, peak = 104), gca=104.0 gca peak = 0.17522 (25) mri peak = 0.64286 ( 5) gca peak = 0.17113 (14) mri peak = 0.20359 ( 4) Fourth_Ventricle (15): linear fit = 0.19 x + 0.0 (170 voxels, overlap=0.018) Fourth_Ventricle (15): linear fit = 0.19 x + 0.0 (170 voxels, peak = 3), gca=2.6 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.16627 (28) gca peak Left_Cerebellum_Cortex = 0.14301 (56) gca peak Third_Ventricle = 0.17522 (25) gca peak Fourth_Ventricle = 0.17113 (14) gca peak CSF = 0.20346 (36) gca peak Left_Accumbens_area = 0.70646 (62) gca peak Left_undetermined = 1.00000 (28) gca peak Left_vessel = 0.89917 (53) gca peak Left_choroid_plexus = 0.11689 (35) gca peak Right_Inf_Lat_Vent = 0.25504 (23) gca peak Right_Accumbens_area = 0.31650 (65) gca peak Right_vessel = 0.77268 (52) gca peak Right_choroid_plexus = 0.13275 (38) gca peak Fifth_Ventricle = 0.60973 (33) gca peak WM_hypointensities = 0.11013 (77) gca peak non_WM_hypointensities = 0.11354 (41) gca peak Optic_Chiasm = 0.51646 (76) not using caudate to estimate GM means setting label Left_Cerebellum_Cortex based on Right_Cerebellum_Cortex = 1.49 x + 0: 82 estimating mean gm scale to be 1.08 x + 0.0 estimating mean wm scale to be 0.99 x + 0.0 estimating mean csf scale to be 0.40 x + 0.0 Left_Pallidum too bright - rescaling by 0.903 (from 1.135) to 99.4 (was 110.1) Right_Pallidum too bright - rescaling by 0.937 (from 1.105) to 99.4 (was 106.1) saving intensity scales to aseg.auto_noCCseg.label_intensities.txt renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.31706 ( 7) mri peak = 0.31458 ( 5) Left_Lateral_Ventricle (4): linear fit = 0.60 x + 0.0 (2810 voxels, overlap=0.692) Left_Lateral_Ventricle (4): linear fit = 0.60 x + 0.0 (2810 voxels, peak = 4), gca=4.2 gca peak = 0.29334 ( 5) mri peak = 0.35043 ( 5) Right_Lateral_Ventricle (43): linear fit = 0.79 x + 0.0 (3826 voxels, overlap=0.655) Right_Lateral_Ventricle (43): linear fit = 0.79 x + 0.0 (3826 voxels, peak = 4), gca=3.9 gca peak = 0.21326 (96) mri peak = 0.17545 (106) Right_Pallidum (52): linear fit = 1.07 x + 0.0 (675 voxels, overlap=0.224) Right_Pallidum (52): linear fit = 1.07 x + 0.0 (675 voxels, peak = 102), gca=102.2 gca peak = 0.15758 (98) mri peak = 0.18614 (107) Left_Pallidum (13): linear fit = 1.11 x + 0.0 (713 voxels, overlap=0.012) Left_Pallidum (13): linear fit = 1.11 x + 0.0 (713 voxels, peak = 108), gca=108.3 gca peak = 0.32093 (51) mri peak = 0.11429 (56) Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (505 voxels, overlap=0.960) Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (505 voxels, peak = 51), gca=51.0 gca peak = 0.28838 (54) mri peak = 0.10912 (55) Left_Hippocampus (17): linear fit = 1.00 x + 0.0 (700 voxels, overlap=0.990) Left_Hippocampus (17): linear fit = 1.00 x + 0.0 (700 voxels, peak = 54), gca=54.0 gca peak = 0.08077 (102) mri peak = 0.13041 (100) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (32271 voxels, overlap=0.734) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (32271 voxels, peak = 101), gca=101.5 gca peak = 0.08345 (103) mri peak = 0.12322 (100) Left_Cerebral_White_Matter (2): linear fit = 0.99 x + 0.0 (36213 voxels, overlap=0.739) Left_Cerebral_White_Matter (2): linear fit = 0.99 x + 0.0 (36213 voxels, peak = 101), gca=101.5 gca peak = 0.08114 (71) mri peak = 0.03177 (70) Left_Cerebral_Cortex (3): linear fit = 0.98 x + 0.0 (13363 voxels, overlap=0.290) Left_Cerebral_Cortex (3): linear fit = 0.98 x + 0.0 (13363 voxels, peak = 69), gca=69.2 gca peak = 0.08689 (75) mri peak = 0.02606 (54) Right_Cerebral_Cortex (42): linear fit = 1.00 x + 0.0 (15557 voxels, overlap=0.127) Right_Cerebral_Cortex (42): linear fit = 1.00 x + 0.0 (15557 voxels, peak = 75), gca=75.0 gca peak = 0.21869 (76) mri peak = 0.14951 (78) Right_Caudate (50): linear fit = 1.00 x + 0.0 (1221 voxels, overlap=1.000) Right_Caudate (50): linear fit = 1.00 x + 0.0 (1221 voxels, peak = 76), gca=76.0 gca peak = 0.16511 (81) mri peak = 0.13097 (80) Left_Caudate (11): linear fit = 1.00 x + 0.0 (1012 voxels, overlap=0.841) Left_Caudate (11): linear fit = 1.00 x + 0.0 (1012 voxels, peak = 81), gca=81.0 gca peak = 0.09160 (82) mri peak = 0.02956 (85) Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (15192 voxels, overlap=0.270) Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (15192 voxels, peak = 82), gca=81.6 gca peak = 0.10481 (82) mri peak = 0.03040 (86) Right_Cerebellum_Cortex (47): linear fit = 0.99 x + 0.0 (16996 voxels, overlap=0.775) Right_Cerebellum_Cortex (47): linear fit = 0.99 x + 0.0 (16996 voxels, peak = 81), gca=80.8 gca peak = 0.16090 (90) mri peak = 0.07461 (88) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (2189 voxels, overlap=0.981) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (2189 voxels, peak = 90), gca=89.6 gca peak = 0.18294 (90) mri peak = 0.08067 (89) Right_Cerebellum_White_Matter (46): linear fit = 1.00 x + 0.0 (1782 voxels, overlap=0.987) Right_Cerebellum_White_Matter (46): linear fit = 1.00 x + 0.0 (1782 voxels, peak = 90), gca=89.6 gca peak = 0.30459 (63) mri peak = 0.10976 (64) Left_Amygdala (18): linear fit = 1.00 x + 0.0 (239 voxels, overlap=0.998) Left_Amygdala (18): linear fit = 1.00 x + 0.0 (239 voxels, peak = 63), gca=63.0 gca peak = 0.24308 (60) mri peak = 0.09591 (63) Right_Amygdala (54): linear fit = 1.02 x + 0.0 (353 voxels, overlap=0.949) Right_Amygdala (54): linear fit = 1.02 x + 0.0 (353 voxels, peak = 62), gca=61.5 gca peak = 0.10304 (90) mri peak = 0.07475 (92) Left_Thalamus (10): linear fit = 1.01 x + 0.0 (3540 voxels, overlap=0.941) Left_Thalamus (10): linear fit = 1.01 x + 0.0 (3540 voxels, peak = 91), gca=91.3 gca peak = 0.10042 (87) mri peak = 0.08502 (87) Right_Thalamus (49): linear fit = 1.02 x + 0.0 (3391 voxels, overlap=0.922) Right_Thalamus (49): linear fit = 1.02 x + 0.0 (3391 voxels, peak = 89), gca=89.2 gca peak = 0.07765 (89) mri peak = 0.09686 (88) Left_Putamen (12): linear fit = 1.00 x + 0.0 (1466 voxels, overlap=0.880) Left_Putamen (12): linear fit = 1.00 x + 0.0 (1466 voxels, peak = 89), gca=89.0 gca peak = 0.08528 (83) mri peak = 0.09853 (87) Right_Putamen (51): linear fit = 1.00 x + 0.0 (1530 voxels, overlap=0.974) Right_Putamen (51): linear fit = 1.00 x + 0.0 (1530 voxels, peak = 83), gca=83.0 gca peak = 0.07498 (91) mri peak = 0.06659 (94) Brain_Stem (16): linear fit = 1.00 x + 0.0 (7812 voxels, overlap=0.933) Brain_Stem (16): linear fit = 1.00 x + 0.0 (7812 voxels, peak = 91), gca=91.0 gca peak = 0.10011 (104) mri peak = 0.08795 (105) Right_VentralDC (60): linear fit = 1.00 x + 0.0 (993 voxels, overlap=0.802) Right_VentralDC (60): linear fit = 1.00 x + 0.0 (993 voxels, peak = 104), gca=104.0 gca peak = 0.15150 (102) mri peak = 0.10287 (104) Left_VentralDC (28): linear fit = 1.00 x + 0.0 (1035 voxels, overlap=0.847) Left_VentralDC (28): linear fit = 1.00 x + 0.0 (1035 voxels, peak = 103), gca=102.5 gca peak = 0.32031 (10) mri peak = 0.64286 ( 5) gca peak = 0.46125 ( 6) mri peak = 0.20359 ( 4) Fourth_Ventricle (15): linear fit = 0.49 x + 0.0 (170 voxels, overlap=0.256) Fourth_Ventricle (15): linear fit = 0.49 x + 0.0 (170 voxels, peak = 3), gca=2.9 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.17428 (26) gca peak Third_Ventricle = 0.32031 (10) gca peak Fourth_Ventricle = 0.46125 ( 6) gca peak CSF = 0.27810 (15) gca peak Left_Accumbens_area = 0.59932 (65) gca peak Left_undetermined = 1.00000 (28) gca peak Left_vessel = 0.89919 (53) gca peak Left_choroid_plexus = 0.11689 (35) gca peak Right_Inf_Lat_Vent = 0.25741 (21) gca peak Right_Accumbens_area = 0.37597 (75) gca peak Right_vessel = 0.78757 (52) gca peak Right_choroid_plexus = 0.13275 (38) gca peak Fifth_Ventricle = 0.92085 (13) gca peak WM_hypointensities = 0.11315 (75) gca peak non_WM_hypointensities = 0.14635 (40) gca peak Optic_Chiasm = 0.75211 (76) not using caudate to estimate GM means estimating mean gm scale to be 1.00 x + 0.0 estimating mean wm scale to be 0.99 x + 0.0 estimating mean csf scale to be 0.69 x + 0.0 Left_Pallidum too bright - rescaling by 0.909 (from 1.105) to 98.4 (was 108.3) Right_Pallidum too bright - rescaling by 0.963 (from 1.065) to 98.4 (was 102.2) saving intensity scales to aseg.auto_noCCseg.label_intensities.txt saving sequentially combined intensity scales to aseg.auto_noCCseg.label_intensities.txt 82108 voxels changed in iteration 0 of unlikely voxel relabeling 358 voxels changed in iteration 1 of unlikely voxel relabeling 69 voxels changed in iteration 2 of unlikely voxel relabeling 5 voxels changed in iteration 3 of unlikely voxel relabeling 0 voxels changed in iteration 4 of unlikely voxel relabeling 59137 gm and wm labels changed (%22 to gray, %78 to white out of all changed labels) 431 hippocampal voxels changed. 0 amygdala voxels changed. Reclassifying using Gibbs Priors pass 1: 79418 changed. image ll: -5.605, PF=0.500 pass 2: 23784 changed. image ll: -5.605, PF=0.500 pass 3: 7438 changed. pass 4: 2670 changed. 70476 voxels changed in iteration 0 of unlikely voxel relabeling 568 voxels changed in iteration 1 of unlikely voxel relabeling 68 voxels changed in iteration 2 of unlikely voxel relabeling 0 voxels changed in iteration 3 of unlikely voxel relabeling 8863 voxels changed in iteration 0 of unlikely voxel relabeling 118 voxels changed in iteration 1 of unlikely voxel relabeling 1 voxels changed in iteration 2 of unlikely voxel relabeling 0 voxels changed in iteration 3 of unlikely voxel relabeling 8258 voxels changed in iteration 0 of unlikely voxel relabeling 80 voxels changed in iteration 1 of unlikely voxel relabeling 3 voxels changed in iteration 2 of unlikely voxel relabeling 2 voxels changed in iteration 3 of unlikely voxel relabeling 0 voxels changed in iteration 4 of unlikely voxel relabeling 6749 voxels changed in iteration 0 of unlikely voxel relabeling 47 voxels changed in iteration 1 of unlikely voxel relabeling 7 voxels changed in iteration 2 of unlikely voxel relabeling 4 voxels changed in iteration 3 of unlikely voxel relabeling 0 voxels changed in iteration 4 of unlikely voxel relabeling !!!!!!!!! ventricle segment 0 with volume 9276 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 0 with volume 142 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 3 with volume 11882 above threshold 100 - not erasing !!!!!!!!!! writing labeled volume to aseg.auto_noCCseg.mgz mri_ca_label utimesec 2075.852622 mri_ca_label stimesec 1.520617 mri_ca_label ru_maxrss 2091260 mri_ca_label ru_ixrss 0 mri_ca_label ru_idrss 0 mri_ca_label ru_isrss 0 mri_ca_label ru_minflt 915891 mri_ca_label ru_majflt 7 mri_ca_label ru_nswap 0 mri_ca_label ru_inblock 1512 mri_ca_label ru_oublock 688 mri_ca_label ru_msgsnd 0 mri_ca_label ru_msgrcv 0 mri_ca_label ru_nsignals 0 mri_ca_label ru_nvcsw 70 mri_ca_label ru_nivcsw 29041 auto-labeling took 34 minutes and 41 seconds. @#@FSTIME 2022:02:17:08:24:41 mri_ca_label N 10 e 2081.26 S 1.58 U 2075.85 P 99% M 2091260 F 7 R 915894 W 0 c 29042 w 71 I 1512 O 696 L 1.03 1.05 1.06 @#@FSLOADPOST 2022:02:17:08:59:22 mri_ca_label N 10 1.02 1.04 1.02 #-------------------------------------- #@# CC Seg Thu Feb 17 08:59:22 EST 2022 mri_cc -aseg aseg.auto_noCCseg.mgz -o aseg.auto.mgz -lta /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/transforms/cc_up.lta sub-LALM61_ses-LALM61 will read input aseg from aseg.auto_noCCseg.mgz writing aseg with cc labels to aseg.auto.mgz will write lta as /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/transforms/cc_up.lta reading aseg from /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/aseg.auto_noCCseg.mgz reading norm from /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/norm.mgz MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab 27466 voxels in left wm, 48499 in right wm, xrange [124, 134] searching rotation angles z=[-12 2], y=[-7 7] searching scale 1 Z rot -11.9 searching scale 1 Z rot -11.6 searching scale 1 Z rot -11.4 searching scale 1 Z rot -11.1 searching scale 1 Z rot -10.9 searching scale 1 Z rot -10.6 searching scale 1 Z rot -10.4 searching scale 1 Z rot -10.1 searching scale 1 Z rot -9.9 searching scale 1 Z rot -9.6 searching scale 1 Z rot -9.4 searching scale 1 Z rot -9.1 searching scale 1 Z rot -8.9 searching scale 1 Z rot -8.6 searching scale 1 Z rot -8.4 searching scale 1 Z rot -8.1 searching scale 1 Z rot -7.9 searching scale 1 Z rot -7.6 searching scale 1 Z rot -7.4 searching scale 1 Z rot -7.1 searching scale 1 Z rot -6.9 searching scale 1 Z rot -6.6 searching scale 1 Z rot -6.4 searching scale 1 Z rot -6.1 searching scale 1 Z rot -5.9 searching scale 1 Z rot -5.6 searching scale 1 Z rot -5.4 searching scale 1 Z rot -5.1 searching scale 1 Z rot -4.9 searching scale 1 Z rot -4.6 searching scale 1 Z rot -4.4 searching scale 1 Z rot -4.1 searching scale 1 Z rot -3.9 searching scale 1 Z rot -3.6 searching scale 1 Z rot -3.4 searching scale 1 Z rot -3.1 searching scale 1 Z rot -2.9 searching scale 1 Z rot -2.6 searching scale 1 Z rot -2.4 searching scale 1 Z rot -2.1 searching scale 1 Z rot -1.9 searching scale 1 Z rot -1.6 searching scale 1 Z rot -1.4 searching scale 1 Z rot -1.1 searching scale 1 Z rot -0.9 searching scale 1 Z rot -0.6 searching scale 1 Z rot -0.4 searching scale 1 Z rot -0.1 searching scale 1 Z rot 0.1 searching scale 1 Z rot 0.4 searching scale 1 Z rot 0.6 searching scale 1 Z rot 0.9 searching scale 1 Z rot 1.1 searching scale 1 Z rot 1.4 searching scale 1 Z rot 1.6 global minimum found at slice 130.0, rotations (0.41, -5.13) final transformation (x=130.0, yr=0.410, zr=-5.130): 0.99597 0.08941 0.00713 -11.70869; -0.08941 0.99600 -0.00064 34.11512; -0.00715 0.00000 0.99997 22.93281; 0.00000 0.00000 0.00000 1.00000; updating x range to be [125, 130] in xformed coordinates best xformed slice 128 min_x_fornix = 146 min_x_fornix = 143 min_x_fornix = 139 min_x_fornix = 141 min_x_fornix = 140 cc center is found at 128 106 106 eigenvectors: 0.00015 -0.00931 0.99996; 0.08016 -0.99674 -0.00929; 0.99678 0.08016 0.00060; MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab error in mid anterior detected - correcting... writing aseg with callosum to /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/aseg.auto.mgz... corpus callosum segmentation took 0.7 minutes #VMPC# mri_cc VmPeak 450476 mri_cc done @#@FSTIME 2022:02:17:08:59:23 mri_cc N 7 e 40.77 S 0.06 U 40.54 P 99% M 344696 F 4 R 22383 W 0 c 490 w 49 I 680 O 672 L 1.02 1.04 1.02 @#@FSLOADPOST 2022:02:17:09:00:03 mri_cc N 7 1.01 1.04 1.01 #-------------------------------------- #@# Merge ASeg Thu Feb 17 09:00:03 EST 2022 cp aseg.auto.mgz aseg.presurf.mgz #-------------------------------------------- #@# Intensity Normalization2 Thu Feb 17 09:00:03 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_normalize -seed 1234 -mprage -aseg aseg.presurf.mgz -mask brainmask.mgz norm.mgz brain.mgz setting seed for random number genererator to 1234 assuming input volume is MGH (Van der Kouwe) MP-RAGE using segmentation for initial intensity normalization using MR volume brainmask.mgz to mask input volume... reading mri_src from norm.mgz... Reading aseg aseg.presurf.mgz normalizing image... NOT doing gentle normalization with control points/label processing with aseg MRIcopyHeader(): source has ctab removing outliers in the aseg WM... MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab 444 control points removed MRIcopyHeader(): source has ctab Building bias image building Voronoi diagram... performing soap bubble smoothing, sigma = 0... Smoothing with sigma 8 Applying bias correction building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Iterating 2 times --------------------------------- 3d normalization pass 1 of 2 white matter peak found at 110 white matter peak found at 110 gm peak at 81 (81), valley at 23 (23) csf peak at 10, setting threshold to 57 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... --------------------------------- 3d normalization pass 2 of 2 white matter peak found at 110 white matter peak found at 110 gm peak at 70 (70), valley at 23 (23) csf peak at 10, setting threshold to 50 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Done iterating --------------------------------- writing output to brain.mgz 3D bias adjustment took 2 minutes and 11 seconds. @#@FSTIME 2022:02:17:09:00:03 mri_normalize N 9 e 132.45 S 0.66 U 131.45 P 99% M 1240044 F 0 R 419186 W 0 c 1532 w 133 I 0 O 3208 L 1.01 1.04 1.01 @#@FSLOADPOST 2022:02:17:09:02:16 mri_normalize N 9 1.02 1.03 1.01 #-------------------------------------------- #@# Mask BFS Thu Feb 17 09:02:16 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_mask -T 5 brain.mgz brainmask.mgz brain.finalsurfs.mgz threshold mask volume at 5 DoAbs = 0 Found 2027907 voxels in mask (pct= 12.09) Writing masked volume to brain.finalsurfs.mgz...done. @#@FSTIME 2022:02:17:09:02:16 mri_mask N 5 e 1.18 S 0.00 U 1.08 P 92% M 73612 F 5 R 2918 W 0 c 34 w 92 I 624 O 3184 L 1.02 1.03 1.01 @#@FSLOADPOST 2022:02:17:09:02:17 mri_mask N 5 1.02 1.03 1.01 #-------------------------------------------- #@# WM Segmentation Thu Feb 17 09:02:17 EST 2022 AntsDenoiseImageFs -i brain.mgz -o antsdn.brain.mgz @#@FSTIME 2022:02:17:09:02:17 AntsDenoiseImageFs N 4 e 42.12 S 0.02 U 41.92 P 99% M 350664 F 17 R 6758 W 0 c 417 w 100 I 3264 O 3232 L 1.02 1.03 1.01 @#@FSLOADPOST 2022:02:17:09:02:59 AntsDenoiseImageFs N 4 1.07 1.04 1.01 mri_segment -wsizemm 13 -mprage antsdn.brain.mgz wm.seg.mgz wsizemm = 13, voxres = 1, wsize = 13 Widening wm low from 89 to 79 assuming input volume is MGH (Van der Kouwe) MP-RAGE wm mean: 110 wsize: 13 wm low: 79 wm hi: 125 gray low: 30 gray hi: 99 Doing initial trinary intensity segmentation Using local statistics to label ambiguous voxels Autodetecting stats Computing class statistics for intensity windows... CCS WM (103.0): 103.6 +- 5.9 [79.0 --> 125.0] CCS GM (71.0) : 69.8 +- 10.3 [30.0 --> 95.0] white_mean 103.627 white_sigma 5.88725 gray_mean 69.7631 gray_sigma 10.2693 setting bottom of white matter range wm_low to 80.0 setting top of gray matter range gray_hi to 90.3 wm_low 80.0324 wm_hi 125 gray_low 30 gray_hi 90.3016 Redoing initial intensity segmentation... Recomputing local statistics to label ambiguous voxels... wm_low 80.0324 wm_hi 125 gray_low 30 gray_hi 90.3016 using local geometry to label remaining ambiguous voxels... polvwsize = 5, polvlen = 3, gray_hi = 90.3016, wm_low = 80.0324 MRIcpolvMedianCurveSegment(): wsize=5, len=3, gmhi=90.3016, wmlow=80.0324 228784 voxels processed (1.36%) 110206 voxels white (0.66%) 118578 voxels non-white (0.71%) Reclassifying voxels using Gaussian border classifier niter=1 MRIreclassify(): wm_low=75.0324, gray_hi=90.3016, wsize=13 365787 voxels tested (2.18%) 92038 voxels changed (0.55%) 93888 multi-scale searches (0.56%) Recovering bright white MRIrecoverBrightWhite() wm_low 80.0324 wm_hi 125 slack 5.88725 pct_thresh 0.33 intensity_thresh 130.887 nvox_thresh 8.58 1423 voxels tested (0.01%) 1198 voxels changed (0.01%) removing voxels with positive offset direction... MRIremoveWrongDirection() wsize=3, lowthr=75.0324, hithr=90.3016 smoothing input volume with sigma = 0.250 157362 voxels tested (0.94%) 39484 voxels changed (0.24%) thicken = 1 removing 1-dimensional structures... MRIremove1dStructures(): max_iter=10000, thresh=2, WM_MIN_VAL=5 6411 sparsely connected voxels removed in 1 iterations thickening thin strands.... thickness 4 nsegments 20 wm_hi 125 6445 diagonally connected voxels added... MRIthickenThinWMStrands(): thickness=4, nsegments=20 20 segments, 2462 filled MRIfindBrightNonWM(): 3046 bright non-wm voxels segmented. MRIfilterMorphology() WM_MIN_VAL=5, DIAGONAL_FILL=230 white matter segmentation took 1.5 minutes writing output to wm.seg.mgz... @#@FSTIME 2022:02:17:09:02:59 mri_segment N 5 e 89.10 S 0.15 U 88.75 P 99% M 154156 F 5 R 91773 W 0 c 1132 w 58 I 784 O 1440 L 1.07 1.04 1.01 @#@FSLOADPOST 2022:02:17:09:04:29 mri_segment N 5 1.07 1.05 1.01 mri_edit_wm_with_aseg -keep-in wm.seg.mgz brain.mgz aseg.presurf.mgz wm.asegedit.mgz preserving editing changes in input volume... auto filling took 0.41 minutes reading wm segmentation from wm.seg.mgz... MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab 0 voxels added to wm to prevent paths from MTL structures to cortex 3218 additional wm voxels added 0 additional wm voxels added SEG EDIT: 51448 voxels turned on, 432037 voxels turned off. propagating editing to output volume from wm.seg.mgz writing edited volume to wm.asegedit.mgz.... @#@FSTIME 2022:02:17:09:04:29 mri_edit_wm_with_aseg N 5 e 24.55 S 0.07 U 24.34 P 99% M 461492 F 6 R 44065 W 0 c 303 w 64 I 1048 O 808 L 1.07 1.05 1.01 @#@FSLOADPOST 2022:02:17:09:04:53 mri_edit_wm_with_aseg N 5 1.05 1.05 1.01 mri_pretess wm.asegedit.mgz wm norm.mgz wm.mgz Iteration Number : 1 pass 1 (xy+): 65 found - 65 modified | TOTAL: 65 pass 2 (xy+): 0 found - 65 modified | TOTAL: 65 pass 1 (xy-): 51 found - 51 modified | TOTAL: 116 pass 2 (xy-): 0 found - 51 modified | TOTAL: 116 pass 1 (yz+): 69 found - 69 modified | TOTAL: 185 pass 2 (yz+): 0 found - 69 modified | TOTAL: 185 pass 1 (yz-): 53 found - 53 modified | TOTAL: 238 pass 2 (yz-): 0 found - 53 modified | TOTAL: 238 pass 1 (xz+): 48 found - 48 modified | TOTAL: 286 pass 2 (xz+): 0 found - 48 modified | TOTAL: 286 pass 1 (xz-): 39 found - 39 modified | TOTAL: 325 pass 2 (xz-): 0 found - 39 modified | TOTAL: 325 Iteration Number : 1 pass 1 (+++): 17 found - 17 modified | TOTAL: 17 pass 2 (+++): 0 found - 17 modified | TOTAL: 17 pass 1 (+++): 33 found - 33 modified | TOTAL: 50 pass 2 (+++): 0 found - 33 modified | TOTAL: 50 pass 1 (+++): 22 found - 22 modified | TOTAL: 72 pass 2 (+++): 0 found - 22 modified | TOTAL: 72 pass 1 (+++): 21 found - 21 modified | TOTAL: 93 pass 2 (+++): 0 found - 21 modified | TOTAL: 93 Iteration Number : 1 pass 1 (++): 45 found - 45 modified | TOTAL: 45 pass 2 (++): 0 found - 45 modified | TOTAL: 45 pass 1 (+-): 57 found - 57 modified | TOTAL: 102 pass 2 (+-): 0 found - 57 modified | TOTAL: 102 pass 1 (--): 58 found - 58 modified | TOTAL: 160 pass 2 (--): 1 found - 59 modified | TOTAL: 161 pass 3 (--): 0 found - 59 modified | TOTAL: 161 pass 1 (-+): 69 found - 69 modified | TOTAL: 230 pass 2 (-+): 0 found - 69 modified | TOTAL: 230 Iteration Number : 2 pass 1 (xy+): 6 found - 6 modified | TOTAL: 6 pass 2 (xy+): 0 found - 6 modified | TOTAL: 6 pass 1 (xy-): 4 found - 4 modified | TOTAL: 10 pass 2 (xy-): 0 found - 4 modified | TOTAL: 10 pass 1 (yz+): 6 found - 6 modified | TOTAL: 16 pass 2 (yz+): 0 found - 6 modified | TOTAL: 16 pass 1 (yz-): 3 found - 3 modified | TOTAL: 19 pass 2 (yz-): 0 found - 3 modified | TOTAL: 19 pass 1 (xz+): 6 found - 6 modified | TOTAL: 25 pass 2 (xz+): 0 found - 6 modified | TOTAL: 25 pass 1 (xz-): 4 found - 4 modified | TOTAL: 29 pass 2 (xz-): 0 found - 4 modified | TOTAL: 29 Iteration Number : 2 pass 1 (+++): 1 found - 1 modified | TOTAL: 1 pass 2 (+++): 0 found - 1 modified | TOTAL: 1 pass 1 (+++): 2 found - 2 modified | TOTAL: 3 pass 2 (+++): 0 found - 2 modified | TOTAL: 3 pass 1 (+++): 0 found - 0 modified | TOTAL: 3 pass 1 (+++): 0 found - 0 modified | TOTAL: 3 Iteration Number : 2 pass 1 (++): 1 found - 1 modified | TOTAL: 1 pass 2 (++): 0 found - 1 modified | TOTAL: 1 pass 1 (+-): 3 found - 3 modified | TOTAL: 4 pass 2 (+-): 0 found - 3 modified | TOTAL: 4 pass 1 (--): 4 found - 4 modified | TOTAL: 8 pass 2 (--): 0 found - 4 modified | TOTAL: 8 pass 1 (-+): 3 found - 3 modified | TOTAL: 11 pass 2 (-+): 0 found - 3 modified | TOTAL: 11 Iteration Number : 3 pass 1 (xy+): 1 found - 1 modified | TOTAL: 1 pass 2 (xy+): 0 found - 1 modified | TOTAL: 1 pass 1 (xy-): 0 found - 0 modified | TOTAL: 1 pass 1 (yz+): 0 found - 0 modified | TOTAL: 1 pass 1 (yz-): 0 found - 0 modified | TOTAL: 1 pass 1 (xz+): 1 found - 1 modified | TOTAL: 2 pass 2 (xz+): 0 found - 1 modified | TOTAL: 2 pass 1 (xz-): 0 found - 0 modified | TOTAL: 2 Iteration Number : 3 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 1 found - 1 modified | TOTAL: 1 pass 2 (--): 0 found - 1 modified | TOTAL: 1 pass 1 (-+): 0 found - 0 modified | TOTAL: 1 Iteration Number : 4 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 0 found - 0 modified | TOTAL: 0 pass 1 (yz+): 0 found - 0 modified | TOTAL: 0 pass 1 (yz-): 0 found - 0 modified | TOTAL: 0 pass 1 (xz+): 0 found - 0 modified | TOTAL: 0 pass 1 (xz-): 0 found - 0 modified | TOTAL: 0 Iteration Number : 4 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 4 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 Total Number of Modified Voxels = 694 (out of 564070: 0.123034) binarizing input wm segmentation... Ambiguous edge configurations... mri_pretess done @#@FSTIME 2022:02:17:09:04:53 mri_pretess N 4 e 2.91 S 0.01 U 2.85 P 98% M 56752 F 1 R 2207 W 0 c 53 w 30 I 128 O 808 L 1.05 1.05 1.01 @#@FSLOADPOST 2022:02:17:09:04:56 mri_pretess N 4 1.04 1.05 1.01 #-------------------------------------------- #@# Fill Thu Feb 17 09:04:56 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_fill -a ../scripts/ponscc.cut.log -xform transforms/talairach.lta -segmentation aseg.presurf.mgz -ctab /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/SubCorticalMassLUT.txt wm.mgz filled.mgz logging cutting plane coordinates to ../scripts/ponscc.cut.log... INFO: Using transforms/talairach.lta and its offset for Talairach volume ... using segmentation aseg.presurf.mgz... reading input volume...done. searching for cutting planes...voxel to talairach voxel transform 1.11214 0.08582 0.01111 -27.76877; -0.09969 1.16824 0.04936 -16.93605; -0.00547 -0.04047 0.98396 -5.00893; 0.00000 0.00000 0.00000 1.00000; voxel to talairach voxel transform 1.11214 0.08582 0.01111 -27.76877; -0.09969 1.16824 0.04936 -16.93605; -0.00547 -0.04047 0.98396 -5.00893; 0.00000 0.00000 0.00000 1.00000; reading segmented volume aseg.presurf.mgz removing CC from segmentation MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab Looking for area (min, max) = (350, 1400) area[0] = 1638 (min = 350, max = 1400), aspect = 0.55 (min = 0.10, max = 0.75) need search nearby using seed (127, 108, 93), TAL = (1.0, -35.0, 20.0) talairach voxel to voxel transform 0.89323 -0.06585 -0.00678 23.65462; 0.07588 0.84891 -0.04345 16.26667; 0.00809 0.03455 1.01448 5.89120; 0.00000 0.00000 0.00000 1.00000; segmentation indicates cc at (127, 108, 93) --> (1.0, -35.0, 20.0) done. filling took 1.0 minutes talairach cc position changed to (1.00, -35.00, 20.00) Erasing brainstem...done. seed_search_size = 9, min_neighbors = 5 search rh wm seed point around talairach space:(19.00, -35.00, 20.00) SRC: (113.27, 112.18, 104.85) search lh wm seed point around talairach space (-17.00, -35.00, 20.00), SRC: (145.43, 114.91, 105.14) compute mri_fill using aseg Erasing Brain Stem and Cerebellum ... Define left and right masks using aseg: Building Voronoi diagram ... Using the Voronoi diagram for separating WM into two hemispheres ... Find the largest connected component for each hemisphere ... Embedding colortable mri_fill done, writing output to filled.mgz... @#@FSTIME 2022:02:17:09:04:56 mri_fill N 10 e 61.70 S 0.52 U 60.99 P 99% M 982416 F 5 R 307196 W 0 c 757 w 43 I 1008 O 272 L 1.04 1.05 1.01 @#@FSLOADPOST 2022:02:17:09:05:58 mri_fill N 10 1.01 1.04 1.00 cp filled.mgz filled.auto.mgz #-------------------------------------------- #@# Tessellate lh Thu Feb 17 09:05:58 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mri_pretess ../mri/filled.mgz 255 ../mri/norm.mgz ../mri/filled-pretess255.mgz Iteration Number : 1 pass 1 (xy+): 5 found - 5 modified | TOTAL: 5 pass 2 (xy+): 0 found - 5 modified | TOTAL: 5 pass 1 (xy-): 6 found - 6 modified | TOTAL: 11 pass 2 (xy-): 0 found - 6 modified | TOTAL: 11 pass 1 (yz+): 7 found - 7 modified | TOTAL: 18 pass 2 (yz+): 0 found - 7 modified | TOTAL: 18 pass 1 (yz-): 8 found - 8 modified | TOTAL: 26 pass 2 (yz-): 0 found - 8 modified | TOTAL: 26 pass 1 (xz+): 4 found - 4 modified | TOTAL: 30 pass 2 (xz+): 0 found - 4 modified | TOTAL: 30 pass 1 (xz-): 3 found - 3 modified | TOTAL: 33 pass 2 (xz-): 0 found - 3 modified | TOTAL: 33 Iteration Number : 1 pass 1 (+++): 2 found - 2 modified | TOTAL: 2 pass 2 (+++): 0 found - 2 modified | TOTAL: 2 pass 1 (+++): 0 found - 0 modified | TOTAL: 2 pass 1 (+++): 0 found - 0 modified | TOTAL: 2 pass 1 (+++): 2 found - 2 modified | TOTAL: 4 pass 2 (+++): 0 found - 2 modified | TOTAL: 4 Iteration Number : 1 pass 1 (++): 1 found - 1 modified | TOTAL: 1 pass 2 (++): 0 found - 1 modified | TOTAL: 1 pass 1 (+-): 4 found - 4 modified | TOTAL: 5 pass 2 (+-): 0 found - 4 modified | TOTAL: 5 pass 1 (--): 1 found - 1 modified | TOTAL: 6 pass 2 (--): 0 found - 1 modified | TOTAL: 6 pass 1 (-+): 1 found - 1 modified | TOTAL: 7 pass 2 (-+): 0 found - 1 modified | TOTAL: 7 Iteration Number : 2 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 0 found - 0 modified | TOTAL: 0 pass 1 (yz+): 0 found - 0 modified | TOTAL: 0 pass 1 (yz-): 0 found - 0 modified | TOTAL: 0 pass 1 (xz+): 1 found - 1 modified | TOTAL: 1 pass 2 (xz+): 0 found - 1 modified | TOTAL: 1 pass 1 (xz-): 0 found - 0 modified | TOTAL: 1 Iteration Number : 2 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 2 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 0 found - 0 modified | TOTAL: 0 pass 1 (yz+): 0 found - 0 modified | TOTAL: 0 pass 1 (yz-): 0 found - 0 modified | TOTAL: 0 pass 1 (xz+): 0 found - 0 modified | TOTAL: 0 pass 1 (xz-): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 Total Number of Modified Voxels = 45 (out of 264703: 0.017000) Ambiguous edge configurations... mri_pretess done @#@FSTIME 2022:02:17:09:05:58 mri_pretess N 4 e 1.89 S 0.00 U 1.82 P 96% M 40240 F 0 R 1648 W 0 c 38 w 45 I 0 O 256 L 1.01 1.04 1.00 @#@FSLOADPOST 2022:02:17:09:06:00 mri_pretess N 4 1.01 1.04 1.00 mri_tessellate ../mri/filled-pretess255.mgz 255 ../surf/lh.orig.nofix 7.2.0 7.2.0 slice 50: 1205 vertices, 1305 faces slice 60: 6966 vertices, 7201 faces slice 70: 15977 vertices, 16350 faces slice 80: 25837 vertices, 26171 faces slice 90: 35842 vertices, 36162 faces slice 100: 46324 vertices, 46697 faces slice 110: 58815 vertices, 59230 faces slice 120: 70657 vertices, 71066 faces slice 130: 81690 vertices, 82084 faces slice 140: 91831 vertices, 92171 faces slice 150: 101078 vertices, 101389 faces slice 160: 108438 vertices, 108729 faces slice 170: 116143 vertices, 116437 faces slice 180: 123125 vertices, 123381 faces slice 190: 128925 vertices, 129143 faces slice 200: 133006 vertices, 133151 faces slice 210: 134110 vertices, 134140 faces slice 220: 134110 vertices, 134140 faces slice 230: 134110 vertices, 134140 faces slice 240: 134110 vertices, 134140 faces slice 250: 134110 vertices, 134140 faces using the conformed surface RAS to save vertex points... writing ../surf/lh.orig.nofix using vox2ras matrix: -1.00000 0.00000 0.00000 128.00000; 0.00000 0.00000 1.00000 -128.00000; 0.00000 -1.00000 0.00000 128.00000; 0.00000 0.00000 0.00000 1.00000; @#@FSTIME 2022:02:17:09:06:00 mri_tessellate N 3 e 1.25 S 0.00 U 1.16 P 93% M 39356 F 0 R 2793 W 0 c 18 w 178 I 0 O 6296 L 1.01 1.04 1.00 @#@FSLOADPOST 2022:02:17:09:06:01 mri_tessellate N 3 1.01 1.04 1.00 rm -f ../mri/filled-pretess255.mgz mris_extract_main_component ../surf/lh.orig.nofix ../surf/lh.orig.nofix counting number of connected components... 134110 voxel in cpt #1: X=-30 [v=134110,e=402420,f=268280] located at (-27.053934, -9.435195, 10.642480) For the whole surface: X=-30 [v=134110,e=402420,f=268280] One single component has been found nothing to do done @#@FSTIME 2022:02:17:09:06:01 mris_extract_main_component N 2 e 0.76 S 0.04 U 0.59 P 82% M 266916 F 1 R 27543 W 0 c 15 w 221 I 6400 O 9440 L 1.01 1.04 1.00 @#@FSLOADPOST 2022:02:17:09:06:02 mris_extract_main_component N 2 1.01 1.04 1.00 #-------------------------------------------- #@# Tessellate rh Thu Feb 17 09:06:02 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mri_pretess ../mri/filled.mgz 127 ../mri/norm.mgz ../mri/filled-pretess127.mgz Iteration Number : 1 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 4 found - 4 modified | TOTAL: 4 pass 2 (xy-): 0 found - 4 modified | TOTAL: 4 pass 1 (yz+): 6 found - 6 modified | TOTAL: 10 pass 2 (yz+): 0 found - 6 modified | TOTAL: 10 pass 1 (yz-): 7 found - 7 modified | TOTAL: 17 pass 2 (yz-): 0 found - 7 modified | TOTAL: 17 pass 1 (xz+): 6 found - 6 modified | TOTAL: 23 pass 2 (xz+): 0 found - 6 modified | TOTAL: 23 pass 1 (xz-): 1 found - 1 modified | TOTAL: 24 pass 2 (xz-): 0 found - 1 modified | TOTAL: 24 Iteration Number : 1 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 1 pass 1 (++): 2 found - 2 modified | TOTAL: 2 pass 2 (++): 0 found - 2 modified | TOTAL: 2 pass 1 (+-): 3 found - 3 modified | TOTAL: 5 pass 2 (+-): 0 found - 3 modified | TOTAL: 5 pass 1 (--): 1 found - 1 modified | TOTAL: 6 pass 2 (--): 0 found - 1 modified | TOTAL: 6 pass 1 (-+): 0 found - 0 modified | TOTAL: 6 Iteration Number : 2 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 0 found - 0 modified | TOTAL: 0 pass 1 (yz+): 1 found - 1 modified | TOTAL: 1 pass 2 (yz+): 0 found - 1 modified | TOTAL: 1 pass 1 (yz-): 0 found - 0 modified | TOTAL: 1 pass 1 (xz+): 0 found - 0 modified | TOTAL: 1 pass 1 (xz-): 0 found - 0 modified | TOTAL: 1 Iteration Number : 2 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 2 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 0 found - 0 modified | TOTAL: 0 pass 1 (yz+): 0 found - 0 modified | TOTAL: 0 pass 1 (yz-): 0 found - 0 modified | TOTAL: 0 pass 1 (xz+): 0 found - 0 modified | TOTAL: 0 pass 1 (xz-): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 Total Number of Modified Voxels = 31 (out of 276973: 0.011192) Ambiguous edge configurations... mri_pretess done @#@FSTIME 2022:02:17:09:06:02 mri_pretess N 4 e 1.79 S 0.00 U 1.74 P 97% M 40256 F 0 R 1647 W 0 c 41 w 23 I 0 O 256 L 1.01 1.04 1.00 @#@FSLOADPOST 2022:02:17:09:06:04 mri_pretess N 4 1.01 1.04 1.00 mri_tessellate ../mri/filled-pretess127.mgz 127 ../surf/rh.orig.nofix 7.2.0 7.2.0 slice 50: 1999 vertices, 2168 faces slice 60: 8783 vertices, 9030 faces slice 70: 17859 vertices, 18204 faces slice 80: 28045 vertices, 28405 faces slice 90: 38776 vertices, 39163 faces slice 100: 49542 vertices, 49911 faces slice 110: 61281 vertices, 61665 faces slice 120: 73125 vertices, 73534 faces slice 130: 84868 vertices, 85300 faces slice 140: 95314 vertices, 95711 faces slice 150: 104628 vertices, 104978 faces slice 160: 111766 vertices, 112060 faces slice 170: 119174 vertices, 119494 faces slice 180: 125788 vertices, 126049 faces slice 190: 131698 vertices, 131917 faces slice 200: 135906 vertices, 136063 faces slice 210: 137068 vertices, 137112 faces slice 220: 137068 vertices, 137112 faces slice 230: 137068 vertices, 137112 faces slice 240: 137068 vertices, 137112 faces slice 250: 137068 vertices, 137112 faces using the conformed surface RAS to save vertex points... writing ../surf/rh.orig.nofix using vox2ras matrix: -1.00000 0.00000 0.00000 128.00000; 0.00000 0.00000 1.00000 -128.00000; 0.00000 -1.00000 0.00000 128.00000; 0.00000 0.00000 0.00000 1.00000; @#@FSTIME 2022:02:17:09:06:04 mri_tessellate N 3 e 1.24 S 0.00 U 1.16 P 94% M 38696 F 0 R 1985 W 0 c 22 w 173 I 0 O 6432 L 1.01 1.04 1.00 @#@FSLOADPOST 2022:02:17:09:06:05 mri_tessellate N 3 1.01 1.03 1.00 rm -f ../mri/filled-pretess127.mgz mris_extract_main_component ../surf/rh.orig.nofix ../surf/rh.orig.nofix counting number of connected components... 137056 voxel in cpt #1: X=-46 [v=137056,e=411306,f=274204] located at (25.372110, -10.718990, 15.519167) 12 voxel in cpt #2: X=2 [v=12,e=30,f=20] located at (3.000000, 8.500000, 4.000000) For the whole surface: X=-44 [v=137068,e=411336,f=274224] 2 components have been found keeping component #1 with 137056 vertices done @#@FSTIME 2022:02:17:09:06:05 mris_extract_main_component N 2 e 0.73 S 0.04 U 0.61 P 90% M 272604 F 0 R 27535 W 0 c 23 w 213 I 0 O 9640 L 1.01 1.03 1.00 @#@FSLOADPOST 2022:02:17:09:06:06 mris_extract_main_component N 2 1.01 1.03 1.00 #-------------------------------------------- #@# Smooth1 lh Thu Feb 17 09:06:06 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_smooth -nw -seed 1234 ../surf/lh.orig.nofix ../surf/lh.smoothwm.nofix setting seed for random number generator to 1234 smoothing surface tessellation for 10 iterations... smoothing complete - recomputing first and second fundamental forms... @#@FSTIME 2022:02:17:09:06:06 mris_smooth N 5 e 2.44 S 0.06 U 2.24 P 94% M 212616 F 0 R 39728 W 0 c 30 w 263 I 9440 O 9440 L 1.01 1.03 1.00 @#@FSLOADPOST 2022:02:17:09:06:09 mris_smooth N 5 1.01 1.03 1.00 #-------------------------------------------- #@# Smooth1 rh Thu Feb 17 09:06:09 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_smooth -nw -seed 1234 ../surf/rh.orig.nofix ../surf/rh.smoothwm.nofix setting seed for random number generator to 1234 smoothing surface tessellation for 10 iterations... smoothing complete - recomputing first and second fundamental forms... @#@FSTIME 2022:02:17:09:06:09 mris_smooth N 5 e 2.48 S 0.05 U 2.28 P 94% M 217012 F 0 R 40059 W 0 c 23 w 289 I 9640 O 9648 L 1.01 1.03 1.00 @#@FSLOADPOST 2022:02:17:09:06:11 mris_smooth N 5 1.01 1.03 1.00 #-------------------------------------------- #@# Inflation1 lh Thu Feb 17 09:06:11 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_inflate -no-save-sulc ../surf/lh.smoothwm.nofix ../surf/lh.inflated.nofix Not saving sulc Reading ../surf/lh.smoothwm.nofix avg radius = 46.2 mm, total surface area = 70100 mm^2 step 000: RMS=0.155 (target=0.015) step 005: RMS=0.116 (target=0.015) step 010: RMS=0.087 (target=0.015) step 015: RMS=0.074 (target=0.015) step 020: RMS=0.064 (target=0.015) step 025: RMS=0.059 (target=0.015) step 030: RMS=0.055 (target=0.015) step 035: RMS=0.050 (target=0.015) step 040: RMS=0.049 (target=0.015) step 045: RMS=0.048 (target=0.015) step 050: RMS=0.047 (target=0.015) step 055: RMS=0.047 (target=0.015) step 060: RMS=0.048 (target=0.015) writing inflated surface to ../surf/lh.inflated.nofix inflation took 0.3 minutes inflation complete. Not saving sulc mris_inflate utimesec 14.815335 mris_inflate stimesec 0.533473 mris_inflate ru_maxrss 216576 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 465137 mris_inflate ru_majflt 1 mris_inflate ru_nswap 0 mris_inflate ru_inblock 144 mris_inflate ru_oublock 9440 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 224 mris_inflate ru_nivcsw 254 @#@FSTIME 2022:02:17:09:06:11 mris_inflate N 3 e 15.48 S 0.53 U 14.81 P 99% M 216576 F 1 R 465140 W 0 c 254 w 225 I 144 O 9440 L 1.01 1.03 1.00 @#@FSLOADPOST 2022:02:17:09:06:27 mris_inflate N 3 1.01 1.03 1.00 #-------------------------------------------- #@# Inflation1 rh Thu Feb 17 09:06:27 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_inflate -no-save-sulc ../surf/rh.smoothwm.nofix ../surf/rh.inflated.nofix Not saving sulc Reading ../surf/rh.smoothwm.nofix avg radius = 46.7 mm, total surface area = 72540 mm^2 step 000: RMS=0.154 (target=0.015) step 005: RMS=0.115 (target=0.015) step 010: RMS=0.085 (target=0.015) step 015: RMS=0.074 (target=0.015) step 020: RMS=0.064 (target=0.015) step 025: RMS=0.058 (target=0.015) step 030: RMS=0.054 (target=0.015) step 035: RMS=0.050 (target=0.015) step 040: RMS=0.048 (target=0.015) step 045: RMS=0.046 (target=0.015) step 050: RMS=0.046 (target=0.015) step 055: RMS=0.046 (target=0.015) step 060: RMS=0.047 (target=0.015) writing inflated surface to ../surf/rh.inflated.nofix inflation took 0.3 minutes inflation complete. Not saving sulc mris_inflate utimesec 15.168123 mris_inflate stimesec 0.507210 mris_inflate ru_maxrss 221068 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 398117 mris_inflate ru_majflt 0 mris_inflate ru_nswap 0 mris_inflate ru_inblock 0 mris_inflate ru_oublock 9648 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 234 mris_inflate ru_nivcsw 137 @#@FSTIME 2022:02:17:09:06:27 mris_inflate N 3 e 15.80 S 0.51 U 15.16 P 99% M 221068 F 0 R 398119 W 0 c 137 w 235 I 0 O 9656 L 1.01 1.03 1.00 @#@FSLOADPOST 2022:02:17:09:06:43 mris_inflate N 3 1.00 1.03 1.00 #-------------------------------------------- #@# QSphere lh Thu Feb 17 09:06:43 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_sphere -q -p 6 -a 128 -seed 1234 ../surf/lh.inflated.nofix ../surf/lh.qsphere.nofix doing quick spherical unfolding. limitting unfolding to 6 passes using n_averages = 128 setting seed for random number genererator to 1234 version: 7.2.0 available threads: 1 scaling brain by 0.312... inflating... projecting onto sphere... surface projected - minimizing metric distortion... vertex spacing 0.99 +- 0.59 (0.00-->6.57) (max @ vno 44900 --> 45927) face area 0.03 +- 0.03 (-0.24-->0.63) Entering MRISinflateToSphere() inflating to sphere (rms error < 2.00) 000: dt: 0.0000, rms radial error=176.894, avgs=0 005/300: dt: 0.9000, rms radial error=176.633, avgs=0 010/300: dt: 0.9000, rms radial error=176.073, avgs=0 015/300: dt: 0.9000, rms radial error=175.338, avgs=0 020/300: dt: 0.9000, rms radial error=174.509, avgs=0 025/300: dt: 0.9000, rms radial error=173.623, avgs=0 030/300: dt: 0.9000, rms radial error=172.703, avgs=0 035/300: dt: 0.9000, rms radial error=171.767, avgs=0 040/300: dt: 0.9000, rms radial error=170.823, avgs=0 045/300: dt: 0.9000, rms radial error=169.878, avgs=0 050/300: dt: 0.9000, rms radial error=168.933, avgs=0 055/300: dt: 0.9000, rms radial error=167.991, avgs=0 060/300: dt: 0.9000, rms radial error=167.053, avgs=0 065/300: dt: 0.9000, rms radial error=166.120, avgs=0 070/300: dt: 0.9000, rms radial error=165.191, avgs=0 075/300: dt: 0.9000, rms radial error=164.267, avgs=0 080/300: dt: 0.9000, rms radial error=163.348, avgs=0 085/300: dt: 0.9000, rms radial error=162.433, avgs=0 090/300: dt: 0.9000, rms radial error=161.524, avgs=0 095/300: dt: 0.9000, rms radial error=160.619, avgs=0 100/300: dt: 0.9000, rms radial error=159.719, avgs=0 105/300: dt: 0.9000, rms radial error=158.824, avgs=0 110/300: dt: 0.9000, rms radial error=157.933, avgs=0 115/300: dt: 0.9000, rms radial error=157.048, avgs=0 120/300: dt: 0.9000, rms radial error=156.167, avgs=0 125/300: dt: 0.9000, rms radial error=155.291, avgs=0 130/300: dt: 0.9000, rms radial error=154.420, avgs=0 135/300: dt: 0.9000, rms radial error=153.553, avgs=0 140/300: dt: 0.9000, rms radial error=152.691, avgs=0 145/300: dt: 0.9000, rms radial error=151.834, avgs=0 150/300: dt: 0.9000, rms radial error=150.981, avgs=0 155/300: dt: 0.9000, rms radial error=150.134, avgs=0 160/300: dt: 0.9000, rms radial error=149.292, avgs=0 165/300: dt: 0.9000, rms radial error=148.454, avgs=0 170/300: dt: 0.9000, rms radial error=147.621, avgs=0 175/300: dt: 0.9000, rms radial error=146.793, avgs=0 180/300: dt: 0.9000, rms radial error=145.969, avgs=0 185/300: dt: 0.9000, rms radial error=145.149, avgs=0 190/300: dt: 0.9000, rms radial error=144.334, avgs=0 195/300: dt: 0.9000, rms radial error=143.524, avgs=0 200/300: dt: 0.9000, rms radial error=142.718, avgs=0 205/300: dt: 0.9000, rms radial error=141.916, avgs=0 210/300: dt: 0.9000, rms radial error=141.119, avgs=0 215/300: dt: 0.9000, rms radial error=140.326, avgs=0 220/300: dt: 0.9000, rms radial error=139.538, avgs=0 225/300: dt: 0.9000, rms radial error=138.754, avgs=0 230/300: dt: 0.9000, rms radial error=137.974, avgs=0 235/300: dt: 0.9000, rms radial error=137.198, avgs=0 240/300: dt: 0.9000, rms radial error=136.427, avgs=0 245/300: dt: 0.9000, rms radial error=135.660, avgs=0 250/300: dt: 0.9000, rms radial error=134.898, avgs=0 255/300: dt: 0.9000, rms radial error=134.140, avgs=0 260/300: dt: 0.9000, rms radial error=133.386, avgs=0 265/300: dt: 0.9000, rms radial error=132.636, avgs=0 270/300: dt: 0.9000, rms radial error=131.890, avgs=0 275/300: dt: 0.9000, rms radial error=131.149, avgs=0 280/300: dt: 0.9000, rms radial error=130.411, avgs=0 285/300: dt: 0.9000, rms radial error=129.678, avgs=0 290/300: dt: 0.9000, rms radial error=128.949, avgs=0 295/300: dt: 0.9000, rms radial error=128.224, avgs=0 300/300: dt: 0.9000, rms radial error=127.503, avgs=0 spherical inflation complete. epoch 1 (K=10.0), pass 1, starting sse = 15721.11 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00036 epoch 2 (K=40.0), pass 1, starting sse = 2606.09 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.01/13 = 0.00039 epoch 3 (K=160.0), pass 1, starting sse = 257.49 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.07/14 = 0.00473 epoch 4 (K=640.0), pass 1, starting sse = 19.09 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.08/17 = 0.00471 final distance error %100000.00 writing spherical brain to ../surf/lh.qsphere.nofix spherical transformation took 0.0278 hours FSRUNTIME@ mris_sphere 0.0278 hours 1 threads #VMPC# mris_sphere VmPeak 501048 mris_sphere done @#@FSTIME 2022:02:17:09:06:43 mris_sphere N 9 e 99.95 S 2.90 U 96.72 P 99% M 221836 F 0 R 2360748 W 0 c 1887 w 243 I 0 O 9440 L 1.00 1.03 1.00 @#@FSLOADPOST 2022:02:17:09:08:23 mris_sphere N 9 1.12 1.05 1.01 #-------------------------------------------- #@# QSphere rh Thu Feb 17 09:08:23 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_sphere -q -p 6 -a 128 -seed 1234 ../surf/rh.inflated.nofix ../surf/rh.qsphere.nofix doing quick spherical unfolding. limitting unfolding to 6 passes using n_averages = 128 setting seed for random number genererator to 1234 version: 7.2.0 available threads: 1 scaling brain by 0.307... inflating... projecting onto sphere... surface projected - minimizing metric distortion... vertex spacing 0.98 +- 0.58 (0.00-->6.79) (max @ vno 99093 --> 99108) face area 0.02 +- 0.03 (-0.17-->0.67) Entering MRISinflateToSphere() inflating to sphere (rms error < 2.00) 000: dt: 0.0000, rms radial error=176.896, avgs=0 005/300: dt: 0.9000, rms radial error=176.635, avgs=0 010/300: dt: 0.9000, rms radial error=176.076, avgs=0 015/300: dt: 0.9000, rms radial error=175.343, avgs=0 020/300: dt: 0.9000, rms radial error=174.507, avgs=0 025/300: dt: 0.9000, rms radial error=173.613, avgs=0 030/300: dt: 0.9000, rms radial error=172.687, avgs=0 035/300: dt: 0.9000, rms radial error=171.745, avgs=0 040/300: dt: 0.9000, rms radial error=170.794, avgs=0 045/300: dt: 0.9000, rms radial error=169.842, avgs=0 050/300: dt: 0.9000, rms radial error=168.890, avgs=0 055/300: dt: 0.9000, rms radial error=167.940, avgs=0 060/300: dt: 0.9000, rms radial error=166.994, avgs=0 065/300: dt: 0.9000, rms radial error=166.053, avgs=0 070/300: dt: 0.9000, rms radial error=165.116, avgs=0 075/300: dt: 0.9000, rms radial error=164.183, avgs=0 080/300: dt: 0.9000, rms radial error=163.256, avgs=0 085/300: dt: 0.9000, rms radial error=162.340, avgs=0 090/300: dt: 0.9000, rms radial error=161.430, avgs=0 095/300: dt: 0.9000, rms radial error=160.525, avgs=0 100/300: dt: 0.9000, rms radial error=159.625, avgs=0 105/300: dt: 0.9000, rms radial error=158.730, avgs=0 110/300: dt: 0.9000, rms radial error=157.840, avgs=0 115/300: dt: 0.9000, rms radial error=156.954, avgs=0 120/300: dt: 0.9000, rms radial error=156.074, avgs=0 125/300: dt: 0.9000, rms radial error=155.198, avgs=0 130/300: dt: 0.9000, rms radial error=154.326, avgs=0 135/300: dt: 0.9000, rms radial error=153.460, avgs=0 140/300: dt: 0.9000, rms radial error=152.599, avgs=0 145/300: dt: 0.9000, rms radial error=151.742, avgs=0 150/300: dt: 0.9000, rms radial error=150.891, avgs=0 155/300: dt: 0.9000, rms radial error=150.043, avgs=0 160/300: dt: 0.9000, rms radial error=149.201, avgs=0 165/300: dt: 0.9000, rms radial error=148.363, avgs=0 170/300: dt: 0.9000, rms radial error=147.529, avgs=0 175/300: dt: 0.9000, rms radial error=146.700, avgs=0 180/300: dt: 0.9000, rms radial error=145.876, avgs=0 185/300: dt: 0.9000, rms radial error=145.056, avgs=0 190/300: dt: 0.9000, rms radial error=144.241, avgs=0 195/300: dt: 0.9000, rms radial error=143.431, avgs=0 200/300: dt: 0.9000, rms radial error=142.626, avgs=0 205/300: dt: 0.9000, rms radial error=141.825, avgs=0 210/300: dt: 0.9000, rms radial error=141.028, avgs=0 215/300: dt: 0.9000, rms radial error=140.236, avgs=0 220/300: dt: 0.9000, rms radial error=139.448, avgs=0 225/300: dt: 0.9000, rms radial error=138.664, avgs=0 230/300: dt: 0.9000, rms radial error=137.885, avgs=0 235/300: dt: 0.9000, rms radial error=137.110, avgs=0 240/300: dt: 0.9000, rms radial error=136.339, avgs=0 245/300: dt: 0.9000, rms radial error=135.573, avgs=0 250/300: dt: 0.9000, rms radial error=134.811, avgs=0 255/300: dt: 0.9000, rms radial error=134.053, avgs=0 260/300: dt: 0.9000, rms radial error=133.299, avgs=0 265/300: dt: 0.9000, rms radial error=132.550, avgs=0 270/300: dt: 0.9000, rms radial error=131.805, avgs=0 275/300: dt: 0.9000, rms radial error=131.064, avgs=0 280/300: dt: 0.9000, rms radial error=130.327, avgs=0 285/300: dt: 0.9000, rms radial error=129.594, avgs=0 290/300: dt: 0.9000, rms radial error=128.865, avgs=0 295/300: dt: 0.9000, rms radial error=128.140, avgs=0 300/300: dt: 0.9000, rms radial error=127.420, avgs=0 spherical inflation complete. epoch 1 (K=10.0), pass 1, starting sse = 16103.94 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00033 epoch 2 (K=40.0), pass 1, starting sse = 2671.78 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00031 epoch 3 (K=160.0), pass 1, starting sse = 265.30 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.07/13 = 0.00534 epoch 4 (K=640.0), pass 1, starting sse = 19.29 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.06/18 = 0.00324 final distance error %100000.00 writing spherical brain to ../surf/rh.qsphere.nofix spherical transformation took 0.0284 hours FSRUNTIME@ mris_sphere 0.0284 hours 1 threads #VMPC# mris_sphere VmPeak 505508 mris_sphere done @#@FSTIME 2022:02:17:09:08:23 mris_sphere N 9 e 102.41 S 3.23 U 98.86 P 99% M 226408 F 0 R 2650080 W 0 c 1504 w 316 I 0 O 9648 L 1.12 1.05 1.01 @#@FSLOADPOST 2022:02:17:09:10:05 mris_sphere N 9 1.02 1.03 1.00 #@# Fix Topology lh Thu Feb 17 09:10:05 EST 2022 mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 sub-LALM61_ses-LALM61 lh reading spherical homeomorphism from 'qsphere.nofix' reading inflated coordinates from 'inflated.nofix' reading original coordinates from 'orig.nofix' using genetic algorithm with optimized parameters setting seed for random number genererator to 1234 ************************************************************* Topology Correction Parameters retessellation mode: genetic search number of patches/generation : 10 number of generations : 10 surface mri loglikelihood coefficient : 1.0 volume mri loglikelihood coefficient : 10.0 normal dot loglikelihood coefficient : 1.0 quadratic curvature loglikelihood coefficient : 1.0 volume resolution : 2 eliminate vertices during search : 1 initial patch selection : 1 select all defect vertices : 0 ordering dependant retessellation: 0 use precomputed edge table : 0 smooth retessellated patch : 2 match retessellated patch : 1 verbose mode : 0 ************************************************************* INFO: assuming .mgz format writing corrected surface to 'orig.premesh' 7.2.0 7.2.0 before topology correction, eno=-30 (nv=134110, nf=268280, ne=402420, g=16) using quasi-homeomorphic spherical map to tessellate cortical surface... Correction of the Topology Finding true center and radius of Spherical Surface...done Surface centered at (0,0,0) with radius 100.0 in 8 iterations marking ambiguous vertices... 9504 ambiguous faces found in tessellation segmenting defects... 29 defects found, arbitrating ambiguous regions... analyzing neighboring defects... -merging segment 6 into 2 28 defects to be corrected 0 vertices coincident reading input surface /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/lh.qsphere.nofix... reading brain volume from brain... reading wm segmentation from wm... Reading original properties of orig.nofix Reading vertex positions of inflated.nofix Computing Initial Surface Statistics -face loglikelihood: -9.4151 (-4.7076) -vertex loglikelihood: -6.5994 (-3.2997) -normal dot loglikelihood: -3.6805 (-3.6805) -quad curv loglikelihood: -6.1692 (-3.0846) Total Loglikelihood : -25.8642 CORRECTING DEFECT 0 (vertices=47, convex hull=91, v0=14309) After retessellation of defect 0 (v0=14309), euler #=-25 (128625,384483,255833) : difference with theory (-25) = 0 CORRECTING DEFECT 1 (vertices=62, convex hull=50, v0=23597) After retessellation of defect 1 (v0=23597), euler #=-24 (128638,384540,255878) : difference with theory (-24) = 0 CORRECTING DEFECT 2 (vertices=262, convex hull=144, v0=26468) After retessellation of defect 2 (v0=26468), euler #=-22 (128705,384808,256081) : difference with theory (-23) = -1 CORRECTING DEFECT 3 (vertices=43, convex hull=74, v0=35816) After retessellation of defect 3 (v0=35816), euler #=-21 (128725,384898,256152) : difference with theory (-22) = -1 CORRECTING DEFECT 4 (vertices=49, convex hull=75, v0=45161) After retessellation of defect 4 (v0=45161), euler #=-20 (128755,385020,256245) : difference with theory (-21) = -1 CORRECTING DEFECT 5 (vertices=66, convex hull=89, v0=45210) After retessellation of defect 5 (v0=45210), euler #=-19 (128795,385179,256365) : difference with theory (-20) = -1 CORRECTING DEFECT 6 (vertices=602, convex hull=106, v0=47415) After retessellation of defect 6 (v0=47415), euler #=-19 (128833,385342,256490) : difference with theory (-19) = 0 CORRECTING DEFECT 7 (vertices=179, convex hull=180, v0=48248) After retessellation of defect 7 (v0=48248), euler #=-18 (128936,385733,256779) : difference with theory (-18) = 0 CORRECTING DEFECT 8 (vertices=7, convex hull=24, v0=49503) After retessellation of defect 8 (v0=49503), euler #=-17 (128940,385751,256794) : difference with theory (-17) = 0 CORRECTING DEFECT 9 (vertices=1143, convex hull=543, v0=54339) A large defect has been detected... This often happens because cerebellum or dura has not been removed from wm.mgz. This may cause recon-all to run very slowly or crash. if so, see https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/TopologicalDefect_freeview After retessellation of defect 9 (v0=54339), euler #=-16 (129148,386656,257492) : difference with theory (-16) = 0 CORRECTING DEFECT 10 (vertices=20, convex hull=22, v0=59991) After retessellation of defect 10 (v0=59991), euler #=-15 (129150,386667,257502) : difference with theory (-15) = 0 CORRECTING DEFECT 11 (vertices=7, convex hull=17, v0=60619) After retessellation of defect 11 (v0=60619), euler #=-14 (129152,386679,257513) : difference with theory (-14) = 0 CORRECTING DEFECT 12 (vertices=352, convex hull=329, v0=63315) After retessellation of defect 12 (v0=63315), euler #=-13 (129289,387251,257949) : difference with theory (-13) = 0 CORRECTING DEFECT 13 (vertices=6, convex hull=27, v0=74065) After retessellation of defect 13 (v0=74065), euler #=-12 (129290,387261,257959) : difference with theory (-12) = 0 CORRECTING DEFECT 14 (vertices=54, convex hull=36, v0=75988) After retessellation of defect 14 (v0=75988), euler #=-11 (129298,387301,257992) : difference with theory (-11) = 0 CORRECTING DEFECT 15 (vertices=6, convex hull=19, v0=76722) After retessellation of defect 15 (v0=76722), euler #=-10 (129301,387315,258004) : difference with theory (-10) = 0 CORRECTING DEFECT 16 (vertices=124, convex hull=127, v0=78509) After retessellation of defect 16 (v0=78509), euler #=-9 (129361,387551,258181) : difference with theory (-9) = 0 CORRECTING DEFECT 17 (vertices=54, convex hull=38, v0=81464) After retessellation of defect 17 (v0=81464), euler #=-8 (129369,387587,258210) : difference with theory (-8) = 0 CORRECTING DEFECT 18 (vertices=59, convex hull=47, v0=83463) After retessellation of defect 18 (v0=83463), euler #=-7 (129382,387642,258253) : difference with theory (-7) = 0 CORRECTING DEFECT 19 (vertices=27, convex hull=30, v0=88387) After retessellation of defect 19 (v0=88387), euler #=-6 (129387,387671,258278) : difference with theory (-6) = 0 CORRECTING DEFECT 20 (vertices=188, convex hull=128, v0=88740) After retessellation of defect 20 (v0=88740), euler #=-5 (129404,387780,258371) : difference with theory (-5) = 0 CORRECTING DEFECT 21 (vertices=1196, convex hull=349, v0=90158) After retessellation of defect 21 (v0=90158), euler #=-4 (129585,388497,258908) : difference with theory (-4) = 0 CORRECTING DEFECT 22 (vertices=63, convex hull=32, v0=91545) After retessellation of defect 22 (v0=91545), euler #=-3 (129594,388535,258938) : difference with theory (-3) = 0 CORRECTING DEFECT 23 (vertices=75, convex hull=104, v0=101757) After retessellation of defect 23 (v0=101757), euler #=-2 (129635,388705,259068) : difference with theory (-2) = 0 CORRECTING DEFECT 24 (vertices=413, convex hull=239, v0=102397) After retessellation of defect 24 (v0=102397), euler #=-1 (129767,389219,259451) : difference with theory (-1) = 0 CORRECTING DEFECT 25 (vertices=149, convex hull=50, v0=104647) After retessellation of defect 25 (v0=104647), euler #=0 (129780,389277,259497) : difference with theory (0) = 0 CORRECTING DEFECT 26 (vertices=228, convex hull=111, v0=119852) After retessellation of defect 26 (v0=119852), euler #=1 (129798,389379,259582) : difference with theory (1) = 0 CORRECTING DEFECT 27 (vertices=15, convex hull=33, v0=128917) After retessellation of defect 27 (v0=128917), euler #=2 (129799,389391,259594) : difference with theory (2) = 0 computing original vertex metric properties... storing new metric properties... computing tessellation statistics... vertex spacing 0.89 +- 0.23 (0.04-->14.56) (max @ vno 49197 --> 52748) face area -nan +- -nan (1000.00-->-1.00) performing soap bubble on retessellated vertices for 0 iterations... vertex spacing 0.89 +- 0.23 (0.04-->14.56) (max @ vno 49197 --> 52748) face area -nan +- -nan (1000.00-->-1.00) tessellation finished, orienting corrected surface... 94 mutations (38.8%), 148 crossovers (61.2%), 260 vertices were eliminated building final representation... 4311 vertices and 0 faces have been removed from triangulation after topology correction, eno=2 (nv=129799, nf=259594, ne=389391, g=0) writing corrected surface to /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/lh.orig.premesh... 0.000 % of the vertices (0 vertices) exhibit an orientation change removing intersecting faces 000: 369 intersecting 001: 58 intersecting 002: 24 intersecting step 1 with no progress (num=24, old_num=24) 003: 24 intersecting step 2 with no progress (num=24, old_num=24) 004: 24 intersecting step 3 with no progress (num=24, old_num=24) 005: 24 intersecting step 4 with no progress (num=24, old_num=24) 006: 24 intersecting step 5 with no progress (num=24, old_num=24) 007: 24 intersecting step 6 with no progress (num=24, old_num=24) 008: 24 intersecting step 7 with no progress (num=24, old_num=24) 009: 24 intersecting step 8 with no progress (num=24, old_num=24) 010: 24 intersecting step 9 with no progress (num=24, old_num=24) 011: 24 intersecting step 10 with no progress (num=24, old_num=24) 012: 24 intersecting step 11 with no progress (num=24, old_num=24) 013: 24 intersecting step 12 with no progress (num=24, old_num=24) 014: 24 intersecting step 13 with no progress (num=24, old_num=24) 015: 24 intersecting step 14 with no progress (num=24, old_num=24) 016: 24 intersecting step 15 with no progress (num=24, old_num=24) 017: 24 intersecting step 16 with no progress (num=24, old_num=24) terminating search with 24 intersecting topology fixing took 5.0 minutes FSRUNTIME@ mris_fix_topology lh 0.0836 hours 1 threads #VMPC# mris_fix_topology VmPeak 780760 @#@FSTIME 2022:02:17:09:10:05 mris_fix_topology N 14 e 300.85 S 0.40 U 299.37 P 99% M 731752 F 0 R 175301 W 0 c 5277 w 353 I 0 O 12272 L 1.02 1.03 1.00 @#@FSLOADPOST 2022:02:17:09:15:06 mris_fix_topology N 14 1.74 1.47 1.18 #@# Fix Topology rh Thu Feb 17 09:15:06 EST 2022 mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 sub-LALM61_ses-LALM61 rh reading spherical homeomorphism from 'qsphere.nofix' reading inflated coordinates from 'inflated.nofix' reading original coordinates from 'orig.nofix' using genetic algorithm with optimized parameters setting seed for random number genererator to 1234 ************************************************************* Topology Correction Parameters retessellation mode: genetic search number of patches/generation : 10 number of generations : 10 surface mri loglikelihood coefficient : 1.0 volume mri loglikelihood coefficient : 10.0 normal dot loglikelihood coefficient : 1.0 quadratic curvature loglikelihood coefficient : 1.0 volume resolution : 2 eliminate vertices during search : 1 initial patch selection : 1 select all defect vertices : 0 ordering dependant retessellation: 0 use precomputed edge table : 0 smooth retessellated patch : 2 match retessellated patch : 1 verbose mode : 0 ************************************************************* INFO: assuming .mgz format writing corrected surface to 'orig.premesh' 7.2.0 7.2.0 before topology correction, eno=-46 (nv=137056, nf=274204, ne=411306, g=24) using quasi-homeomorphic spherical map to tessellate cortical surface... Correction of the Topology Finding true center and radius of Spherical Surface...done Surface centered at (0,0,0) with radius 100.0 in 8 iterations marking ambiguous vertices... 9198 ambiguous faces found in tessellation segmenting defects... 30 defects found, arbitrating ambiguous regions... analyzing neighboring defects... -merging segment 4 into 1 -merging segment 11 into 5 -merging segment 16 into 7 -merging segment 17 into 19 -merging segment 23 into 21 25 defects to be corrected 0 vertices coincident reading input surface /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/rh.qsphere.nofix... reading brain volume from brain... reading wm segmentation from wm... Reading original properties of orig.nofix Reading vertex positions of inflated.nofix Computing Initial Surface Statistics -face loglikelihood: -9.3656 (-4.6828) -vertex loglikelihood: -6.5241 (-3.2620) -normal dot loglikelihood: -3.6181 (-3.6181) -quad curv loglikelihood: -5.9693 (-2.9847) Total Loglikelihood : -25.4772 CORRECTING DEFECT 0 (vertices=50, convex hull=34, v0=27779) After retessellation of defect 0 (v0=27779), euler #=-27 (131709,393621,261885) : difference with theory (-22) = 5 CORRECTING DEFECT 1 (vertices=187, convex hull=185, v0=35136) After retessellation of defect 1 (v0=35136), euler #=-25 (131805,394006,262176) : difference with theory (-21) = 4 CORRECTING DEFECT 2 (vertices=246, convex hull=197, v0=35322) After retessellation of defect 2 (v0=35322), euler #=-24 (131875,394309,262410) : difference with theory (-20) = 4 CORRECTING DEFECT 3 (vertices=24, convex hull=60, v0=37462) After retessellation of defect 3 (v0=37462), euler #=-23 (131889,394374,262462) : difference with theory (-19) = 4 CORRECTING DEFECT 4 (vertices=628, convex hull=387, v0=42736) After retessellation of defect 4 (v0=42736), euler #=-21 (132020,394956,262915) : difference with theory (-18) = 3 CORRECTING DEFECT 5 (vertices=381, convex hull=123, v0=49536) After retessellation of defect 5 (v0=49536), euler #=-20 (132066,395162,263076) : difference with theory (-17) = 3 CORRECTING DEFECT 6 (vertices=976, convex hull=661, v0=54740) An extra large defect has been detected... This often happens because cerebellum or dura has not been removed from wm.mgz. This may cause recon-all to run very slowly or crash. if so, see https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/TopologicalDefect_freeview After retessellation of defect 6 (v0=54740), euler #=-18 (132169,395805,263618) : difference with theory (-16) = 2 CORRECTING DEFECT 7 (vertices=142, convex hull=56, v0=56192) After retessellation of defect 7 (v0=56192), euler #=-17 (132198,395915,263700) : difference with theory (-15) = 2 CORRECTING DEFECT 8 (vertices=6, convex hull=20, v0=62018) After retessellation of defect 8 (v0=62018), euler #=-16 (132200,395926,263710) : difference with theory (-14) = 2 CORRECTING DEFECT 9 (vertices=7, convex hull=25, v0=64582) After retessellation of defect 9 (v0=64582), euler #=-15 (132203,395942,263724) : difference with theory (-13) = 2 CORRECTING DEFECT 10 (vertices=443, convex hull=239, v0=71945) After retessellation of defect 10 (v0=71945), euler #=-14 (132356,396525,264155) : difference with theory (-12) = 2 CORRECTING DEFECT 11 (vertices=351, convex hull=59, v0=73701) After retessellation of defect 11 (v0=73701), euler #=-13 (132367,396586,264206) : difference with theory (-11) = 2 CORRECTING DEFECT 12 (vertices=65, convex hull=58, v0=73797) After retessellation of defect 12 (v0=73797), euler #=-12 (132380,396650,264258) : difference with theory (-10) = 2 CORRECTING DEFECT 13 (vertices=676, convex hull=114, v0=78396) After retessellation of defect 13 (v0=78396), euler #=-11 (132390,396739,264338) : difference with theory (-9) = 2 CORRECTING DEFECT 14 (vertices=60, convex hull=74, v0=93101) After retessellation of defect 14 (v0=93101), euler #=-10 (132410,396827,264407) : difference with theory (-8) = 2 CORRECTING DEFECT 15 (vertices=53, convex hull=69, v0=94684) After retessellation of defect 15 (v0=94684), euler #=-8 (132421,396885,264456) : difference with theory (-7) = 1 CORRECTING DEFECT 16 (vertices=169, convex hull=136, v0=94962) After retessellation of defect 16 (v0=94962), euler #=-7 (132497,397177,264673) : difference with theory (-6) = 1 CORRECTING DEFECT 17 (vertices=132, convex hull=73, v0=97113) After retessellation of defect 17 (v0=97113), euler #=-5 (132535,397322,264782) : difference with theory (-5) = 0 CORRECTING DEFECT 18 (vertices=37, convex hull=65, v0=98315) After retessellation of defect 18 (v0=98315), euler #=-4 (132553,397406,264849) : difference with theory (-4) = 0 CORRECTING DEFECT 19 (vertices=106, convex hull=96, v0=102723) After retessellation of defect 19 (v0=102723), euler #=-3 (132583,397534,264948) : difference with theory (-3) = 0 CORRECTING DEFECT 20 (vertices=94, convex hull=87, v0=108153) After retessellation of defect 20 (v0=108153), euler #=-2 (132606,397638,265030) : difference with theory (-2) = 0 CORRECTING DEFECT 21 (vertices=240, convex hull=181, v0=110187) After retessellation of defect 21 (v0=110187), euler #=-1 (132665,397901,265235) : difference with theory (-1) = 0 CORRECTING DEFECT 22 (vertices=57, convex hull=80, v0=112496) After retessellation of defect 22 (v0=112496), euler #=0 (132698,398028,265330) : difference with theory (0) = 0 CORRECTING DEFECT 23 (vertices=54, convex hull=74, v0=123239) After retessellation of defect 23 (v0=123239), euler #=1 (132729,398156,265428) : difference with theory (1) = 0 CORRECTING DEFECT 24 (vertices=161, convex hull=53, v0=123951) After retessellation of defect 24 (v0=123951), euler #=2 (132741,398217,265478) : difference with theory (2) = 0 computing original vertex metric properties... storing new metric properties... computing tessellation statistics... vertex spacing 0.89 +- 0.26 (0.02-->13.79) (max @ vno 70705 --> 71659) face area -nan +- -nan (1000.00-->-1.00) performing soap bubble on retessellated vertices for 0 iterations... vertex spacing 0.89 +- 0.26 (0.02-->13.79) (max @ vno 70705 --> 71659) face area -nan +- -nan (1000.00-->-1.00) tessellation finished, orienting corrected surface... 86 mutations (34.7%), 162 crossovers (65.3%), 487 vertices were eliminated building final representation... 4315 vertices and 0 faces have been removed from triangulation after topology correction, eno=2 (nv=132741, nf=265478, ne=398217, g=0) writing corrected surface to /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/rh.orig.premesh... 0.000 % of the vertices (0 vertices) exhibit an orientation change removing intersecting faces 000: 454 intersecting 001: 80 intersecting 002: 24 intersecting 003: 21 intersecting 004: 18 intersecting step 1 with no progress (num=18, old_num=18) 005: 18 intersecting step 2 with no progress (num=18, old_num=18) 006: 18 intersecting step 3 with no progress (num=18, old_num=18) 007: 18 intersecting step 4 with no progress (num=18, old_num=18) 008: 18 intersecting step 5 with no progress (num=18, old_num=18) 009: 18 intersecting step 6 with no progress (num=18, old_num=18) 010: 18 intersecting step 7 with no progress (num=18, old_num=18) 011: 18 intersecting step 8 with no progress (num=18, old_num=18) 012: 18 intersecting step 9 with no progress (num=18, old_num=18) 013: 18 intersecting step 10 with no progress (num=18, old_num=18) 014: 18 intersecting step 11 with no progress (num=18, old_num=18) 015: 18 intersecting step 12 with no progress (num=18, old_num=18) 016: 18 intersecting step 13 with no progress (num=18, old_num=18) 017: 18 intersecting step 14 with no progress (num=18, old_num=18) 018: 18 intersecting step 15 with no progress (num=18, old_num=18) 019: 18 intersecting step 16 with no progress (num=18, old_num=18) terminating search with 18 intersecting topology fixing took 5.6 minutes FSRUNTIME@ mris_fix_topology rh 0.0940 hours 1 threads #VMPC# mris_fix_topology VmPeak 783980 @#@FSTIME 2022:02:17:09:15:06 mris_fix_topology N 14 e 338.54 S 0.50 U 336.99 P 99% M 734792 F 0 R 176079 W 0 c 5557 w 311 I 0 O 12552 L 1.74 1.47 1.18 @#@FSLOADPOST 2022:02:17:09:20:45 mris_fix_topology N 14 1.18 1.31 1.19 mris_euler_number ../surf/lh.orig.premesh euler # = v-e+f = 2g-2: 129799 - 389391 + 259594 = 2 --> 0 holes F =2V-4: 259594 = 259598-4 (0) 2E=3F: 778782 = 778782 (0) total defect index = 0 mris_euler_number ../surf/rh.orig.premesh euler # = v-e+f = 2g-2: 132741 - 398217 + 265478 = 2 --> 0 holes F =2V-4: 265478 = 265482-4 (0) 2E=3F: 796434 = 796434 (0) total defect index = 0 Thu Feb 17 09:20:46 EST 2022 setenv SUBJECTS_DIR /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts /autofs/cluster/freesurfer/centos7_x86_64/7.2.0/bin/defect2seg --s sub-LALM61_ses-LALM61 freesurfer-linux-centos7_x86_64-7.2.0-20210720-aa8f76b defect2seg 7.2.0 Linux erso.nmr.mgh.harvard.edu 4.18.0-365.el8.x86_64 #1 SMP Thu Feb 10 16:11:23 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux pid 2110571 mri_label2vol --defects /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/lh.orig.nofix /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/lh.defect_labels /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/orig.mgz 1000 0 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/surface.defects.mgz mri_label2vol supposed to be reproducible but seed not set Changing input type 0 to MRI_INT Converting defects to volume: offset=1000, merge=0 Writing to /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/surface.defects.mgz mris_defects_pointset -s /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/lh.orig.nofix -d /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/lh.defect_labels -o /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/lh.defects.pointset Reading in surface /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/lh.orig.nofix Reading in defect segmentation /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/lh.defect_labels #VMPC# mris_defects_pointset 190356 mris_defects_pointset done mri_label2vol --defects /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/rh.orig.nofix /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/rh.defect_labels /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/surface.defects.mgz 2000 1 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/surface.defects.mgz mri_label2vol supposed to be reproducible but seed not set Converting defects to volume: offset=2000, merge=1 MRIcopyHeader(): source has ctab Writing to /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri/surface.defects.mgz mris_defects_pointset -s /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/rh.orig.nofix -d /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/rh.defect_labels -o /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/rh.defects.pointset Reading in surface /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/rh.orig.nofix Reading in defect segmentation /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/rh.defect_labels #VMPC# mris_defects_pointset 193580 mris_defects_pointset done Started at Thu Feb 17 09:20:46 EST 2022 Ended at Thu Feb 17 09:20:50 EST 2022 Defect2seg-Run-Time-Sec 4 Defect2seg-Run-Time-Min 0.08 Defect2seg-Run-Time-Hours 0.00 tkmeditfv sub-LALM61_ses-LALM61 brain.finalsurfs.mgz -defect defect2seg Done @#@FSTIME 2022:02:17:09:20:46 defect2seg N 2 e 4.50 S 0.17 U 3.95 P 91% M 242304 F 0 R 98924 W 0 c 72 w 441 I 0 O 632 L 1.17 1.30 1.19 @#@FSLOADPOST 2022:02:17:09:20:50 defect2seg N 2 1.16 1.30 1.19 mris_remesh --remesh --iters 3 --input /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/lh.orig.premesh --output /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/lh.orig iters = 3 standard remeshing without target adjusted l: 0.709382 remeshing to edge length 0.709382 with 3 iterations avg qual before : 0.890758 after: 0.971201 Removing intersections removing intersecting faces 000: 24 intersecting 001: 10 intersecting 002: 5 intersecting step 1 with no progress (num=5, old_num=5) 003: 5 intersecting step 2 with no progress (num=5, old_num=5) 004: 5 intersecting step 3 with no progress (num=5, old_num=5) 005: 5 intersecting step 4 with no progress (num=5, old_num=5) 006: 5 intersecting step 5 with no progress (num=5, old_num=5) 007: 5 intersecting step 6 with no progress (num=5, old_num=5) 008: 5 intersecting step 7 with no progress (num=5, old_num=5) 009: 5 intersecting step 8 with no progress (num=5, old_num=5) 010: 5 intersecting step 9 with no progress (num=5, old_num=5) 011: 5 intersecting step 10 with no progress (num=5, old_num=5) 012: 5 intersecting step 11 with no progress (num=5, old_num=5) 013: 5 intersecting step 12 with no progress (num=5, old_num=5) 014: 5 intersecting step 13 with no progress (num=5, old_num=5) 015: 5 intersecting step 14 with no progress (num=5, old_num=5) 016: 5 intersecting step 15 with no progress (num=5, old_num=5) 017: 5 intersecting step 16 with no progress (num=5, old_num=5) terminating search with 5 intersecting Remeshed surface quality stats nv0 = 129799 nv = 135237 1.0419 Area 270470 0.30135 0.03374 0.004866 0.4978 Corner 811410 60.00000 8.82792 0.923508 177.1186 Edge 405705 0.84217 0.08234 0.076565 1.2445 Hinge 405705 10.04480 10.20185 0.000030 176.5601 mris_remesh done @#@FSTIME 2022:02:17:09:20:50 mris_remesh N 7 e 48.37 S 0.32 U 47.82 P 99% M 937288 F 0 R 222118 W 0 c 805 w 304 I 0 O 9520 L 1.16 1.30 1.19 @#@FSLOADPOST 2022:02:17:09:21:39 mris_remesh N 7 1.15 1.27 1.18 mris_remesh --remesh --iters 3 --input /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/rh.orig.premesh --output /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf/rh.orig iters = 3 standard remeshing without target adjusted l: 0.712616 remeshing to edge length 0.712616 with 3 iterations avg qual before : 0.891603 after: 0.971553 Removing intersections removing intersecting faces 000: 21 intersecting terminating search with 0 intersecting Remeshed surface quality stats nv0 = 132741 nv = 138581 1.044 Area 277158 0.30410 0.03365 0.045419 0.4766 Corner 831474 60.00000 8.74315 15.502342 147.6324 Edge 415737 0.84591 0.08213 0.275156 1.2309 Hinge 415737 9.97756 10.08571 0.000001 164.1865 mris_remesh done @#@FSTIME 2022:02:17:09:21:39 mris_remesh N 7 e 26.14 S 0.21 U 25.78 P 99% M 782616 F 0 R 183509 W 0 c 352 w 212 I 0 O 9760 L 1.15 1.27 1.18 @#@FSLOADPOST 2022:02:17:09:22:05 mris_remesh N 7 1.10 1.25 1.18 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_remove_intersection ../surf/lh.orig ../surf/lh.orig intersection removal took 0.01 hours removing intersecting faces 000: 5 intersecting step 1 with no progress (num=5, old_num=5) 001: 5 intersecting step 2 with no progress (num=5, old_num=5) 002: 5 intersecting step 3 with no progress (num=5, old_num=5) 003: 5 intersecting step 4 with no progress (num=5, old_num=5) 004: 5 intersecting step 5 with no progress (num=5, old_num=5) 005: 5 intersecting step 6 with no progress (num=5, old_num=5) 006: 5 intersecting step 7 with no progress (num=5, old_num=5) 007: 5 intersecting step 8 with no progress (num=5, old_num=5) 008: 5 intersecting step 9 with no progress (num=5, old_num=5) 009: 5 intersecting step 10 with no progress (num=5, old_num=5) 010: 5 intersecting step 11 with no progress (num=5, old_num=5) 011: 5 intersecting step 12 with no progress (num=5, old_num=5) 012: 5 intersecting step 13 with no progress (num=5, old_num=5) 013: 5 intersecting step 14 with no progress (num=5, old_num=5) 014: 5 intersecting step 15 with no progress (num=5, old_num=5) 015: 5 intersecting step 16 with no progress (num=5, old_num=5) terminating search with 5 intersecting writing corrected surface to ../surf/lh.orig @#@FSTIME 2022:02:17:09:22:05 mris_remove_intersection N 2 e 23.06 S 0.10 U 22.81 P 99% M 378472 F 0 R 77143 W 0 c 216 w 216 I 0 O 9512 L 1.10 1.25 1.18 @#@FSLOADPOST 2022:02:17:09:22:28 mris_remove_intersection N 2 1.06 1.23 1.17 rm -f ../surf/lh.inflated /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_remove_intersection ../surf/rh.orig ../surf/rh.orig intersection removal took 0.00 hours writing corrected surface to ../surf/rh.orig @#@FSTIME 2022:02:17:09:22:28 mris_remove_intersection N 2 e 2.12 S 0.07 U 1.96 P 96% M 333620 F 0 R 57718 W 0 c 20 w 226 I 0 O 9752 L 1.06 1.23 1.17 @#@FSLOADPOST 2022:02:17:09:22:30 mris_remove_intersection N 2 1.06 1.22 1.17 rm -f ../surf/rh.inflated #-------------------------------------------- #@# AutoDetGWStats lh Thu Feb 17 09:22:30 EST 2022 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mris_autodet_gwstats --o ../surf/autodet.gw.stats.lh.dat --i brain.finalsurfs.mgz --wm wm.mgz --surf ../surf/lh.orig.premesh 7.2.0 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri setenv SUBJECTS_DIR /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer mris_autodet_gwstats --o ../surf/autodet.gw.stats.lh.dat --i brain.finalsurfs.mgz --wm wm.mgz --surf ../surf/lh.orig.premesh border white: 257759 voxels (1.54%) border gray 300766 voxels (1.79%) Reading in intensity volume brain.finalsurfs.mgz Reading in wm volume wm.mgz Reading in surf ../surf/lh.orig.premesh Auto detecting stats MRIclipBrightWM(): nthresh=17159, wmmin=5, clip=110 Binarizing thresholding at 5 computing class statistics... low=30, hi=110.000000 CCS WM (98.0): 97.6 +- 8.4 [70.0 --> 110.0] CCS GM (71.0) : 70.2 +- 13.0 [30.0 --> 110.0] white_mean = 97.6483 +/- 8.4111, gray_mean = 70.2357 +/- 13.0399 using class modes intead of means, discounting robust sigmas.... MRIScomputeClassModes(): min=0 max=188 nbins=189 intensity peaks found at WM=102+-5.2, GM=61+-11.3 white_mode = 102, gray_mode = 61 std_scale = 1 Applying sanity checks, max_scale_down = 0.2 setting MIN_GRAY_AT_WHITE_BORDER to 48.0 (was 70.000000) setting MAX_BORDER_WHITE to 110.4 (was 105.000000) setting MIN_BORDER_WHITE to 61.0 (was 85.000000) setting MAX_CSF to 34.9 (was 40.000000) setting MAX_GRAY to 93.6 (was 95.000000) setting MAX_GRAY_AT_CSF_BORDER to 48.0 (was 75.000000) setting MIN_GRAY_AT_CSF_BORDER to 21.9 (was 40.000000) When placing the white surface white_border_hi = 110.411; white_border_low = 61; white_outside_low = 47.9602; white_inside_hi = 120; white_outside_hi = 110.411; When placing the pial surface pial_border_hi = 47.9602; pial_border_low = 21.8804; pial_outside_low = 10; pial_inside_hi = 93.5889; pial_outside_hi = 41.4402; #VMPC# mris_autodet_gwstats VmPeak 265444 mris_autodet_gwstats done @#@FSTIME 2022:02:17:09:22:30 mris_autodet_gwstats N 8 e 3.89 S 0.04 U 3.77 P 98% M 233744 F 0 R 28275 W 0 c 73 w 42 I 0 O 8 L 1.06 1.22 1.17 @#@FSLOADPOST 2022:02:17:09:22:34 mris_autodet_gwstats N 8 1.06 1.22 1.17 #-------------------------------------------- #@# AutoDetGWStats rh Thu Feb 17 09:22:34 EST 2022 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mris_autodet_gwstats --o ../surf/autodet.gw.stats.rh.dat --i brain.finalsurfs.mgz --wm wm.mgz --surf ../surf/rh.orig.premesh 7.2.0 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri setenv SUBJECTS_DIR /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer mris_autodet_gwstats --o ../surf/autodet.gw.stats.rh.dat --i brain.finalsurfs.mgz --wm wm.mgz --surf ../surf/rh.orig.premesh border white: 257759 voxels (1.54%) border gray 300766 voxels (1.79%) Reading in intensity volume brain.finalsurfs.mgz Reading in wm volume wm.mgz Reading in surf ../surf/rh.orig.premesh Auto detecting stats MRIclipBrightWM(): nthresh=17159, wmmin=5, clip=110 Binarizing thresholding at 5 computing class statistics... low=30, hi=110.000000 CCS WM (98.0): 97.6 +- 8.4 [70.0 --> 110.0] CCS GM (71.0) : 70.2 +- 13.0 [30.0 --> 110.0] white_mean = 97.6483 +/- 8.4111, gray_mean = 70.2357 +/- 13.0399 using class modes intead of means, discounting robust sigmas.... MRIScomputeClassModes(): min=0 max=188 nbins=189 intensity peaks found at WM=101+-4.3, GM=62+-10.4 white_mode = 101, gray_mode = 62 std_scale = 1 Applying sanity checks, max_scale_down = 0.2 setting MIN_GRAY_AT_WHITE_BORDER to 49.0 (was 70.000000) setting MAX_BORDER_WHITE to 109.4 (was 105.000000) setting MIN_BORDER_WHITE to 62.0 (was 85.000000) setting MAX_CSF to 35.9 (was 40.000000) setting MAX_GRAY to 92.6 (was 95.000000) setting MAX_GRAY_AT_CSF_BORDER to 49.0 (was 75.000000) setting MIN_GRAY_AT_CSF_BORDER to 22.9 (was 40.000000) When placing the white surface white_border_hi = 109.411; white_border_low = 62; white_outside_low = 48.9602; white_inside_hi = 120; white_outside_hi = 109.411; When placing the pial surface pial_border_hi = 48.9602; pial_border_low = 22.8804; pial_outside_low = 10; pial_inside_hi = 92.5889; pial_outside_hi = 42.4402; #VMPC# mris_autodet_gwstats VmPeak 269328 mris_autodet_gwstats done @#@FSTIME 2022:02:17:09:22:34 mris_autodet_gwstats N 8 e 3.88 S 0.04 U 3.79 P 98% M 237744 F 0 R 33172 W 0 c 72 w 27 I 0 O 8 L 1.06 1.22 1.17 @#@FSLOADPOST 2022:02:17:09:22:38 mris_autodet_gwstats N 8 1.05 1.22 1.17 #-------------------------------------------- #@# WhitePreAparc lh Thu Feb 17 09:22:38 EST 2022 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mris_place_surface --adgws-in ../surf/autodet.gw.stats.lh.dat --wm wm.mgz --threads 1 --invol brain.finalsurfs.mgz --lh --i ../surf/lh.orig --o ../surf/lh.white.preaparc --white --seg aseg.presurf.mgz --nsmooth 5 7.2.0 7.2.0 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri setenv SUBJECTS_DIR /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer mris_place_surface --adgws-in ../surf/autodet.gw.stats.lh.dat --wm wm.mgz --threads 1 --invol brain.finalsurfs.mgz --lh --i ../surf/lh.orig --o ../surf/lh.white.preaparc --white --seg aseg.presurf.mgz --nsmooth 5 Reading in input surface ../surf/lh.orig Smoothing surface with 5 iterations removing intersecting faces 000: 22 intersecting step 1 with no progress (num=32, old_num=22) 001: 32 intersecting 002: 29 intersecting step 1 with no progress (num=49, old_num=29) 003: 49 intersecting step 2 with no progress (num=49, old_num=49) 004: 49 intersecting step 3 with no progress (num=53, old_num=49) 005: 53 intersecting step 4 with no progress (num=55, old_num=53) 006: 55 intersecting 007: 39 intersecting 008: 33 intersecting step 1 with no progress (num=40, old_num=33) 009: 40 intersecting step 2 with no progress (num=40, old_num=40) 010: 40 intersecting step 3 with no progress (num=44, old_num=40) 011: 44 intersecting 012: 43 intersecting 013: 38 intersecting step 1 with no progress (num=40, old_num=38) 014: 40 intersecting step 2 with no progress (num=49, old_num=40) 015: 49 intersecting 016: 43 intersecting step 1 with no progress (num=48, old_num=43) 017: 48 intersecting 018: 43 intersecting step 1 with no progress (num=45, old_num=43) 019: 45 intersecting 020: 41 intersecting step 1 with no progress (num=42, old_num=41) 021: 42 intersecting step 2 with no progress (num=58, old_num=42) 022: 58 intersecting 023: 42 intersecting step 1 with no progress (num=48, old_num=42) 024: 48 intersecting step 2 with no progress (num=50, old_num=48) 025: 50 intersecting 026: 45 intersecting 027: 43 intersecting 028: 41 intersecting step 1 with no progress (num=53, old_num=41) 029: 53 intersecting 030: 43 intersecting step 1 with no progress (num=46, old_num=43) 031: 46 intersecting 032: 42 intersecting step 1 with no progress (num=46, old_num=42) 033: 46 intersecting step 2 with no progress (num=47, old_num=46) 034: 47 intersecting 035: 42 intersecting 036: 36 intersecting step 1 with no progress (num=44, old_num=36) 037: 44 intersecting step 2 with no progress (num=51, old_num=44) 038: 51 intersecting 039: 41 intersecting step 1 with no progress (num=47, old_num=41) 040: 47 intersecting step 2 with no progress (num=47, old_num=47) 041: 47 intersecting step 3 with no progress (num=52, old_num=47) 042: 52 intersecting step 4 with no progress (num=55, old_num=52) 043: 55 intersecting 044: 54 intersecting 045: 53 intersecting step 1 with no progress (num=53, old_num=53) 046: 53 intersecting 047: 52 intersecting step 1 with no progress (num=60, old_num=52) 048: 60 intersecting 049: 56 intersecting 050: 52 intersecting step 1 with no progress (num=66, old_num=52) 051: 66 intersecting 052: 62 intersecting step 1 with no progress (num=71, old_num=62) 053: 71 intersecting step 2 with no progress (num=75, old_num=71) 054: 75 intersecting step 3 with no progress (num=84, old_num=75) 055: 84 intersecting 056: 83 intersecting 057: 82 intersecting 058: 80 intersecting 059: 79 intersecting step 1 with no progress (num=80, old_num=79) 060: 80 intersecting 061: 79 intersecting step 1 with no progress (num=80, old_num=79) 062: 80 intersecting step 2 with no progress (num=80, old_num=80) 063: 80 intersecting step 3 with no progress (num=80, old_num=80) 064: 80 intersecting step 4 with no progress (num=80, old_num=80) 065: 80 intersecting step 5 with no progress (num=80, old_num=80) 066: 80 intersecting step 6 with no progress (num=80, old_num=80) 067: 80 intersecting step 7 with no progress (num=80, old_num=80) 068: 80 intersecting step 8 with no progress (num=80, old_num=80) 069: 80 intersecting step 9 with no progress (num=80, old_num=80) 070: 80 intersecting step 10 with no progress (num=80, old_num=80) 071: 80 intersecting step 11 with no progress (num=80, old_num=80) 072: 80 intersecting step 12 with no progress (num=80, old_num=80) 073: 80 intersecting step 13 with no progress (num=80, old_num=80) 074: 80 intersecting step 14 with no progress (num=80, old_num=80) 075: 80 intersecting step 15 with no progress (num=80, old_num=80) 076: 80 intersecting step 16 with no progress (num=80, old_num=80) terminating search with 22 intersecting Area 270470 0.26452 0.06209 0.000004 0.6106 Corner 811410 60.00000 9.55560 0.013078 179.9543 Edge 405705 0.78542 0.11321 0.003748 1.3831 Hinge 405705 6.66438 6.72959 0.000004 179.4170 Not reading in aparc Reading in input volume brain.finalsurfs.mgz Reading in wm volume wm.mgz MRIclipBrightWM(): nthresh=17159, wmmin=5, clip=110 MRIfindBrightNonWM(): 7510 bright non-wm voxels segmented. Masking bright non-wm for white surface MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 Reading in seg volume aseg.presurf.mgz Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=0 MRIcopyHeader(): source has ctab #FML# MRISripMidline(): nmarked=6574, nmarked2=5, nripped=6574 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 MRISripSegs(): -2 2 0.5 ripped 0 vertex 67619: xyz = (-50.272,1.13403,-18.7637) oxyz = (-50.272,1.13403,-18.7637) wxzy = (-50.272,1.13403,-18.7637) pxyz = (0,0,0) CBVO Creating mask 135237 n_averages 4 Iteration 0 ========================================= n_averages=4, current_sigma=2 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6574 #FML# MRISripMidline(): nmarked=6574, nmarked2=5, nripped=6574 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 247 MRISripSegs(): -2 2 0.5 ripped 0 Computing target border values Entering MRIScomputeBorderValues_new(): inside_hi = 120.0000000; border_hi = 110.4111020; border_low = 61.0000000; outside_low = 47.9601520; outside_hi = 110.4111020; sigma = 2; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=135237 Gdiag_no=-1 vno start=0, stop=135237 Replacing 255s with 0s #SI# sigma=2 had to be increased for 268 vertices, nripped=6574 mean border=76.8, 337 (337) missing vertices, mean dist 0.3 [0.7 (%36.2)->0.9 (%63.8))] %67 local maxima, %28 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.2295 min Finding expansion regions mean absolute distance = 0.81 +- 1.04 3887 vertices more than 2 sigmas from mean. Averaging target values for 5 iterations... Positioning Surface: tspring = 0.3, nspring = 0.3, spring = 0, niters = 100 l_repulse = 5, l_surf_repulse = 0, checktol = 0 Positioning pial surface Entering MRISpositionSurface() max_mm = 0.3 MAX_REDUCTIONS = 2, REDUCTION_PCT = 0.5 parms->check_tol = 0, niterations = 100 tol=1.0e-04, sigma=2.0, host=erso., nav=4, nbrs=2, l_repulse=5.000, l_tspring=0.300, l_nspring=0.300, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 complete_dist_mat 0 rms 0 smooth_averages 0 remove_neg 0 ico_order 0 which_surface 0 target_radius 0.000000 nfields 0 scale 0.000000 desired_rms_height 0.000000 momentum 0.000000 nbhd_size 0 max_nbrs 0 niterations 100 nsurfaces 0 SURFACES 3 flags 0 (0) use curv 0 no sulc 0 no rigid align 0 mris->nsize 2 mris->hemisphere 0 randomSeed 0 000: dt: 0.0000, sse=4633224.5, rms=13.219 001: dt: 0.5000, sse=2806858.0, rms=10.188 (22.933%) 002: dt: 0.5000, sse=1864227.1, rms=8.188 (19.631%) 003: dt: 0.5000, sse=1260475.8, rms=6.579 (19.648%) 004: dt: 0.5000, sse=907755.1, rms=5.423 (17.577%) 005: dt: 0.5000, sse=718372.9, rms=4.711 (13.119%) 006: dt: 0.5000, sse=651477.1, rms=4.396 (6.685%) 007: dt: 0.5000, sse=598907.6, rms=4.174 (5.068%) 008: dt: 0.5000, sse=585571.9, rms=4.090 (1.993%) 009: dt: 0.5000, sse=569501.5, rms=4.008 (2.022%) rms = 4.0032/4.0077, sse=566050.8/569501.5, time step reduction 1 of 3 to 0.250 0 0 1 010: dt: 0.5000, sse=566050.8, rms=4.003 (0.112%) 011: dt: 0.2500, sse=327313.6, rms=2.565 (35.916%) 012: dt: 0.2500, sse=281149.8, rms=2.185 (14.847%) 013: dt: 0.2500, sse=270629.9, rms=2.062 (5.602%) 014: dt: 0.2500, sse=260273.2, rms=1.986 (3.678%) rms = 1.9428/1.9863, sse=259349.1/260273.2, time step reduction 2 of 3 to 0.125 0 0 1 015: dt: 0.2500, sse=259349.1, rms=1.943 (2.191%) 016: dt: 0.1250, sse=252962.1, rms=1.851 (4.734%) rms = 1.8156/1.8508, sse=243460.9/252962.1, time step reduction 3 of 3 to 0.062 0 0 1 017: dt: 0.1250, sse=243460.9, rms=1.816 (1.902%) maximum number of reductions reached, breaking from loop positioning took 1.4 minutes Iteration 1 ========================================= n_averages=2, current_sigma=1 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6574 removing 3 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6742, nmarked2=5, nripped=6742 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 247 247 MRISripSegs(): -2 2 0.5 ripped 0 Computing target border values Entering MRIScomputeBorderValues_new(): inside_hi = 120.0000000; border_hi = 110.4111020; border_low = 61.0000000; outside_low = 47.9601520; outside_hi = 110.4111020; sigma = 1; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=135237 Gdiag_no=-1 vno start=0, stop=135237 Replacing 255s with 0s #SI# sigma=1 had to be increased for 193 vertices, nripped=6742 mean border=78.2, 265 (128) missing vertices, mean dist -0.2 [0.4 (%57.0)->0.2 (%43.0))] %67 local maxima, %28 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.1475 min Finding expansion regions mean absolute distance = 0.36 +- 0.80 3446 vertices more than 2 sigmas from mean. Averaging target values for 5 iterations... Positioning Surface: tspring = 0.3, nspring = 0.3, spring = 0, niters = 100 l_repulse = 5, l_surf_repulse = 0, checktol = 0 Positioning pial surface Entering MRISpositionSurface() max_mm = 0.3 MAX_REDUCTIONS = 2, REDUCTION_PCT = 0.5 parms->check_tol = 0, niterations = 100 tol=1.0e-04, sigma=1.0, host=erso., nav=2, nbrs=2, l_repulse=5.000, l_tspring=0.300, l_nspring=0.300, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 000: dt: 0.0000, sse=645114.6, rms=4.100 018: dt: 0.5000, sse=498791.8, rms=3.254 (20.647%) rms = 3.4611/3.2538, sse=533654.5/498791.8, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 019: dt: 0.2500, sse=383128.3, rms=2.460 (24.407%) 020: dt: 0.2500, sse=321515.2, rms=2.024 (17.723%) 021: dt: 0.2500, sse=301844.4, rms=1.855 (8.342%) rms = 1.7430/1.8549, sse=305092.4/301844.4, time step reduction 2 of 3 to 0.125 0 1 0 022: dt: 0.2500, sse=305092.4, rms=1.743 (6.031%) 023: dt: 0.1250, sse=283183.8, rms=1.654 (5.089%) rms = 1.6150/1.6543, sse=284307.2/283183.7, time step reduction 3 of 3 to 0.062 0 1 1 024: dt: 0.1250, sse=284307.2, rms=1.615 (2.377%) maximum number of reductions reached, breaking from loop positioning took 0.6 minutes Iteration 2 ========================================= n_averages=1, current_sigma=0.5 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6742 removing 2 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6776, nmarked2=5, nripped=6776 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 247 247 247 MRISripSegs(): -2 2 0.5 ripped 0 Computing target border values Entering MRIScomputeBorderValues_new(): inside_hi = 120.0000000; border_hi = 110.4111020; border_low = 61.0000000; outside_low = 47.9601520; outside_hi = 110.4111020; sigma = 0.5; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=135237 Gdiag_no=-1 vno start=0, stop=135237 Replacing 255s with 0s #SI# sigma=0.5 had to be increased for 205 vertices, nripped=6776 mean border=80.5, 228 (81) missing vertices, mean dist -0.2 [0.4 (%65.1)->0.2 (%34.9))] %77 local maxima, %18 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0812 min Finding expansion regions mean absolute distance = 0.33 +- 0.57 3622 vertices more than 2 sigmas from mean. Averaging target values for 5 iterations... Positioning Surface: tspring = 0.3, nspring = 0.3, spring = 0, niters = 100 l_repulse = 5, l_surf_repulse = 0, checktol = 0 Positioning pial surface Entering MRISpositionSurface() max_mm = 0.3 MAX_REDUCTIONS = 2, REDUCTION_PCT = 0.5 parms->check_tol = 0, niterations = 100 tol=1.0e-04, sigma=0.5, host=erso., nav=1, nbrs=2, l_repulse=5.000, l_tspring=0.300, l_nspring=0.300, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 000: dt: 0.0000, sse=605137.4, rms=3.895 025: dt: 0.5000, sse=477744.2, rms=3.180 (18.377%) rms = 3.3493/3.1796, sse=504411.5/477744.2, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 026: dt: 0.2500, sse=356341.6, rms=2.343 (26.324%) 027: dt: 0.2500, sse=301953.2, rms=1.854 (20.865%) 028: dt: 0.2500, sse=289752.2, rms=1.688 (8.928%) 029: dt: 0.2500, sse=278787.8, rms=1.614 (4.385%) rms = 1.6010/1.6143, sse=286530.3/278787.8, time step reduction 2 of 3 to 0.125 0 1 1 030: dt: 0.2500, sse=286530.3, rms=1.601 (0.825%) 031: dt: 0.1250, sse=281765.9, rms=1.468 (8.316%) rms = 1.4397/1.4678, sse=277615.8/281765.9, time step reduction 3 of 3 to 0.062 0 0 1 032: dt: 0.1250, sse=277615.8, rms=1.440 (1.913%) maximum number of reductions reached, breaking from loop positioning took 0.7 minutes Iteration 3 ========================================= n_averages=0, current_sigma=0.25 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6776 removing 3 vertices from ripped group in thread:0 removing 1 vertices from ripped group in thread:0 removing 4 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6776, nmarked2=5, nripped=6776 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 247 247 247 247 MRISripSegs(): -2 2 0.5 ripped 0 Computing target border values Entering MRIScomputeBorderValues_new(): inside_hi = 120.0000000; border_hi = 110.4111020; border_low = 61.0000000; outside_low = 47.9601520; outside_hi = 110.4111020; sigma = 0.25; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=135237 Gdiag_no=-1 vno start=0, stop=135237 Replacing 255s with 0s #SI# sigma=0.25 had to be increased for 165 vertices, nripped=6776 mean border=81.7, 235 (57) missing vertices, mean dist -0.1 [0.3 (%58.0)->0.2 (%42.0))] %83 local maxima, %12 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0521 min Finding expansion regions mean absolute distance = 0.27 +- 0.42 3228 vertices more than 2 sigmas from mean. Averaging target values for 5 iterations... Positioning Surface: tspring = 0.3, nspring = 0.3, spring = 0, niters = 100 l_repulse = 5, l_surf_repulse = 0, checktol = 0 Positioning pial surface Entering MRISpositionSurface() max_mm = 0.3 MAX_REDUCTIONS = 2, REDUCTION_PCT = 0.5 parms->check_tol = 0, niterations = 100 tol=1.0e-04, sigma=0.2, host=erso., nav=0, nbrs=2, l_repulse=5.000, l_tspring=0.300, l_nspring=0.300, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 000: dt: 0.0000, sse=387893.7, rms=2.560 033: dt: 0.5000, sse=362704.0, rms=2.391 (6.606%) rms = 2.8285/2.3913, sse=417416.9/362704.0, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 034: dt: 0.2500, sse=287147.6, rms=1.636 (31.582%) 035: dt: 0.2500, sse=261442.8, rms=1.472 (10.019%) rms = 1.4308/1.4721, sse=255866.0/261442.8, time step reduction 2 of 3 to 0.125 0 0 1 036: dt: 0.2500, sse=255866.0, rms=1.431 (2.805%) 037: dt: 0.1250, sse=242511.2, rms=1.216 (15.024%) rms = 1.1927/1.2159, sse=239792.8/242511.2, time step reduction 3 of 3 to 0.062 0 0 1 038: dt: 0.1250, sse=239792.8, rms=1.193 (1.906%) maximum number of reductions reached, breaking from loop positioning took 0.5 minutes Writing output to ../surf/lh.white.preaparc #ET# mris_place_surface 3.69 minutes #VMPC# mris_place_surfaces VmPeak 2427248 mris_place_surface done @#@FSTIME 2022:02:17:09:22:38 mris_place_surface N 18 e 323.66 S 0.57 U 322.24 P 99% M 2161020 F 0 R 441292 W 0 c 4125 w 277 I 9512 O 9520 L 1.05 1.22 1.17 @#@FSLOADPOST 2022:02:17:09:28:02 mris_place_surface N 18 1.10 1.21 1.18 #-------------------------------------------- #@# WhitePreAparc rh Thu Feb 17 09:28:02 EST 2022 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mris_place_surface --adgws-in ../surf/autodet.gw.stats.rh.dat --wm wm.mgz --threads 1 --invol brain.finalsurfs.mgz --rh --i ../surf/rh.orig --o ../surf/rh.white.preaparc --white --seg aseg.presurf.mgz --nsmooth 5 7.2.0 7.2.0 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri setenv SUBJECTS_DIR /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer mris_place_surface --adgws-in ../surf/autodet.gw.stats.rh.dat --wm wm.mgz --threads 1 --invol brain.finalsurfs.mgz --rh --i ../surf/rh.orig --o ../surf/rh.white.preaparc --white --seg aseg.presurf.mgz --nsmooth 5 Reading in input surface ../surf/rh.orig Smoothing surface with 5 iterations removing intersecting faces 000: 2 intersecting step 1 with no progress (num=2, old_num=2) 001: 2 intersecting terminating search with 0 intersecting Area 277158 0.26726 0.06190 0.000079 0.6384 Corner 831474 60.00000 9.34756 0.110542 179.2324 Edge 415737 0.78940 0.11254 0.011153 1.3725 Hinge 415737 6.61695 6.38833 0.000039 178.4475 Not reading in aparc Reading in input volume brain.finalsurfs.mgz Reading in wm volume wm.mgz MRIclipBrightWM(): nthresh=17159, wmmin=5, clip=110 MRIfindBrightNonWM(): 7510 bright non-wm voxels segmented. Masking bright non-wm for white surface MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 Reading in seg volume aseg.presurf.mgz Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=0 MRIcopyHeader(): source has ctab removing 2 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6576, nmarked2=5, nripped=6576 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 MRISripSegs(): -2 2 0.5 ripped 0 vertex 69291: xyz = (48.5344,0.674893,36.5232) oxyz = (48.5344,0.674893,36.5232) wxzy = (48.5344,0.674893,36.5232) pxyz = (0,0,0) CBVO Creating mask 138581 n_averages 4 Iteration 0 ========================================= n_averages=4, current_sigma=2 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6576 removing 2 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6576, nmarked2=5, nripped=6576 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 247 MRISripSegs(): -2 2 0.5 ripped 0 Computing target border values Entering MRIScomputeBorderValues_new(): inside_hi = 120.0000000; border_hi = 109.4111020; border_low = 62.0000000; outside_low = 48.9601520; outside_hi = 109.4111020; sigma = 2; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=138581 Gdiag_no=-1 vno start=0, stop=138581 Replacing 255s with 0s #SI# sigma=2 had to be increased for 232 vertices, nripped=6576 mean border=77.7, 194 (194) missing vertices, mean dist 0.4 [0.6 (%35.9)->0.9 (%64.1))] %69 local maxima, %26 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.1801 min Finding expansion regions mean absolute distance = 0.76 +- 0.92 4566 vertices more than 2 sigmas from mean. Averaging target values for 5 iterations... Positioning Surface: tspring = 0.3, nspring = 0.3, spring = 0, niters = 100 l_repulse = 5, l_surf_repulse = 0, checktol = 0 Positioning pial surface Entering MRISpositionSurface() max_mm = 0.3 MAX_REDUCTIONS = 2, REDUCTION_PCT = 0.5 parms->check_tol = 0, niterations = 100 tol=1.0e-04, sigma=2.0, host=erso., nav=4, nbrs=2, l_repulse=5.000, l_tspring=0.300, l_nspring=0.300, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 complete_dist_mat 0 rms 0 smooth_averages 0 remove_neg 0 ico_order 0 which_surface 0 target_radius 0.000000 nfields 0 scale 0.000000 desired_rms_height 0.000000 momentum 0.000000 nbhd_size 0 max_nbrs 0 niterations 100 nsurfaces 0 SURFACES 3 flags 0 (0) use curv 0 no sulc 0 no rigid align 0 mris->nsize 2 mris->hemisphere 1 randomSeed 0 000: dt: 0.0000, sse=3853192.8, rms=11.849 001: dt: 0.5000, sse=2156223.2, rms=8.725 (26.362%) 002: dt: 0.5000, sse=1364494.5, rms=6.790 (22.178%) 003: dt: 0.5000, sse=958007.0, rms=5.530 (18.557%) 004: dt: 0.5000, sse=725862.8, rms=4.678 (15.406%) 005: dt: 0.5000, sse=598929.6, rms=4.127 (11.788%) 006: dt: 0.5000, sse=535195.4, rms=3.813 (7.592%) 007: dt: 0.5000, sse=503559.6, rms=3.647 (4.362%) 008: dt: 0.5000, sse=488971.0, rms=3.564 (2.275%) rms = 3.5251/3.5642, sse=482936.0/488971.0, time step reduction 1 of 3 to 0.250 0 0 1 009: dt: 0.5000, sse=482936.0, rms=3.525 (1.097%) 010: dt: 0.2500, sse=266065.3, rms=2.021 (42.677%) 011: dt: 0.2500, sse=229041.3, rms=1.636 (19.014%) 012: dt: 0.2500, sse=219124.1, rms=1.516 (7.357%) 013: dt: 0.2500, sse=215320.2, rms=1.462 (3.573%) rms = 1.4294/1.4619, sse=213426.8/215320.2, time step reduction 2 of 3 to 0.125 0 0 1 014: dt: 0.2500, sse=213426.8, rms=1.429 (2.226%) 015: dt: 0.1250, sse=208977.8, rms=1.372 (3.992%) rms = 1.3718/1.3723, sse=209102.7/208977.7, time step reduction 3 of 3 to 0.062 0 1 1 016: dt: 0.1250, sse=209102.7, rms=1.372 (0.035%) maximum number of reductions reached, breaking from loop positioning took 1.3 minutes Iteration 1 ========================================= n_averages=2, current_sigma=1 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6576 removing 3 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 removing 4 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 removing 4 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6788, nmarked2=5, nripped=6788 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 247 247 MRISripSegs(): -2 2 0.5 ripped 0 Computing target border values Entering MRIScomputeBorderValues_new(): inside_hi = 120.0000000; border_hi = 109.4111020; border_low = 62.0000000; outside_low = 48.9601520; outside_hi = 109.4111020; sigma = 1; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=138581 Gdiag_no=-1 vno start=0, stop=138581 Replacing 255s with 0s #SI# sigma=1 had to be increased for 174 vertices, nripped=6788 mean border=79.0, 194 (35) missing vertices, mean dist -0.1 [0.4 (%56.9)->0.2 (%43.1))] %69 local maxima, %26 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.1303 min Finding expansion regions mean absolute distance = 0.35 +- 0.76 3625 vertices more than 2 sigmas from mean. Averaging target values for 5 iterations... Positioning Surface: tspring = 0.3, nspring = 0.3, spring = 0, niters = 100 l_repulse = 5, l_surf_repulse = 0, checktol = 0 Positioning pial surface Entering MRISpositionSurface() max_mm = 0.3 MAX_REDUCTIONS = 2, REDUCTION_PCT = 0.5 parms->check_tol = 0, niterations = 100 tol=1.0e-04, sigma=1.0, host=erso., nav=2, nbrs=2, l_repulse=5.000, l_tspring=0.300, l_nspring=0.300, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 000: dt: 0.0000, sse=629540.4, rms=3.965 017: dt: 0.5000, sse=445402.9, rms=2.942 (25.812%) rms = 3.0984/2.9418, sse=467423.6/445402.9, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 018: dt: 0.2500, sse=342297.0, rms=2.184 (25.759%) 019: dt: 0.2500, sse=294645.9, rms=1.703 (22.022%) 020: dt: 0.2500, sse=278909.2, rms=1.512 (11.196%) 021: dt: 0.2500, sse=275824.0, rms=1.412 (6.667%) 022: dt: 0.2500, sse=264615.0, rms=1.341 (4.985%) rms = 1.2915/1.3412, sse=262786.6/264615.0, time step reduction 2 of 3 to 0.125 0 0 1 023: dt: 0.2500, sse=262786.6, rms=1.292 (3.702%) rms = 1.2475/1.2915, sse=258909.9/262786.6, time step reduction 3 of 3 to 0.062 0 0 1 024: dt: 0.1250, sse=258909.9, rms=1.248 (3.407%) maximum number of reductions reached, breaking from loop positioning took 0.7 minutes Iteration 2 ========================================= n_averages=1, current_sigma=0.5 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6788 removing 3 vertices from ripped group in thread:0 removing 4 vertices from ripped group in thread:0 removing 4 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 removing 2 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6813, nmarked2=5, nripped=6813 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 247 247 247 MRISripSegs(): -2 2 0.5 ripped 0 Computing target border values Entering MRIScomputeBorderValues_new(): inside_hi = 120.0000000; border_hi = 109.4111020; border_low = 62.0000000; outside_low = 48.9601520; outside_hi = 109.4111020; sigma = 0.5; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=138581 Gdiag_no=-1 vno start=0, stop=138581 Replacing 255s with 0s #SI# sigma=0.5 had to be increased for 148 vertices, nripped=6813 mean border=81.3, 183 (23) missing vertices, mean dist -0.2 [0.4 (%65.1)->0.2 (%34.9))] %78 local maxima, %17 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0748 min Finding expansion regions mean absolute distance = 0.33 +- 0.54 4827 vertices more than 2 sigmas from mean. Averaging target values for 5 iterations... Positioning Surface: tspring = 0.3, nspring = 0.3, spring = 0, niters = 100 l_repulse = 5, l_surf_repulse = 0, checktol = 0 Positioning pial surface Entering MRISpositionSurface() max_mm = 0.3 MAX_REDUCTIONS = 2, REDUCTION_PCT = 0.5 parms->check_tol = 0, niterations = 100 tol=1.0e-04, sigma=0.5, host=erso., nav=1, nbrs=2, l_repulse=5.000, l_tspring=0.300, l_nspring=0.300, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 000: dt: 0.0000, sse=584844.4, rms=3.743 025: dt: 0.5000, sse=410902.0, rms=2.731 (27.024%) rms = 2.9022/2.7311, sse=441807.4/410902.0, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 026: dt: 0.2500, sse=316544.4, rms=1.970 (27.885%) 027: dt: 0.2500, sse=269420.2, rms=1.434 (27.194%) 028: dt: 0.2500, sse=255144.8, rms=1.230 (14.196%) 029: dt: 0.2500, sse=253358.3, rms=1.171 (4.825%) rms = 1.1459/1.1710, sse=252960.2/253358.3, time step reduction 2 of 3 to 0.125 0 0 1 030: dt: 0.2500, sse=252960.2, rms=1.146 (2.142%) rms = 1.0962/1.1459, sse=249191.6/252960.2, time step reduction 3 of 3 to 0.062 0 0 1 031: dt: 0.1250, sse=249191.6, rms=1.096 (4.343%) maximum number of reductions reached, breaking from loop positioning took 0.6 minutes Iteration 3 ========================================= n_averages=0, current_sigma=0.25 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6813 removing 4 vertices from ripped group in thread:0 removing 4 vertices from ripped group in thread:0 removing 2 vertices from ripped group in thread:0 removing 4 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 removing 4 vertices from ripped group in thread:0 removing 1 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6828, nmarked2=5, nripped=6828 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 247 247 247 247 MRISripSegs(): -2 2 0.5 ripped 0 Computing target border values Entering MRIScomputeBorderValues_new(): inside_hi = 120.0000000; border_hi = 109.4111020; border_low = 62.0000000; outside_low = 48.9601520; outside_hi = 109.4111020; sigma = 0.25; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=138581 Gdiag_no=-1 vno start=0, stop=138581 Replacing 255s with 0s #SI# sigma=0.25 had to be increased for 132 vertices, nripped=6828 mean border=82.5, 197 (14) missing vertices, mean dist -0.1 [0.3 (%59.0)->0.2 (%41.0))] %84 local maxima, %11 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0498 min Finding expansion regions mean absolute distance = 0.29 +- 0.41 4119 vertices more than 2 sigmas from mean. Averaging target values for 5 iterations... Positioning Surface: tspring = 0.3, nspring = 0.3, spring = 0, niters = 100 l_repulse = 5, l_surf_repulse = 0, checktol = 0 Positioning pial surface Entering MRISpositionSurface() max_mm = 0.3 MAX_REDUCTIONS = 2, REDUCTION_PCT = 0.5 parms->check_tol = 0, niterations = 100 tol=1.0e-04, sigma=0.2, host=erso., nav=0, nbrs=2, l_repulse=5.000, l_tspring=0.300, l_nspring=0.300, l_intensity=0.200, l_curv=1.000 mom=0.00, dt=0.50 000: dt: 0.0000, sse=356483.0, rms=2.343 032: dt: 0.5000, sse=313566.4, rms=1.886 (19.498%) rms = 2.3759/1.8862, sse=358755.1/313566.4, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 033: dt: 0.2500, sse=252525.9, rms=1.237 (34.399%) 034: dt: 0.2500, sse=238946.7, rms=1.033 (16.554%) 035: dt: 0.2500, sse=233813.0, rms=0.933 (9.683%) rms = 0.9676/0.9326, sse=238305.9/233813.1, time step reduction 2 of 3 to 0.125 0 1 1 RMS increased, rejecting step rms = 0.9067/0.9326, sse=232570.6/233813.1, time step reduction 3 of 3 to 0.062 0 0 1 036: dt: 0.1250, sse=232570.6, rms=0.907 (2.774%) maximum number of reductions reached, breaking from loop positioning took 0.5 minutes Writing output to ../surf/rh.white.preaparc #ET# mris_place_surface 3.58 minutes #VMPC# mris_place_surfaces VmPeak 2191108 mris_place_surface done @#@FSTIME 2022:02:17:09:28:02 mris_place_surface N 18 e 225.71 S 0.49 U 224.49 P 99% M 1924908 F 0 R 376625 W 0 c 3781 w 343 I 9752 O 9752 L 1.10 1.21 1.18 @#@FSLOADPOST 2022:02:17:09:31:48 mris_place_surface N 18 1.44 1.25 1.20 #-------------------------------------------- #@# CortexLabel lh Thu Feb 17 09:31:48 EST 2022 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_label2label --label-cortex ../surf/lh.white.preaparc aseg.presurf.mgz 0 ../label/lh.cortex.label Generating cortex label... RemoveHipAmgy=0 NucAccIsMedialWall=0 mris->useRealRAS=0 MRIcopyHeader(): source has ctab 13 non-cortical segments detected only using segment with 7740 vertices erasing segment 1 (vno[0] = 36706) erasing segment 2 (vno[0] = 45213) erasing segment 3 (vno[0] = 46714) erasing segment 4 (vno[0] = 51130) erasing segment 5 (vno[0] = 75869) erasing segment 6 (vno[0] = 77362) erasing segment 7 (vno[0] = 77835) erasing segment 8 (vno[0] = 78390) erasing segment 9 (vno[0] = 80565) erasing segment 10 (vno[0] = 112159) erasing segment 11 (vno[0] = 114899) erasing segment 12 (vno[0] = 119387) @#@FSTIME 2022:02:17:09:31:48 mri_label2label N 5 e 13.07 S 0.04 U 12.66 P 97% M 356332 F 2 R 37813 W 0 c 205 w 199 I 296 O 11080 L 1.44 1.25 1.20 @#@FSLOADPOST 2022:02:17:09:32:01 mri_label2label N 5 1.34 1.23 1.19 #-------------------------------------------- #@# CortexLabel+HipAmyg lh Thu Feb 17 09:32:01 EST 2022 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_label2label --label-cortex ../surf/lh.white.preaparc aseg.presurf.mgz 1 ../label/lh.cortex+hipamyg.label Generating cortex label... RemoveHipAmgy=1 NucAccIsMedialWall=0 mris->useRealRAS=0 MRIcopyHeader(): source has ctab 20 non-cortical segments detected only using segment with 5574 vertices erasing segment 1 (vno[0] = 36706) erasing segment 2 (vno[0] = 43892) erasing segment 3 (vno[0] = 45213) erasing segment 4 (vno[0] = 45916) erasing segment 5 (vno[0] = 46714) erasing segment 6 (vno[0] = 48137) erasing segment 7 (vno[0] = 51130) erasing segment 8 (vno[0] = 57873) erasing segment 9 (vno[0] = 70837) erasing segment 10 (vno[0] = 75869) erasing segment 11 (vno[0] = 77362) erasing segment 12 (vno[0] = 77835) erasing segment 13 (vno[0] = 78390) erasing segment 14 (vno[0] = 80565) erasing segment 15 (vno[0] = 112159) erasing segment 16 (vno[0] = 114899) erasing segment 17 (vno[0] = 119387) erasing segment 18 (vno[0] = 123503) erasing segment 19 (vno[0] = 132244) @#@FSTIME 2022:02:17:09:32:02 mri_label2label N 5 e 12.83 S 0.08 U 12.63 P 99% M 389424 F 0 R 41058 W 0 c 180 w 256 I 0 O 11272 L 1.34 1.23 1.19 @#@FSLOADPOST 2022:02:17:09:32:14 mri_label2label N 5 1.29 1.23 1.19 #-------------------------------------------- #@# CortexLabel rh Thu Feb 17 09:32:15 EST 2022 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_label2label --label-cortex ../surf/rh.white.preaparc aseg.presurf.mgz 0 ../label/rh.cortex.label Generating cortex label... RemoveHipAmgy=0 NucAccIsMedialWall=0 mris->useRealRAS=0 MRIcopyHeader(): source has ctab 8 non-cortical segments detected only using segment with 7792 vertices erasing segment 1 (vno[0] = 62078) erasing segment 2 (vno[0] = 76166) erasing segment 3 (vno[0] = 76764) erasing segment 4 (vno[0] = 80506) erasing segment 5 (vno[0] = 83541) erasing segment 6 (vno[0] = 97056) erasing segment 7 (vno[0] = 117748) @#@FSTIME 2022:02:17:09:32:15 mri_label2label N 5 e 13.13 S 0.05 U 12.96 P 99% M 337020 F 0 R 36530 W 0 c 220 w 260 I 0 O 11088 L 1.29 1.23 1.19 @#@FSLOADPOST 2022:02:17:09:32:28 mri_label2label N 5 1.38 1.25 1.20 #-------------------------------------------- #@# CortexLabel+HipAmyg rh Thu Feb 17 09:32:28 EST 2022 cd /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/mri mri_label2label --label-cortex ../surf/rh.white.preaparc aseg.presurf.mgz 1 ../label/rh.cortex+hipamyg.label Generating cortex label... RemoveHipAmgy=1 NucAccIsMedialWall=0 mris->useRealRAS=0 MRIcopyHeader(): source has ctab 18 non-cortical segments detected only using segment with 5512 vertices erasing segment 1 (vno[0] = 45486) erasing segment 2 (vno[0] = 50548) erasing segment 3 (vno[0] = 51396) erasing segment 4 (vno[0] = 51423) erasing segment 5 (vno[0] = 51446) erasing segment 6 (vno[0] = 54393) erasing segment 7 (vno[0] = 57201) erasing segment 8 (vno[0] = 62078) erasing segment 9 (vno[0] = 67071) erasing segment 10 (vno[0] = 76166) erasing segment 11 (vno[0] = 76764) erasing segment 12 (vno[0] = 80506) erasing segment 13 (vno[0] = 83541) erasing segment 14 (vno[0] = 97056) erasing segment 15 (vno[0] = 113218) erasing segment 16 (vno[0] = 117748) erasing segment 17 (vno[0] = 137138) @#@FSTIME 2022:02:17:09:32:28 mri_label2label N 5 e 13.27 S 0.06 U 13.08 P 99% M 385916 F 0 R 41594 W 0 c 198 w 208 I 0 O 11280 L 1.38 1.25 1.20 @#@FSLOADPOST 2022:02:17:09:32:41 mri_label2label N 5 1.29 1.24 1.19 #-------------------------------------------- #@# Smooth2 lh Thu Feb 17 09:32:41 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_smooth -n 3 -nw -seed 1234 ../surf/lh.white.preaparc ../surf/lh.smoothwm smoothing for 3 iterations setting seed for random number generator to 1234 smoothing surface tessellation for 3 iterations... smoothing complete - recomputing first and second fundamental forms... @#@FSTIME 2022:02:17:09:32:42 mris_smooth N 7 e 2.59 S 0.05 U 2.45 P 96% M 207552 F 0 R 38080 W 0 c 45 w 207 I 0 O 9512 L 1.29 1.24 1.19 @#@FSLOADPOST 2022:02:17:09:32:44 mris_smooth N 7 1.29 1.24 1.19 #-------------------------------------------- #@# Smooth2 rh Thu Feb 17 09:32:44 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_smooth -n 3 -nw -seed 1234 ../surf/rh.white.preaparc ../surf/rh.smoothwm smoothing for 3 iterations setting seed for random number generator to 1234 smoothing surface tessellation for 3 iterations... smoothing complete - recomputing first and second fundamental forms... @#@FSTIME 2022:02:17:09:32:44 mris_smooth N 7 e 2.67 S 0.04 U 2.54 P 96% M 212624 F 0 R 39080 W 0 c 49 w 204 I 0 O 9752 L 1.29 1.24 1.19 @#@FSLOADPOST 2022:02:17:09:32:47 mris_smooth N 7 1.35 1.25 1.20 #-------------------------------------------- #@# Inflation2 lh Thu Feb 17 09:32:47 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_inflate ../surf/lh.smoothwm ../surf/lh.inflated Reading ../surf/lh.smoothwm avg radius = 46.4 mm, total surface area = 83453 mm^2 step 000: RMS=0.176 (target=0.015) step 005: RMS=0.116 (target=0.015) step 010: RMS=0.089 (target=0.015) step 015: RMS=0.073 (target=0.015) step 020: RMS=0.061 (target=0.015) step 025: RMS=0.051 (target=0.015) step 030: RMS=0.043 (target=0.015) step 035: RMS=0.036 (target=0.015) step 040: RMS=0.031 (target=0.015) step 045: RMS=0.027 (target=0.015) step 050: RMS=0.025 (target=0.015) step 055: RMS=0.023 (target=0.015) step 060: RMS=0.022 (target=0.015) writing inflated surface to ../surf/lh.inflated writing sulcal depths to ../surf/lh.sulc inflation complete. inflation took 0.3 minutes mris_inflate utimesec 17.259992 mris_inflate stimesec 0.511848 mris_inflate ru_maxrss 211656 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 411305 mris_inflate ru_majflt 0 mris_inflate ru_nswap 0 mris_inflate ru_inblock 0 mris_inflate ru_oublock 10584 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 238 mris_inflate ru_nivcsw 191 @#@FSTIME 2022:02:17:09:32:47 mris_inflate N 2 e 17.92 S 0.51 U 17.26 P 99% M 211656 F 0 R 411307 W 0 c 191 w 239 I 0 O 10584 L 1.35 1.25 1.20 @#@FSLOADPOST 2022:02:17:09:33:05 mris_inflate N 2 1.25 1.23 1.19 #-------------------------------------------- #@# Inflation2 rh Thu Feb 17 09:33:05 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/scripts mris_inflate ../surf/rh.smoothwm ../surf/rh.inflated Reading ../surf/rh.smoothwm avg radius = 47.1 mm, total surface area = 85546 mm^2 step 000: RMS=0.175 (target=0.015) step 005: RMS=0.115 (target=0.015) step 010: RMS=0.087 (target=0.015) step 015: RMS=0.072 (target=0.015) step 020: RMS=0.059 (target=0.015) step 025: RMS=0.049 (target=0.015) step 030: RMS=0.040 (target=0.015) step 035: RMS=0.034 (target=0.015) step 040: RMS=0.028 (target=0.015) step 045: RMS=0.025 (target=0.015) step 050: RMS=0.022 (target=0.015) step 055: RMS=0.021 (target=0.015) step 060: RMS=0.019 (target=0.015) writing inflated surface to ../surf/rh.inflated writing sulcal depths to ../surf/rh.sulc inflation complete. inflation took 0.3 minutes mris_inflate utimesec 17.752988 mris_inflate stimesec 0.045753 mris_inflate ru_maxrss 217436 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 31657 mris_inflate ru_majflt 0 mris_inflate ru_nswap 0 mris_inflate ru_inblock 0 mris_inflate ru_oublock 10840 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 247 mris_inflate ru_nivcsw 250 @#@FSTIME 2022:02:17:09:33:05 mris_inflate N 2 e 17.96 S 0.05 U 17.75 P 99% M 217660 F 0 R 31659 W 0 c 250 w 248 I 0 O 10840 L 1.25 1.23 1.19 @#@FSLOADPOST 2022:02:17:09:33:23 mris_inflate N 2 1.34 1.25 1.20 #-------------------------------------------- #@# Curv .H and .K lh Thu Feb 17 09:33:23 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf mris_curvature -w -seed 1234 lh.white.preaparc setting seed for random number generator to 1234 total integrated curvature = 24.222*4pi (304.377) --> -23 handles ICI = 173.7, FI = 1566.2, variation=25183.981 writing Gaussian curvature to ./lh.white.preaparc.K...done. writing mean curvature to ./lh.white.preaparc.H...done. @#@FSTIME 2022:02:17:09:33:23 mris_curvature N 4 e 1.39 S 0.02 U 1.29 P 94% M 154172 F 1 R 19786 W 0 c 12 w 107 I 8 O 2128 L 1.34 1.25 1.20 @#@FSLOADPOST 2022:02:17:09:33:25 mris_curvature N 4 1.34 1.25 1.20 rm -f lh.white.H ln -s lh.white.preaparc.H lh.white.H rm -f lh.white.K ln -s lh.white.preaparc.K lh.white.K mris_curvature -seed 1234 -thresh .999 -n -a 5 -w -distances 10 10 lh.inflated setting seed for random number generator to 1234 normalizing curvature values. averaging curvature patterns 5 times. sampling 10 neighbors out to a distance of 10 mm 165 vertices thresholded to be in k1 ~ [-0.60 0.95], k2 ~ [-0.31 0.14] total integrated curvature = 0.586*4pi (7.366) --> 0 handles ICI = 1.5, FI = 8.2, variation=146.742 148 vertices thresholded to be in [-0.06 0.05] writing Gaussian curvature to ./lh.inflated.K...thresholding curvature at 99.90% level curvature mean = 0.000, std = 0.002 129 vertices thresholded to be in [-0.30 0.16] done. writing mean curvature to ./lh.inflated.H...curvature mean = -0.016, std = 0.023 done. @#@FSTIME 2022:02:17:09:33:25 mris_curvature N 12 e 40.88 S 0.12 U 40.63 P 99% M 365724 F 0 R 71728 W 0 c 553 w 99 I 0 O 2128 L 1.34 1.25 1.20 @#@FSLOADPOST 2022:02:17:09:34:06 mris_curvature N 12 1.29 1.25 1.20 #@# Curv .H and .K rh Thu Feb 17 09:34:06 EST 2022 /autofs/vast/bandlab/studies/kyoto_preliminary/data/derivatives/freesurfer/sub-LALM61_ses-LALM61/surf mris_curvature -w -seed 1234 rh.white.preaparc setting seed for random number generator to 1234 total integrated curvature = 26.759*4pi (336.266) --> -26 handles ICI = 173.9, FI = 1548.0, variation=24893.111 writing Gaussian curvature to ./rh.white.preaparc.K...done. writing mean curvature to ./rh.white.preaparc.H...done. @#@FSTIME 2022:02:17:09:34:06 mris_curvature N 4 e 1.41 S 0.03 U 1.30 P 94% M 157860 F 0 R 20801 W 0 c 20 w 53 I 0 O 2176 L 1.29 1.25 1.20 @#@FSLOADPOST 2022:02:17:09:34:07 mris_curvature N 4 1.29 1.25 1.20 Linux erso.nmr.mgh.harvard.edu 4.18.0-365.el8.x86_64 #1 SMP Thu Feb 10 16:11:23 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux recon-all -s sub-LALM61_ses-LALM61 exited with ERRORS at Thu Feb 17 09:34:08 EST 2022 To report a problem, see http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting