Wed Jan 12 16:40:36 CST 2022 cd /home/valia/mmvt_root/subjects/UMNC03 setenv SUBJECTS_DIR /home/valia/mmvt_root/subjects /usr/local/freesurfer/7.2.0-1/bin/recon-all -i /home/valia/mmvt_root/dicoms/Aidan/dicom/4186254_MEG_0000 -subjid UMNC03 -all -parallel subjid UMNC03 setenv SUBJECTS_DIR /home/valia/mmvt_root/subjects FREESURFER_HOME /usr/local/freesurfer/7.2.0-1 Actual FREESURFER_HOME /usr/local/freesurfer/7.2.0-1 build-stamp.txt: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b Linux faraday 4.18.0-326.el8.x86_64 #1 SMP Wed Jul 28 21:21:05 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux cputime unlimited filesize unlimited datasize unlimited stacksize 8192 kbytes coredumpsize 0 kbytes memoryuse unlimited vmemoryuse unlimited descriptors 1024 memorylocked 64 kbytes maxproc 30379 maxlocks unlimited maxsignal 30379 maxmessage 819200 maxnice 0 maxrtprio 0 maxrttime unlimited total used free shared buff/cache available Mem: 7.5Gi 3.3Gi 306Mi 946Mi 3.9Gi 2.9Gi Swap: 15Gi 77Mi 15Gi ######################################## program versions used 7.2.0 (freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b) 7.2.0 ProgramName: lta_convert ProgramArguments: lta_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_and ProgramArguments: mri_and -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_annotation2label ProgramArguments: mri_annotation2label -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_aparc2aseg ProgramArguments: mri_aparc2aseg -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_surf2volseg ProgramArguments: mri_surf2volseg -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_binarize ProgramArguments: mri_binarize -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_ca_label ProgramArguments: mri_ca_label -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_ca_normalize ProgramArguments: mri_ca_normalize -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_ca_register ProgramArguments: mri_ca_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_cc ProgramArguments: mri_cc -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_compute_overlap ProgramArguments: mri_compute_overlap -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_compute_seg_overlap ProgramArguments: mri_compute_seg_overlap -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_concat ProgramArguments: mri_concat -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_concatenate_lta ProgramArguments: mri_concatenate_lta -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 mri_convert -all-info ProgramName: mri_convert ProgramArguments: mri_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_diff ProgramArguments: mri_diff -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_edit_wm_with_aseg ProgramArguments: mri_edit_wm_with_aseg -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_em_register ProgramArguments: mri_em_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_fill ProgramArguments: mri_fill -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_fuse_segmentations ProgramArguments: mri_fuse_segmentations -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_fwhm ProgramArguments: mri_fwhm -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_gcut ProgramArguments: mri_gcut -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_info ProgramArguments: mri_info -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_label2label ProgramArguments: mri_label2label -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_label2vol ProgramArguments: mri_label2vol -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_log_likelihood ProgramArguments: mri_log_likelihood -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_mask ProgramArguments: mri_mask -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_matrix_multiply ProgramArguments: mri_matrix_multiply -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_normalize ProgramArguments: mri_normalize -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_normalize_tp2 ProgramArguments: mri_normalize_tp2 -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_or ProgramArguments: mri_or -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_relabel_hypointensities ProgramArguments: mri_relabel_hypointensities -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_relabel_nonwm_hypos ProgramArguments: mri_relabel_nonwm_hypos -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_remove_neck ProgramArguments: mri_remove_neck -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 7.2.0 ProgramName: mri_robust_register ProgramArguments: mri_robust_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 7.2.0 ProgramName: mri_robust_template ProgramArguments: mri_robust_template -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_anatomical_stats ProgramArguments: mris_anatomical_stats -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_ca_label ProgramArguments: mris_ca_label -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_calc ProgramArguments: mris_calc -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_convert ProgramArguments: mris_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_curvature ProgramArguments: mris_curvature -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_curvature_stats ProgramArguments: mris_curvature_stats -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_diff ProgramArguments: mris_diff -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_divide_parcellation ProgramArguments: mris_divide_parcellation -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_segment ProgramArguments: mri_segment -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_segstats ProgramArguments: mri_segstats -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_euler_number ProgramArguments: mris_euler_number -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_fix_topology ProgramArguments: mris_fix_topology -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_topo_fixer ProgramArguments: mris_topo_fixer -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_jacobian ProgramArguments: mris_jacobian -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_label2annot ProgramArguments: mris_label2annot -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_left_right_register ProgramArguments: mris_left_right_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_place_surface ProgramArguments: mris_place_surface -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mrisp_paint ProgramArguments: mrisp_paint -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_register ProgramArguments: mris_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_smooth ProgramArguments: mris_smooth -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_sphere ProgramArguments: mris_sphere -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_surface_stats ProgramArguments: mris_surface_stats -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_stats2seg ProgramArguments: mri_stats2seg -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_thickness ProgramArguments: mris_thickness -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_thickness_diff ProgramArguments: mris_thickness_diff -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_topo_fixer ProgramArguments: mris_topo_fixer -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_surf2surf ProgramArguments: mri_surf2surf -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_surf2vol ProgramArguments: mri_surf2vol -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_surfcluster ProgramArguments: mri_surfcluster -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mris_volmask ProgramArguments: mris_volmask -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_tessellate ProgramArguments: mri_tessellate -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_vol2surf ProgramArguments: mri_vol2surf -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_vol2vol ProgramArguments: mri_vol2vol -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_voldiff ProgramArguments: mri_voldiff -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: mri_watershed ProgramArguments: mri_watershed -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 ProgramName: tkregister2 ProgramArguments: tkregister2_cmdl -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 mri_motion_correct.fsl 7.2.0 mri_convert -all-info ProgramName: mri_convert ProgramArguments: mri_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2022/01/12-22:40:36-GMT BuildTime: Jul 20 2021 21:45:58 BuildStamp: freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b User: valia Machine: faraday Platform: Linux PlatformVersion: 4.18.0-326.el8.x86_64 CompilerName: GCC CompilerVersion: 8.4.1 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 /usr/local/freesurfer/7.2.0-1/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 /usr/local/freesurfer/7.2.0-1/average GCS DKaparc.atlas.acfb40.noaparc.i12.2016-08-02.gcs ####################################### /home/valia/mmvt_root/subjects/UMNC03 mri_convert /home/valia/mmvt_root/dicoms/Aidan/dicom/4186254_MEG_0000 /home/valia/mmvt_root/subjects/UMNC03/mri/orig/001.mgz mri_convert /home/valia/mmvt_root/dicoms/Aidan/dicom/4186254_MEG_0000 /home/valia/mmvt_root/subjects/UMNC03/mri/orig/001.mgz reading from /home/valia/mmvt_root/dicoms/Aidan/dicom/4186254_MEG_0000... Starting DICOMRead2() dcmfile = /home/valia/mmvt_root/dicoms/Aidan/dicom/4186254_MEG_0000 dcmdir = /home/valia/mmvt_root/dicoms/Aidan/dicom Ref Series No = 601 Found 167 files, checking for dicoms Found 165 dicom files in series. First Sorting Computing Slice Direction Vs: -0.0396967 -0.23757 0.970558 Vs: -0.0396967 -0.23757 0.970559 Second Sorting IsDWI = 0, IsPhilipsDWI = 0 Counting frames nframes = 1 nslices = 165 ndcmfiles = 165 INFO: applying rescale intercept and slope based on (0028,1052) (0028,1053). If you do not want this, then set FS_RESCALE_DICOM to 0 and rerun. PE Dir = ROW (dicom read) Loading pixel data TR=6.91, TE=3.50, TI=0.00, flip angle=8.00 i_ras = (-0.997809, -0.0420269, 0.0510983) j_ras = (0.052929, -0.970461, 0.235382) k_ras = (0.0396967, 0.23757, 0.970559) writing to /home/valia/mmvt_root/subjects/UMNC03/mri/orig/001.mgz... @#@FSTIME 2022:01:12:16:40:36 mri_convert N 2 e 10.12 S 0.42 U 10.88 P 111% M 178640 F 0 R 42878 W 0 c 933 w 7 I 0 O 57024 L 1.83 1.72 1.62 @#@FSLOADPOST 2022:01:12:16:40:46 mri_convert N 2 2.01 1.76 1.64 #-------------------------------------------- #@# MotionCor Wed Jan 12 16:40:48 CST 2022 Found 1 runs /home/valia/mmvt_root/subjects/UMNC03/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 /home/valia/mmvt_root/subjects/UMNC03/mri/orig/001.mgz /home/valia/mmvt_root/subjects/UMNC03/mri/rawavg.mgz /home/valia/mmvt_root/subjects/UMNC03 mri_convert /home/valia/mmvt_root/subjects/UMNC03/mri/rawavg.mgz /home/valia/mmvt_root/subjects/UMNC03/mri/orig.mgz --conform mri_convert /home/valia/mmvt_root/subjects/UMNC03/mri/rawavg.mgz /home/valia/mmvt_root/subjects/UMNC03/mri/orig.mgz --conform reading from /home/valia/mmvt_root/subjects/UMNC03/mri/rawavg.mgz... TR=6.91, TE=3.50, TI=0.00, flip angle=8.00 i_ras = (-0.997809, -0.0420269, 0.0510983) j_ras = (0.052929, -0.970461, 0.235382) k_ras = (0.0396967, 0.23757, 0.970559) changing data type from float to uchar (noscale = 0)... MRIchangeType: Building histogram 0 11483.9 1000, flo=0, fhi=0.999, dest_type=0 Reslicing using trilinear interpolation writing to /home/valia/mmvt_root/subjects/UMNC03/mri/orig.mgz... @#@FSTIME 2022:01:12:16:40:52 mri_convert N 3 e 18.42 S 0.12 U 19.47 P 106% M 218940 F 0 R 57762 W 0 c 447 w 5 I 0 O 7296 L 1.85 1.73 1.63 @#@FSLOADPOST 2022:01:12:16:41:10 mri_convert N 3 2.32 1.84 1.67 mri_add_xform_to_header -c /home/valia/mmvt_root/subjects/UMNC03/mri/transforms/talairach.xfm /home/valia/mmvt_root/subjects/UMNC03/mri/orig.mgz /home/valia/mmvt_root/subjects/UMNC03/mri/orig.mgz INFO: extension is mgz @#@FSTIME 2022:01:12:16:41:10 mri_add_xform_to_header N 4 e 0.51 S 0.01 U 0.88 P 176% M 23128 F 2 R 4038 W 0 c 152 w 9 I 5656 O 7296 L 2.32 1.84 1.67 @#@FSLOADPOST 2022:01:12:16:41:11 mri_add_xform_to_header N 4 2.54 1.90 1.68 #-------------------------------------------- #@# Talairach Wed Jan 12 16:41:11 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/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 /home/valia/mmvt_root/subjects/UMNC03/mri /usr/local/freesurfer/7.2.0-1/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 faraday 4.18.0-326.el8.x86_64 #1 SMP Wed Jul 28 21:21:05 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux Wed Jan 12 16:41:11 CST 2022 tmpdir is ./tmp.mri_nu_correct.mni.3037148 cd /home/valia/mmvt_root/subjects/UMNC03/mri AntsN4BiasFieldCorrectionFs -i orig.mgz -o ./tmp.mri_nu_correct.mni.3037148/nu0.mgz --dtype uchar AntsN4BiasFieldCorrectionFs done mri_convert ./tmp.mri_nu_correct.mni.3037148/nu0.mgz orig_nu.mgz --like orig.mgz --conform mri_convert ./tmp.mri_nu_correct.mni.3037148/nu0.mgz orig_nu.mgz --like orig.mgz --conform reading from ./tmp.mri_nu_correct.mni.3037148/nu0.mgz... TR=6.91, TE=3.50, TI=0.00, flip angle=8.00 i_ras = (-1, 1.86265e-09, 0) j_ras = (0, 0, -1) k_ras = (9.31323e-10, 1, 2.98023e-08) INFO: transform src into the like-volume: orig.mgz writing to orig_nu.mgz... Wed Jan 12 16:44:52 CST 2022 mri_nu_correct.mni done @#@FSTIME 2022:01:12:16:41:11 mri_nu_correct.mni N 12 e 220.89 S 0.51 U 218.86 P 99% M 504888 F 2 R 150562 W 0 c 9269 w 100 I 7048 O 13872 L 2.54 1.90 1.68 @#@FSLOADPOST 2022:01:12:16:44:52 mri_nu_correct.mni N 12 2.77 2.31 1.90 talairach_avi --i orig_nu.mgz --xfm transforms/talairach.auto.xfm talairach_avi log file is transforms/talairach_avi.log... Started at Wed Jan 12 16:44:52 CST 2022 Ended at Wed Jan 12 16:45:23 CST 2022 talairach_avi done @#@FSTIME 2022:01:12:16:44:52 talairach_avi N 4 e 31.48 S 1.15 U 24.24 P 80% M 256192 F 9 R 394403 W 0 c 930 w 230 I 270624 O 295256 L 2.77 2.31 1.90 @#@FSLOADPOST 2022:01:12:16:45:23 talairach_avi N 4 2.59 2.31 1.92 cp transforms/talairach.auto.xfm transforms/talairach.xfm lta_convert --src orig.mgz --trg /usr/local/freesurfer/7.2.0-1/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: /usr/local/freesurfer/7.2.0-1/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 1.00292 0.05850 -0.01647 4.63899; -0.03520 0.95968 0.02751 -38.68243; 0.02705 0.07117 1.07057 -27.56706; 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 Wed Jan 12 16:45:26 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/mri talairach_afd -T 0.005 -xfm transforms/talairach.xfm talairach_afd: Talairach Transform: transforms/talairach.xfm OK (p=0.5833, pval=0.2817 >= threshold=0.0050) @#@FSTIME 2022:01:12:16:45:26 talairach_afd N 4 e 0.02 S 0.00 U 0.00 P 23% M 5468 F 2 R 195 W 0 c 0 w 11 I 5816 O 0 L 2.78 2.36 1.93 @#@FSLOADPOST 2022:01:12:16:45:26 talairach_afd N 4 2.78 2.36 1.93 awk -f /usr/local/freesurfer/7.2.0-1/bin/extract_talairach_avi_QA.awk /home/valia/mmvt_root/subjects/UMNC03/mri/transforms/talairach_avi.log tal_QC_AZS /home/valia/mmvt_root/subjects/UMNC03/mri/transforms/talairach_avi.log TalAviQA: 0.97619 z-score: 0 #-------------------------------------------- #@# Nu Intensity Correction Wed Jan 12 16:45:26 CST 2022 mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --n 2 --ants-n4 /usr/bin/bc /home/valia/mmvt_root/subjects/UMNC03/mri /usr/local/freesurfer/7.2.0-1/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 faraday 4.18.0-326.el8.x86_64 #1 SMP Wed Jul 28 21:21:05 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux Wed Jan 12 16:45:26 CST 2022 tmpdir is ./tmp.mri_nu_correct.mni.3037571 cd /home/valia/mmvt_root/subjects/UMNC03/mri AntsN4BiasFieldCorrectionFs -i orig.mgz -o ./tmp.mri_nu_correct.mni.3037571/nu0.mgz --dtype uchar AntsN4BiasFieldCorrectionFs done mri_binarize --i ./tmp.mri_nu_correct.mni.3037571/nu0.mgz --min -1 --o ./tmp.mri_nu_correct.mni.3037571/ones.mgz 7.2.0 cwd /home/valia/mmvt_root/subjects/UMNC03/mri cmdline mri_binarize --i ./tmp.mri_nu_correct.mni.3037571/nu0.mgz --min -1 --o ./tmp.mri_nu_correct.mni.3037571/ones.mgz sysname Linux hostname faraday machine x86_64 user valia input ./tmp.mri_nu_correct.mni.3037571/nu0.mgz frame 0 nErode3d 0 nErode2d 0 output ./tmp.mri_nu_correct.mni.3037571/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.3037571/ones.mgz Count: 16777215 16777215.000000 16777216 99.999994 mri_binarize done mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.3037571/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.3037571/sum.junk --avgwf ./tmp.mri_nu_correct.mni.3037571/input.mean.dat 7.2.0 cwd cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.3037571/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.3037571/sum.junk --avgwf ./tmp.mri_nu_correct.mni.3037571/input.mean.dat sysname Linux hostname faraday machine x86_64 user valia whitesurfname white UseRobust 0 Loading ./tmp.mri_nu_correct.mni.3037571/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.3037571/input.mean.dat mri_segstats done mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.3037571/ones.mgz --i ./tmp.mri_nu_correct.mni.3037571/nu0.mgz --sum ./tmp.mri_nu_correct.mni.3037571/sum.junk --avgwf ./tmp.mri_nu_correct.mni.3037571/output.mean.dat 7.2.0 cwd cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.3037571/ones.mgz --i ./tmp.mri_nu_correct.mni.3037571/nu0.mgz --sum ./tmp.mri_nu_correct.mni.3037571/sum.junk --avgwf ./tmp.mri_nu_correct.mni.3037571/output.mean.dat sysname Linux hostname faraday machine x86_64 user valia whitesurfname white UseRobust 0 Loading ./tmp.mri_nu_correct.mni.3037571/ones.mgz Loading ./tmp.mri_nu_correct.mni.3037571/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.3037571/output.mean.dat mri_segstats done mris_calc -o ./tmp.mri_nu_correct.mni.3037571/nu0.mgz ./tmp.mri_nu_correct.mni.3037571/nu0.mgz mul 1.22708568417446396981 Saving result to './tmp.mri_nu_correct.mni.3037571/nu0.mgz' (type = MGH ) [ ok ] mri_convert ./tmp.mri_nu_correct.mni.3037571/nu0.mgz nu.mgz --like orig.mgz mri_convert ./tmp.mri_nu_correct.mni.3037571/nu0.mgz nu.mgz --like orig.mgz reading from ./tmp.mri_nu_correct.mni.3037571/nu0.mgz... TR=6.91, TE=3.50, TI=0.00, flip angle=8.00 i_ras = (-1, 1.86265e-09, 0) j_ras = (0, 0, -1) k_ras = (9.31323e-10, 1, 2.98023e-08) 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 6 seconds. mapping ( 8, 131) to ( 3, 110) Wed Jan 12 16:49:29 CST 2022 mri_nu_correct.mni done @#@FSTIME 2022:01:12:16:45:26 mri_nu_correct.mni N 9 e 243.74 S 1.12 U 247.10 P 101% M 613824 F 2 R 553062 W 0 c 3939 w 200 I 5048 O 35720 L 2.78 2.36 1.93 @#@FSLOADPOST 2022:01:12:16:49:29 mri_nu_correct.mni N 9 3.18 2.70 2.17 mri_add_xform_to_header -c /home/valia/mmvt_root/subjects/UMNC03/mri/transforms/talairach.xfm nu.mgz nu.mgz INFO: extension is mgz @#@FSTIME 2022:01:12:16:49:29 mri_add_xform_to_header N 4 e 0.46 S 0.01 U 0.87 P 193% M 23152 F 1 R 4549 W 0 c 85 w 5 I 24 O 6320 L 3.18 2.70 2.17 @#@FSLOADPOST 2022:01:12:16:49:30 mri_add_xform_to_header N 4 3.18 2.70 2.17 #-------------------------------------------- #@# Intensity Normalization Wed Jan 12 16:49:30 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/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 1.00292 0.05850 -0.01647 4.63899; -0.03520 0.95968 0.02751 -38.68243; 0.02705 0.07117 1.07057 -27.56706; 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 = 21 Starting OpenSpline(): npoints = 21 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 109 gm peak at 64 (64), valley at 49 (49) csf peak at 31, setting threshold to 53 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 64 (64), valley at 47 (47) csf peak at 30, setting threshold to 52 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Done iterating --------------------------------- writing output to T1.mgz 3D bias adjustment took 1 minutes and 13 seconds. @#@FSTIME 2022:01:12:16:49:30 mri_normalize N 7 e 73.31 S 0.22 U 84.43 P 115% M 583668 F 0 R 94268 W 0 c 2491 w 19 I 0 O 6104 L 3.18 2.70 2.17 @#@FSLOADPOST 2022:01:12:16:50:43 mri_normalize N 7 2.50 2.63 2.19 #-------------------------------------------- #@# Skull Stripping Wed Jan 12 16:50:43 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/mri mri_em_register -skull nu.mgz /usr/local/freesurfer/7.2.0-1/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 = 4 == reading 1 input volumes... logging results to talairach_with_skull.log reading '/usr/local/freesurfer/7.2.0-1/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=13.0 skull bounding box = (48, 45, 20) --> (207, 213, 231) finding center of left hemi white matter using (101, 101, 126) as brain centroid of Right_Cerebral_White_Matter... MRImask(): AllowDiffGeom = 1 mean wm in atlas = 108, using box (81,80,100) --> (120, 121,152) to find MRI wm before smoothing, mri peak at 108 robust fit to distribution - 108 +- 5.2 after smoothing, mri peak at 108, scaling input intensities by 1.000 scaling channel 0 by 1 initial log_p = -4.408 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.307530 @ (-10.526, -10.526, -10.526) max log p = -4.032493 @ (5.263, 5.263, -5.263) max log p = -4.032493 @ (0.000, 0.000, 0.000) max log p = -4.015950 @ (1.316, -1.316, 1.316) max log p = -3.996497 @ (0.658, -0.658, 1.974) max log p = -3.996497 @ (0.000, 0.000, 0.000) max log p = -3.996497 @ (0.000, 0.000, 0.000) max log p = -3.996497 @ (0.000, 0.000, 0.000) Found translation: (-3.3, -7.2, -12.5): log p = -3.996 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.988, old_max_log_p =-3.996 (thresh=-4.0) 1.00000 0.00000 0.00000 -3.28948; 0.00000 1.06580 -0.14032 7.78812; 0.00000 0.13053 0.99144 -30.93571; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 22 seconds. **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.957, old_max_log_p =-3.988 (thresh=-4.0) 0.99144 0.00399 -0.13105 13.56671; 0.00000 1.13981 -0.02828 -16.70957; 0.13053 -0.03031 0.99544 -25.28326; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 22 seconds. **************************************** Nine parameter search. iteration 2 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.947, old_max_log_p =-3.957 (thresh=-4.0) 0.99144 0.00399 -0.13105 13.56671; -0.01704 1.13401 -0.15797 2.45954; 0.11970 0.10982 0.90949 -33.14503; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 22 seconds. **************************************** Nine parameter search. iteration 3 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.938, old_max_log_p =-3.947 (thresh=-3.9) 1.07348 0.01966 -0.01206 -14.02202; -0.01704 1.13401 -0.15797 2.45954; -0.01073 0.10836 0.91881 -17.35209; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 23 seconds. **************************************** Nine parameter search. iteration 4 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.938, old_max_log_p =-3.938 (thresh=-3.9) 1.07348 0.01966 -0.01206 -14.02202; -0.01704 1.13401 -0.15797 2.45954; -0.01073 0.10836 0.91881 -17.35209; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 iteration took 0 minutes and 23 seconds. **************************************** Nine parameter search. iteration 5 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.875, old_max_log_p =-3.938 (thresh=-3.9) 1.01234 0.01184 -0.06812 1.92592; -0.01464 1.11514 -0.12313 -1.44343; 0.06340 0.07920 0.97278 -29.31516; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 21 seconds. **************************************** Nine parameter search. iteration 6 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.854, old_max_log_p =-3.875 (thresh=-3.9) 0.97438 0.01139 -0.06557 6.54504; -0.01491 1.13605 -0.12544 -2.26005; 0.06340 0.07920 0.97278 -29.31516; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 21 seconds. **************************************** Nine parameter search. iteration 7 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.854, old_max_log_p =-3.854 (thresh=-3.9) 0.97438 0.01139 -0.06557 6.54504; -0.01491 1.13605 -0.12544 -2.26005; 0.06340 0.07920 0.97278 -29.31516; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 iteration took 0 minutes and 21 seconds. **************************************** Nine parameter search. iteration 8 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.847, old_max_log_p =-3.854 (thresh=-3.9) 0.97552 0.01141 -0.06564 6.40611; -0.01490 1.13472 -0.12529 -2.09086; 0.06363 0.07948 0.97620 -29.80943; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 18 seconds. **************************************** Nine parameter search. iteration 9 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.847, old_max_log_p =-3.847 (thresh=-3.8) 0.97552 0.01141 -0.06564 6.40611; -0.01490 1.13472 -0.12529 -2.09086; 0.06370 0.07957 0.97734 -29.97463; 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 0.97552 0.01141 -0.06564 6.40611; -0.01490 1.13472 -0.12529 -2.09086; 0.06370 0.07957 0.97734 -29.97463; 0.00000 0.00000 0.00000 1.00000; nsamples 3292 Quasinewton: input matrix 0.97552 0.01141 -0.06564 6.40611; -0.01490 1.13472 -0.12529 -2.09086; 0.06370 0.07957 0.97734 -29.97463; 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: 0.97552 0.01141 -0.06564 6.40611; -0.01490 1.13472 -0.12529 -2.09086; 0.06370 0.07957 0.97734 -29.97463; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -3.847 (old=-4.408) transform before final EM align: 0.97552 0.01141 -0.06564 6.40611; -0.01490 1.13472 -0.12529 -2.09086; 0.06370 0.07957 0.97734 -29.97463; 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 0.97552 0.01141 -0.06564 6.40611; -0.01490 1.13472 -0.12529 -2.09086; 0.06370 0.07957 0.97734 -29.97463; 0.00000 0.00000 0.00000 1.00000; nsamples 364986 Quasinewton: input matrix 0.97552 0.01141 -0.06564 6.40611; -0.01490 1.13472 -0.12529 -2.09086; 0.06370 0.07957 0.97734 -29.97463; 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) = 4.3 tol 0.000000 final transform: 0.97552 0.01141 -0.06564 6.40611; -0.01490 1.13472 -0.12529 -2.09086; 0.06370 0.07957 0.97734 -29.97463; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach_with_skull.lta... #VMPC# mri_em_register VmPeak 1037132 FSRUNTIME@ mri_em_register 0.0714 hours 4 threads registration took 4 minutes and 17 seconds. @#@FSTIME 2022:01:12:16:50:43 mri_em_register N 4 e 257.22 S 1.26 U 903.29 P 351% M 630804 F 0 R 117856 W 0 c 67840 w 177 I 149672 O 16 L 2.50 2.63 2.19 @#@FSLOADPOST 2022:01:12:16:55:01 mri_em_register N 4 3.84 3.58 2.70 mri_watershed -T1 -brain_atlas /usr/local/freesurfer/7.2.0-1/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=129 y=118 z=127 r=74 first estimation of the main basin volume: 1736275 voxels Looking for seedpoints 2 found in the cerebellum 18 found in the rest of the brain global maximum in x=148, y=97, z=91, Imax=255 CSF=15, 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=9568765715 voxels, voxel volume =1.000 = 9568765715 mmm3 = 9568765.952 cm3 done. PostAnalyze...Basin Prior 127 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=129,y=122, z=119, r=10263 iterations ^^^^^^^^ couldn't find WM with original limits - expanding ^^^^^^ GLOBAL CSF_MIN=1, CSF_intensity=2, CSF_MAX=34 , nb = 44874 RIGHT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=38 , nb = 3492 LEFT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=39 , nb = 3006 RIGHT_BRAIN CSF_MIN=1, CSF_intensity=2, CSF_MAX=31 , nb = 18810 LEFT_BRAIN CSF_MIN=1, CSF_intensity=2, CSF_MAX=5 , nb = 18954 OTHER CSF_MIN=0, CSF_intensity=29, CSF_MAX=43 , nb = 612 Problem with the least square interpolation in GM_MIN calculation. CSF_MAX TRANSITION GM_MIN GM GLOBAL before analyzing : 34, 38, 42, 62 after analyzing : 34, 40, 42, 45 RIGHT_CER before analyzing : 38, 37, 36, 73 after analyzing : 23, 37, 37, 46 LEFT_CER before analyzing : 39, 37, 37, 73 after analyzing : 25, 37, 37, 46 RIGHT_BRAIN before analyzing : 31, 37, 42, 62 after analyzing : 31, 40, 42, 45 LEFT_BRAIN before analyzing : 5, 10, 43, 61 after analyzing : 5, 32, 43, 39 OTHER before analyzing : 43, 48, 62, 95 after analyzing : 43, 57, 62, 66 mri_strip_skull: done peeling brain highly tesselated surface with 10242 vertices matching...66 iterations *********************VALIDATION********************* curvature mean = -0.012, std = 0.009 curvature mean = 74.877, std = 9.174 No Rigid alignment: -atlas Mode Off (basic atlas / no registration) before rotation: sse = 7.11, sigma = 12.36 after rotation: sse = 7.11, sigma = 12.36 Localization of inacurate regions: Erosion-Dilation steps the sse mean is 7.45, its var is 9.82 before Erosion-Dilatation 7.01% 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...41 iterations mri_strip_skull: done peeling brain Brain Size = 1990177 voxels, voxel volume = 1.000 mm3 = 1990177 mmm3 = 1990.177 cm3 ****************************** Saving brainmask.auto.mgz done mri_watershed utimesec 22.068740 mri_watershed stimesec 0.217008 mri_watershed ru_maxrss 814864 mri_watershed ru_ixrss 0 mri_watershed ru_idrss 0 mri_watershed ru_isrss 0 mri_watershed ru_minflt 187731 mri_watershed ru_majflt 0 mri_watershed ru_nswap 0 mri_watershed ru_inblock 8024 mri_watershed ru_oublock 3232 mri_watershed ru_msgsnd 0 mri_watershed ru_msgrcv 0 mri_watershed ru_nsignals 0 mri_watershed ru_nvcsw 435 mri_watershed ru_nivcsw 897 mri_watershed done @#@FSTIME 2022:01:12:16:55:01 mri_watershed N 6 e 13.83 S 0.25 U 22.06 P 161% M 814864 F 0 R 187733 W 0 c 899 w 436 I 8024 O 3232 L 3.84 3.58 2.70 @#@FSLOADPOST 2022:01:12:16:55:14 mri_watershed N 6 3.35 3.48 2.68 cp brainmask.auto.mgz brainmask.mgz #------------------------------------- #@# EM Registration Wed Jan 12 16:55:15 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/mri mri_em_register -uns 3 -mask brainmask.mgz nu.mgz /usr/local/freesurfer/7.2.0-1/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 = 4 == reading 1 input volumes... logging results to talairach.log reading '/usr/local/freesurfer/7.2.0-1/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=28.9 skull bounding box = (59, 60, 29) --> (200, 177, 217) finding center of left hemi white matter using (106, 99, 123) as brain centroid of Right_Cerebral_White_Matter... MRImask(): AllowDiffGeom = 1 mean wm in atlas = 107, using box (89,85,100) --> (123, 113,146) to find MRI wm before smoothing, mri peak at 108 robust fit to distribution - 108 +- 4.7 after smoothing, mri peak at 108, scaling input intensities by 0.991 scaling channel 0 by 0.990741 initial log_p = -4.088 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.072296 @ (0.000, 0.000, 0.000) max log p = -3.742453 @ (-5.263, -5.263, -15.789) max log p = -3.692916 @ (2.632, 2.632, 2.632) max log p = -3.692916 @ (0.000, 0.000, 0.000) max log p = -3.672800 @ (-0.658, -1.974, 1.974) max log p = -3.672800 @ (0.000, 0.000, 0.000) max log p = -3.672800 @ (0.000, 0.000, 0.000) max log p = -3.672800 @ (0.000, 0.000, 0.000) Found translation: (-3.3, -4.6, -11.2): log p = -3.673 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.644, old_max_log_p =-3.673 (thresh=-3.7) 0.99144 0.00000 -0.13053 13.04349; 0.00000 1.07500 0.00000 -13.89744; 0.12074 0.00000 0.91709 -17.22291; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 20 seconds. **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.621, old_max_log_p =-3.644 (thresh=-3.6) 0.99859 0.01831 -0.01073 -4.12370; -0.01576 1.06580 -0.11970 3.25023; -0.01073 0.13912 0.91850 -17.52071; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 20 seconds. **************************************** Nine parameter search. iteration 2 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.621, old_max_log_p =-3.621 (thresh=-3.6) 0.99859 0.01831 -0.01073 -4.12370; -0.01576 1.06580 -0.11970 3.25023; -0.01073 0.13912 0.91850 -17.52071; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 iteration took 0 minutes and 20 seconds. **************************************** Nine parameter search. iteration 3 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.471, old_max_log_p =-3.621 (thresh=-3.6) 0.96096 -0.02537 -0.03581 8.99346; 0.01836 1.09147 -0.06401 -9.23144; 0.02458 0.06833 0.94105 -17.90779; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 19 seconds. **************************************** Nine parameter search. iteration 4 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.465, old_max_log_p =-3.471 (thresh=-3.5) 0.97773 0.00828 -0.06992 6.73774; -0.01305 1.09179 -0.06180 -5.44212; 0.05601 0.06746 0.93937 -23.57136; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 19 seconds. **************************************** Nine parameter search. iteration 5 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.464, old_max_log_p =-3.465 (thresh=-3.5) 0.97894 -0.02524 -0.03711 6.72916; 0.01863 1.07108 -0.06187 -7.02602; 0.02398 0.06715 0.94116 -17.69890; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 iteration took 0 minutes and 19 seconds. **************************************** Nine parameter search. iteration 6 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.435, old_max_log_p =-3.464 (thresh=-3.5) 0.98111 -0.05306 -0.05075 11.46687; 0.04229 1.07011 -0.07861 -7.53435; 0.04035 0.08434 0.94039 -22.78457; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 17 seconds. **************************************** Nine parameter search. iteration 7 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.426, old_max_log_p =-3.435 (thresh=-3.4) 0.98073 -0.06179 -0.05011 12.50586; 0.05022 1.06713 -0.07884 -8.17644; 0.04039 0.08444 0.94149 -22.93446; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 17 seconds. **************************************** Nine parameter search. iteration 8 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.425, old_max_log_p =-3.426 (thresh=-3.4) 0.98073 -0.06179 -0.05011 12.50586; 0.05010 1.06463 -0.07866 -7.87769; 0.04039 0.08444 0.94149 -22.93446; 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 0.98073 -0.06179 -0.05011 12.50586; 0.05010 1.06463 -0.07866 -7.87769; 0.04039 0.08444 0.94149 -22.93446; 0.00000 0.00000 0.00000 1.00000; nsamples 2841 Quasinewton: input matrix 0.98073 -0.06179 -0.05011 12.50586; 0.05010 1.06463 -0.07866 -7.87769; 0.04039 0.08444 0.94149 -22.93446; 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: 011: -log(p) = -0.0 tol 0.000010 Resulting transform: 0.98073 -0.06179 -0.05011 12.50586; 0.05010 1.06463 -0.07866 -7.87769; 0.04039 0.08444 0.94149 -22.93446; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -3.425 (old=-4.088) transform before final EM align: 0.98073 -0.06179 -0.05011 12.50586; 0.05010 1.06463 -0.07866 -7.87769; 0.04039 0.08444 0.94149 -22.93446; 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 0.98073 -0.06179 -0.05011 12.50586; 0.05010 1.06463 -0.07866 -7.87769; 0.04039 0.08444 0.94149 -22.93446; 0.00000 0.00000 0.00000 1.00000; nsamples 315638 Quasinewton: input matrix 0.98073 -0.06179 -0.05011 12.50586; 0.05010 1.06463 -0.07866 -7.87769; 0.04039 0.08444 0.94149 -22.93446; 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: 013: -log(p) = 3.9 tol 0.000000 final transform: 0.98073 -0.06179 -0.05011 12.50586; 0.05010 1.06463 -0.07866 -7.87769; 0.04039 0.08444 0.94149 -22.93446; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach.lta... #VMPC# mri_em_register VmPeak 1024584 FSRUNTIME@ mri_em_register 0.0556 hours 4 threads registration took 3 minutes and 20 seconds. @#@FSTIME 2022:01:12:16:55:15 mri_em_register N 7 e 200.08 S 1.15 U 712.38 P 356% M 622492 F 0 R 124542 W 0 c 54298 w 194 I 139952 O 16 L 3.35 3.48 2.68 @#@FSLOADPOST 2022:01:12:16:58:36 mri_em_register N 7 5.33 4.22 3.12 #-------------------------------------- #@# CA Normalize Wed Jan 12 16:58:36 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/mri mri_ca_normalize -c ctrl_pts.mgz -mask brainmask.mgz nu.mgz /usr/local/freesurfer/7.2.0-1/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 '/usr/local/freesurfer/7.2.0-1/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=28.9 skull bounding box = (59, 60, 29) --> (200, 177, 217) finding center of left hemi white matter using (106, 99, 123) as brain centroid of Right_Cerebral_White_Matter... mean wm in atlas = 107, using box (89,85,100) --> (123, 113,146) to find MRI wm before smoothing, mri peak at 108 robust fit to distribution - 108 +- 4.7 after smoothing, mri peak at 108, scaling input intensities by 0.991 scaling channel 0 by 0.990741 using 246437 sample points... INFO: compute sample coordinates transform 0.98073 -0.06179 -0.05011 12.50586; 0.05010 1.06463 -0.07866 -7.87769; 0.04039 0.08444 0.94149 -22.93446; 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 (125, 58, 34) --> (198, 170, 213) Left_Cerebral_White_Matter: limiting intensities to 97.0 --> 132.0 0 of 627 (0.0%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (63, 59, 36) --> (133, 175, 214) Right_Cerebral_White_Matter: limiting intensities to 96.0 --> 132.0 0 of 240 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (131, 133, 54) --> (182, 176, 113) Left_Cerebellum_White_Matter: limiting intensities to 101.0 --> 132.0 3 of 12 (25.0%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (86, 134, 53) --> (130, 178, 115) Right_Cerebellum_White_Matter: limiting intensities to 98.0 --> 132.0 0 of 9 (0.0%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (114, 131, 91) --> (150, 195, 126) Brain_Stem: limiting intensities to 101.0 --> 132.0 7 of 12 (58.3%) samples deleted using 900 total control points for intensity normalization... bias field = 0.975 +- 0.049 7 of 890 control points discarded finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (125, 58, 34) --> (198, 170, 213) Left_Cerebral_White_Matter: limiting intensities to 90.0 --> 132.0 0 of 719 (0.0%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (63, 59, 36) --> (133, 175, 214) Right_Cerebral_White_Matter: limiting intensities to 89.0 --> 132.0 0 of 378 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (131, 133, 54) --> (182, 176, 113) Left_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 7 of 72 (9.7%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (86, 134, 53) --> (130, 178, 115) Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 7 of 83 (8.4%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (114, 131, 91) --> (150, 195, 126) Brain_Stem: limiting intensities to 88.0 --> 132.0 22 of 99 (22.2%) samples deleted using 1351 total control points for intensity normalization... bias field = 0.999 +- 0.064 3 of 1299 control points discarded finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (125, 58, 34) --> (198, 170, 213) Left_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 0 of 793 (0.0%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (63, 59, 36) --> (133, 175, 214) Right_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 0 of 442 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (131, 133, 54) --> (182, 176, 113) Left_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 28 of 91 (30.8%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (86, 134, 53) --> (130, 178, 115) Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 53 of 97 (54.6%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (114, 131, 91) --> (150, 195, 126) Brain_Stem: limiting intensities to 88.0 --> 132.0 112 of 166 (67.5%) samples deleted using 1589 total control points for intensity normalization... bias field = 1.011 +- 0.049 7 of 1365 control points discarded writing normalized volume to norm.mgz... writing control points to ctrl_pts.mgz freeing GCA...done. normalization took 0 minutes and 54 seconds. @#@FSTIME 2022:01:12:16:58:36 mri_ca_normalize N 8 e 53.61 S 0.37 U 57.40 P 107% M 697232 F 2 R 326413 W 0 c 1862 w 144 I 3032 O 4248 L 5.33 4.22 3.12 @#@FSLOADPOST 2022:01:12:16:59:29 mri_ca_normalize N 8 2.87 3.71 3.00 #-------------------------------------- #@# CA Reg Wed Jan 12 16:59:29 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/mri mri_ca_register -nobigventricles -T transforms/talairach.lta -align-after -mask brainmask.mgz norm.mgz /usr/local/freesurfer/7.2.0-1/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 = 4 == reading 1 input volumes... logging results to talairach.log reading input volume 'norm.mgz'... reading GCA '/usr/local/freesurfer/7.2.0-1/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 0.80565 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.743655) vs oldopt=(dt=369.92,rms=0.75919) #GCMRL# 0 dt 221.952000 rms 0.744 7.695% neg 0 invalid 762 tFOTS 7.1680 tGradient 2.7140 tsec 10.4030 #FOTS# QuadFit found better minimum quadopt=(dt=282.425,rms=0.726989) vs oldopt=(dt=369.92,rms=0.728453) #GCMRL# 1 dt 282.424821 rms 0.727 2.241% neg 0 invalid 762 tFOTS 7.1870 tGradient 2.6880 tsec 10.4070 #FOTS# QuadFit found better minimum quadopt=(dt=174.667,rms=0.7182) vs oldopt=(dt=92.48,rms=0.720617) #GCMRL# 2 dt 174.666667 rms 0.718 1.209% neg 0 invalid 762 tFOTS 7.1570 tGradient 2.6980 tsec 10.3770 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.714293) vs oldopt=(dt=369.92,rms=0.714927) #GCMRL# 3 dt 221.952000 rms 0.714 0.544% neg 0 invalid 762 tFOTS 7.1510 tGradient 2.5770 tsec 10.2580 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.710322) vs oldopt=(dt=369.92,rms=0.710796) #GCMRL# 4 dt 295.936000 rms 0.710 0.556% neg 0 invalid 762 tFOTS 7.1710 tGradient 2.8920 tsec 10.5940 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.708268) vs oldopt=(dt=92.48,rms=0.708394) #GCMRL# 5 dt 129.472000 rms 0.708 0.289% neg 0 invalid 762 tFOTS 7.5890 tGradient 2.7130 tsec 10.8320 #FOTS# QuadFit found better minimum quadopt=(dt=1183.74,rms=0.703423) vs oldopt=(dt=1479.68,rms=0.704061) #GCMRL# 6 dt 1183.744000 rms 0.703 0.684% neg 0 invalid 762 tFOTS 7.1910 tGradient 2.5710 tsec 10.2960 #FOTS# QuadFit found better minimum quadopt=(dt=94.5778,rms=0.700332) vs oldopt=(dt=92.48,rms=0.700341) #GCMRL# 7 dt 94.577778 rms 0.700 0.439% neg 0 invalid 762 tFOTS 7.6260 tGradient 2.7510 tsec 10.9060 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.700048) vs oldopt=(dt=92.48,rms=0.700117) #GCMRL# 8 dt 129.472000 rms 0.700 0.000% neg 0 invalid 762 tFOTS 7.5760 tGradient 2.6800 tsec 10.8120 #GCMRL# 9 dt 129.472000 rms 0.700 0.067% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7550 tsec 3.2920 #GCMRL# 10 dt 129.472000 rms 0.699 0.091% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7070 tsec 3.2400 #GCMRL# 11 dt 129.472000 rms 0.698 0.110% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6330 tsec 3.1690 #GCMRL# 12 dt 129.472000 rms 0.697 0.129% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6300 tsec 3.1650 #GCMRL# 13 dt 129.472000 rms 0.696 0.162% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8020 tsec 3.3380 #GCMRL# 14 dt 129.472000 rms 0.695 0.189% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9090 tsec 3.4430 #GCMRL# 15 dt 129.472000 rms 0.693 0.199% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9420 tsec 3.5020 #GCMRL# 16 dt 129.472000 rms 0.692 0.193% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6580 tsec 3.2170 #GCMRL# 17 dt 129.472000 rms 0.691 0.195% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6760 tsec 3.2370 #GCMRL# 18 dt 129.472000 rms 0.690 0.170% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6200 tsec 3.1520 #GCMRL# 19 dt 129.472000 rms 0.689 0.147% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5920 tsec 3.1440 #GCMRL# 20 dt 129.472000 rms 0.688 0.130% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6450 tsec 3.1820 #GCMRL# 21 dt 129.472000 rms 0.687 0.109% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6070 tsec 3.1540 #GCMRL# 22 dt 129.472000 rms 0.686 0.100% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6050 tsec 3.1710 #FOTS# QuadFit found better minimum quadopt=(dt=1775.62,rms=0.685545) vs oldopt=(dt=1479.68,rms=0.685575) #GCMRL# 23 dt 1775.616000 rms 0.686 0.101% neg 0 invalid 762 tFOTS 7.5810 tGradient 2.6330 tsec 10.7440 #FOTS# QuadFit found better minimum quadopt=(dt=3.468,rms=0.685588) vs oldopt=(dt=5.78,rms=0.685588) #GCAMreg# pass 0 level1 5 level2 1 tsec 165.448 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 0.686045 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.684753) vs oldopt=(dt=92.48,rms=0.684933) #GCMRL# 25 dt 129.472000 rms 0.685 0.188% neg 0 invalid 762 tFOTS 7.2420 tGradient 2.7030 tsec 10.4700 #GCMRL# 26 dt 369.920000 rms 0.684 0.000% neg 0 invalid 762 tFOTS 7.1230 tGradient 2.6060 tsec 10.2820 #GCMRL# 27 dt 369.920000 rms 0.683 0.134% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7430 tsec 3.2920 #GCMRL# 28 dt 369.920000 rms 0.683 0.063% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8400 tsec 3.3770 #GCMRL# 29 dt 369.920000 rms 0.682 0.052% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6400 tsec 3.2010 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.68187) vs oldopt=(dt=369.92,rms=0.681938) #GCMRL# 30 dt 517.888000 rms 0.682 0.073% neg 0 invalid 762 tFOTS 7.7090 tGradient 2.7070 tsec 10.9560 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 0.691661 #FOTS# QuadFit found better minimum quadopt=(dt=20.736,rms=0.690781) vs oldopt=(dt=25.92,rms=0.690783) #GCMRL# 32 dt 20.736000 rms 0.691 0.127% neg 0 invalid 762 tFOTS 7.6340 tGradient 2.3380 tsec 10.4980 #GCMRL# 33 dt 25.920000 rms 0.691 0.000% neg 0 invalid 762 tFOTS 7.1230 tGradient 2.2330 tsec 9.9130 #GCMRL# 34 dt 25.920000 rms 0.690 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4440 tsec 2.9790 #GCMRL# 35 dt 25.920000 rms 0.690 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1990 tsec 2.7310 #GCAMreg# pass 0 level1 4 level2 1 tsec 31.519 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 0.690694 #FOTS# QuadFit found better minimum quadopt=(dt=107.492,rms=0.688157) vs oldopt=(dt=103.68,rms=0.688159) #GCMRL# 37 dt 107.491713 rms 0.688 0.367% neg 0 invalid 762 tFOTS 7.2610 tGradient 2.2520 tsec 10.0520 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.687849) vs oldopt=(dt=103.68,rms=0.687852) #GCMRL# 38 dt 124.416000 rms 0.688 0.000% neg 0 invalid 762 tFOTS 7.2090 tGradient 2.2940 tsec 10.0630 #GCMRL# 39 dt 124.416000 rms 0.687 0.090% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3490 tsec 2.8920 #GCMRL# 40 dt 124.416000 rms 0.686 0.202% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2650 tsec 2.8050 #GCMRL# 41 dt 124.416000 rms 0.684 0.201% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3290 tsec 2.8680 #GCMRL# 42 dt 124.416000 rms 0.683 0.242% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1850 tsec 2.7250 #GCMRL# 43 dt 124.416000 rms 0.681 0.310% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2870 tsec 2.8220 #GCMRL# 44 dt 124.416000 rms 0.678 0.329% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2510 tsec 2.7850 #GCMRL# 45 dt 124.416000 rms 0.677 0.229% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3240 tsec 2.8600 #GCMRL# 46 dt 124.416000 rms 0.676 0.094% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2570 tsec 2.7930 #GCMRL# 47 dt 124.416000 rms 0.676 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2300 tsec 2.7890 #GCMRL# 48 dt 103.680000 rms 0.676 0.043% neg 0 invalid 762 tFOTS 7.1690 tGradient 2.1830 tsec 9.8850 #FOTS# QuadFit found better minimum quadopt=(dt=0.00189844,rms=0.675726) vs oldopt=(dt=0.00158203,rms=0.675726) 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 0.695173 #GCMRL# 50 dt 0.000000 rms 0.695 0.084% neg 0 invalid 762 tFOTS 6.4160 tGradient 2.0260 tsec 8.9780 #GCAMreg# pass 0 level1 3 level2 1 tsec 20.838 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 0.695173 #GCMRL# 52 dt 0.000000 rms 0.695 0.084% neg 0 invalid 762 tFOTS 6.2780 tGradient 2.0120 tsec 8.8160 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 0.753181 #FOTS# QuadFit found better minimum quadopt=(dt=2.11962,rms=0.737581) vs oldopt=(dt=2.88,rms=0.739449) #GCMRL# 54 dt 2.119617 rms 0.738 2.071% neg 0 invalid 762 tFOTS 6.8250 tGradient 1.9510 tsec 9.3000 #FOTS# QuadFit found better minimum quadopt=(dt=0.977273,rms=0.736992) vs oldopt=(dt=0.72,rms=0.737031) #GCMRL# 55 dt 0.977273 rms 0.737 0.000% neg 0 invalid 762 tFOTS 6.7480 tGradient 2.0290 tsec 9.3320 #GCAMreg# pass 0 level1 2 level2 1 tsec 24.159 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 0.737552 #FOTS# QuadFit found better minimum quadopt=(dt=0.054,rms=0.73696) vs oldopt=(dt=0.045,rms=0.73696) #GCMRL# 57 dt 0.054000 rms 0.737 0.080% neg 0 invalid 762 tFOTS 6.2760 tGradient 1.9840 tsec 8.7890 #FOTS# QuadFit found better minimum quadopt=(dt=0.0135,rms=0.736964) vs oldopt=(dt=0.01125,rms=0.736964) 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 0.796761 #FOTS# QuadFit found better minimum quadopt=(dt=0.526316,rms=0.791889) vs oldopt=(dt=0.32,rms=0.792901) #GCMRL# 59 dt 0.526316 rms 0.792 0.611% neg 0 invalid 762 tFOTS 6.7720 tGradient 1.9580 tsec 9.2700 #FOTS# QuadFit found better minimum quadopt=(dt=0.57438,rms=0.785935) vs oldopt=(dt=0.32,rms=0.787472) #GCMRL# 60 dt 0.574380 rms 0.786 0.752% neg 0 invalid 762 tFOTS 6.9270 tGradient 1.9320 tsec 9.3930 #FOTS# QuadFit found better minimum quadopt=(dt=0.194346,rms=0.784284) vs oldopt=(dt=0.32,rms=0.784554) #GCMRL# 61 dt 0.194346 rms 0.784 0.000% neg 0 invalid 762 tFOTS 6.8330 tGradient 1.8620 tsec 9.2500 #GCMRL# 62 dt 0.194346 rms 0.783 0.137% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8670 tsec 2.4200 #GCMRL# 63 dt 0.194346 rms 0.781 0.220% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9100 tsec 2.4690 #GCMRL# 64 dt 0.194346 rms 0.779 0.283% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9380 tsec 2.4760 #GCMRL# 65 dt 0.194346 rms 0.777 0.295% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9690 tsec 2.5090 #GCMRL# 66 dt 0.194346 rms 0.775 0.222% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8900 tsec 2.4380 #GCMRL# 67 dt 0.194346 rms 0.774 0.131% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9060 tsec 2.4520 #GCMRL# 68 dt 0.194346 rms 0.774 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9340 tsec 2.4710 #GCMRL# 69 dt 0.194346 rms 0.774 -0.010% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9880 tsec 2.9940 #FOTS# QuadFit found better minimum quadopt=(dt=1.536,rms=0.771247) vs oldopt=(dt=1.28,rms=0.771319) #GCMRL# 70 dt 1.536000 rms 0.771 0.349% neg 0 invalid 762 tFOTS 6.9030 tGradient 2.0010 tsec 9.4550 #FOTS# QuadFit found better minimum quadopt=(dt=0.256,rms=0.770102) vs oldopt=(dt=0.32,rms=0.770238) #GCMRL# 71 dt 0.256000 rms 0.770 0.000% neg 0 invalid 762 tFOTS 6.8080 tGradient 1.9000 tsec 9.2840 #GCMRL# 72 dt 0.256000 rms 0.770 0.067% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9920 tsec 2.5240 #GCMRL# 73 dt 0.256000 rms 0.769 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9170 tsec 2.4520 #GCMRL# 74 dt 0.256000 rms 0.769 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9340 tsec 2.4930 #GCAMreg# pass 0 level1 1 level2 1 tsec 86.235 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 0.769765 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.768582) vs oldopt=(dt=0.32,rms=0.768632) #GCMRL# 76 dt 0.448000 rms 0.769 0.154% neg 0 invalid 762 tFOTS 6.7260 tGradient 1.9880 tsec 9.2370 #FOTS# QuadFit found better minimum quadopt=(dt=0.096,rms=0.768542) vs oldopt=(dt=0.08,rms=0.768542) #GCMRL# 77 dt 0.096000 rms 0.769 0.000% neg 0 invalid 762 tFOTS 6.7750 tGradient 2.0050 tsec 9.3400 #GCMRL# 78 dt 0.096000 rms 0.769 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0110 tsec 2.5440 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 0.743145 #FOTS# QuadFit found better minimum quadopt=(dt=0.141129,rms=0.73885) vs oldopt=(dt=0.08,rms=0.739888) #GCMRL# 80 dt 0.141129 rms 0.739 0.578% neg 0 invalid 762 tFOTS 6.7450 tGradient 1.5320 tsec 8.8000 #FOTS# QuadFit found better minimum quadopt=(dt=0.0015,rms=0.738838) vs oldopt=(dt=0.00125,rms=0.738838) #GCMRL# 81 dt 0.001500 rms 0.739 0.000% neg 0 invalid 762 tFOTS 6.7580 tGradient 1.4420 tsec 8.7530 #GCAMreg# pass 0 level1 0 level2 1 tsec 22.498 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 0.739607 #FOTS# QuadFit found better minimum quadopt=(dt=0.007,rms=0.738798) vs oldopt=(dt=0.005,rms=0.738805) #GCMRL# 83 dt 0.007000 rms 0.739 0.109% neg 0 invalid 762 tFOTS 6.7540 tGradient 1.4230 tsec 8.7150 #FOTS# QuadFit found better minimum quadopt=(dt=0.003,rms=0.738795) vs oldopt=(dt=0.005,rms=0.738796) #GCMRL# 84 dt 0.003000 rms 0.739 0.000% neg 0 invalid 762 tFOTS 6.7650 tGradient 1.4780 tsec 8.7980 GCAMregister done in 9.44063 min Starting GCAmapRenormalizeWithAlignment() without scales renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.10253 (16) mri peak = 0.08937 (25) Left_Lateral_Ventricle (4): linear fit = 1.10 x + 0.0 (1401 voxels, overlap=0.817) Left_Lateral_Ventricle (4): linear fit = 1.10 x + 0.0 (1401 voxels, peak = 18), gca=17.5 gca peak = 0.17690 (16) mri peak = 0.06696 (23) Right_Lateral_Ventricle (43): linear fit = 1.40 x + 0.0 (913 voxels, overlap=0.770) Right_Lateral_Ventricle (43): linear fit = 1.40 x + 0.0 (913 voxels, peak = 22), gca=22.5 gca peak = 0.28275 (96) mri peak = 0.06956 (89) Right_Pallidum (52): linear fit = 0.94 x + 0.0 (1042 voxels, overlap=1.005) Right_Pallidum (52): linear fit = 0.94 x + 0.0 (1042 voxels, peak = 90), gca=89.8 gca peak = 0.18948 (93) mri peak = 0.07481 (92) Left_Pallidum (13): linear fit = 0.94 x + 0.0 (918 voxels, overlap=0.844) Left_Pallidum (13): linear fit = 0.94 x + 0.0 (918 voxels, peak = 88), gca=87.9 gca peak = 0.20755 (55) mri peak = 0.04709 (90) Right_Hippocampus (53): linear fit = 1.49 x + 0.0 (882 voxels, overlap=0.201) Right_Hippocampus (53): linear fit = 1.49 x + 0.0 (882 voxels, peak = 82), gca=81.7 gca peak = 0.31831 (58) mri peak = 0.05791 (67) Left_Hippocampus (17): linear fit = 1.16 x + 0.0 (754 voxels, overlap=0.072) Left_Hippocampus (17): linear fit = 1.16 x + 0.0 (754 voxels, peak = 68), gca=67.6 gca peak = 0.11957 (102) mri peak = 0.11227 (105) Right_Cerebral_White_Matter (41): linear fit = 1.02 x + 0.0 (71703 voxels, overlap=0.711) Right_Cerebral_White_Matter (41): linear fit = 1.02 x + 0.0 (71703 voxels, peak = 105), gca=104.5 gca peak = 0.11429 (102) mri peak = 0.10515 (107) Left_Cerebral_White_Matter (2): linear fit = 1.04 x + 0.0 (72099 voxels, overlap=0.663) Left_Cerebral_White_Matter (2): linear fit = 1.04 x + 0.0 (72099 voxels, peak = 107), gca=106.6 gca peak = 0.14521 (59) mri peak = 0.04956 (63) Left_Cerebral_Cortex (3): linear fit = 1.08 x + 0.0 (27289 voxels, overlap=0.909) Left_Cerebral_Cortex (3): linear fit = 1.08 x + 0.0 (27289 voxels, peak = 63), gca=63.4 gca peak = 0.14336 (58) mri peak = 0.04982 (63) Right_Cerebral_Cortex (42): linear fit = 1.08 x + 0.0 (29282 voxels, overlap=0.922) Right_Cerebral_Cortex (42): linear fit = 1.08 x + 0.0 (29282 voxels, peak = 62), gca=62.4 gca peak = 0.13305 (70) mri peak = 0.09290 (71) Right_Caudate (50): linear fit = 1.05 x + 0.0 (808 voxels, overlap=0.938) Right_Caudate (50): linear fit = 1.05 x + 0.0 (808 voxels, peak = 74), gca=73.8 gca peak = 0.15761 (71) mri peak = 0.09421 (72) Left_Caudate (11): linear fit = 1.00 x + 0.0 (1333 voxels, overlap=0.936) Left_Caudate (11): linear fit = 1.00 x + 0.0 (1333 voxels, peak = 71), gca=71.0 gca peak = 0.13537 (57) mri peak = 0.04979 (65) Left_Cerebellum_Cortex (8): linear fit = 1.16 x + 0.0 (28578 voxels, overlap=0.468) Left_Cerebellum_Cortex (8): linear fit = 1.16 x + 0.0 (28578 voxels, peak = 66), gca=66.4 gca peak = 0.13487 (56) mri peak = 0.05725 (66) Right_Cerebellum_Cortex (47): linear fit = 1.16 x + 0.0 (32524 voxels, overlap=0.344) Right_Cerebellum_Cortex (47): linear fit = 1.16 x + 0.0 (32524 voxels, peak = 65), gca=65.2 gca peak = 0.19040 (84) mri peak = 0.06995 (87) Left_Cerebellum_White_Matter (7): linear fit = 1.04 x + 0.0 (10825 voxels, overlap=0.620) Left_Cerebellum_White_Matter (7): linear fit = 1.04 x + 0.0 (10825 voxels, peak = 88), gca=87.8 gca peak = 0.18871 (83) mri peak = 0.06888 (85) Right_Cerebellum_White_Matter (46): linear fit = 1.03 x + 0.0 (9201 voxels, overlap=0.890) Right_Cerebellum_White_Matter (46): linear fit = 1.03 x + 0.0 (9201 voxels, peak = 86), gca=85.9 gca peak = 0.24248 (57) mri peak = 0.09772 (68) Left_Amygdala (18): linear fit = 1.17 x + 0.0 (320 voxels, overlap=0.426) Left_Amygdala (18): linear fit = 1.17 x + 0.0 (320 voxels, peak = 67), gca=67.0 gca peak = 0.35833 (56) mri peak = 0.07269 (70) Right_Amygdala (54): linear fit = 1.21 x + 0.0 (512 voxels, overlap=0.033) Right_Amygdala (54): linear fit = 1.21 x + 0.0 (512 voxels, peak = 67), gca=67.5 gca peak = 0.12897 (85) mri peak = 0.05647 (90) Left_Thalamus (10): linear fit = 1.07 x + 0.0 (6466 voxels, overlap=0.830) Left_Thalamus (10): linear fit = 1.07 x + 0.0 (6466 voxels, peak = 91), gca=90.5 gca peak = 0.13127 (83) mri peak = 0.06405 (89) Right_Thalamus (49): linear fit = 1.08 x + 0.0 (5168 voxels, overlap=0.803) Right_Thalamus (49): linear fit = 1.08 x + 0.0 (5168 voxels, peak = 89), gca=89.2 gca peak = 0.12974 (78) mri peak = 0.05816 (83) Left_Putamen (12): linear fit = 1.09 x + 0.0 (2569 voxels, overlap=0.772) Left_Putamen (12): linear fit = 1.09 x + 0.0 (2569 voxels, peak = 85), gca=84.6 gca peak = 0.17796 (79) mri peak = 0.08082 (84) Right_Putamen (51): linear fit = 1.04 x + 0.0 (2702 voxels, overlap=0.924) Right_Putamen (51): linear fit = 1.04 x + 0.0 (2702 voxels, peak = 83), gca=82.6 gca peak = 0.10999 (80) mri peak = 0.12293 (81) Brain_Stem (16): linear fit = 1.07 x + 0.0 (13594 voxels, overlap=0.415) Brain_Stem (16): linear fit = 1.07 x + 0.0 (13594 voxels, peak = 85), gca=85.2 gca peak = 0.13215 (88) mri peak = 0.07698 (86) Right_VentralDC (60): linear fit = 1.04 x + 0.0 (1525 voxels, overlap=0.729) Right_VentralDC (60): linear fit = 1.04 x + 0.0 (1525 voxels, peak = 92), gca=92.0 gca peak = 0.11941 (89) mri peak = 0.07457 (92) Left_VentralDC (28): linear fit = 1.03 x + 0.0 (1476 voxels, overlap=0.788) Left_VentralDC (28): linear fit = 1.03 x + 0.0 (1476 voxels, peak = 92), gca=92.1 gca peak = 0.20775 (25) mri peak = 1.00000 (44) Third_Ventricle (14): linear fit = 1.74 x + 0.0 (73 voxels, overlap=2.038) Third_Ventricle (14): linear fit = 1.74 x + 0.0 (73 voxels, peak = 43), gca=43.4 gca peak = 0.13297 (21) mri peak = 0.06109 (24) Fourth_Ventricle (15): linear fit = 1.20 x + 0.0 (418 voxels, overlap=0.781) Fourth_Ventricle (15): linear fit = 1.20 x + 0.0 (418 voxels, peak = 25), gca=25.1 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.19087 (28) gca peak Third_Ventricle = 0.20775 (25) 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 estimating mean gm scale to be 1.20 x + 0.0 estimating mean wm scale to be 1.03 x + 0.0 estimating mean csf scale to be 1.23 x + 0.0 saving intensity scales to talairach.label_intensities.txt GCAmapRenormalizeWithAlignment() took 3.34863 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 0.752315 #FOTS# QuadFit found better minimum quadopt=(dt=241.291,rms=0.722952) vs oldopt=(dt=369.92,rms=0.729213) #GCMRL# 86 dt 241.290954 rms 0.723 3.903% neg 0 invalid 762 tFOTS 7.1550 tGradient 2.6100 tsec 10.2880 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.713286) vs oldopt=(dt=369.92,rms=0.713576) #GCMRL# 87 dt 295.936000 rms 0.713 1.337% neg 0 invalid 762 tFOTS 6.7520 tGradient 2.6660 tsec 9.9500 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.707008) vs oldopt=(dt=369.92,rms=0.707583) #GCMRL# 88 dt 295.936000 rms 0.707 0.880% neg 0 invalid 762 tFOTS 6.7270 tGradient 2.6130 tsec 9.8730 #FOTS# QuadFit found better minimum quadopt=(dt=135,rms=0.704502) vs oldopt=(dt=92.48,rms=0.704946) #GCMRL# 89 dt 135.000000 rms 0.705 0.354% neg 0 invalid 762 tFOTS 7.5860 tGradient 2.5540 tsec 10.6710 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.700232) vs oldopt=(dt=369.92,rms=0.701028) #GCMRL# 90 dt 517.888000 rms 0.700 0.606% neg 0 invalid 762 tFOTS 7.3510 tGradient 2.7870 tsec 10.6930 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.698773) vs oldopt=(dt=92.48,rms=0.698938) #GCMRL# 91 dt 129.472000 rms 0.699 0.208% neg 0 invalid 762 tFOTS 7.2200 tGradient 2.6810 tsec 10.4490 #FOTS# QuadFit found better minimum quadopt=(dt=1183.74,rms=0.695207) vs oldopt=(dt=1479.68,rms=0.695836) #GCMRL# 92 dt 1183.744000 rms 0.695 0.510% neg 0 invalid 762 tFOTS 7.2930 tGradient 2.7250 tsec 10.5600 #FOTS# QuadFit found better minimum quadopt=(dt=93.3836,rms=0.69246) vs oldopt=(dt=92.48,rms=0.692463) #GCMRL# 93 dt 93.383648 rms 0.692 0.395% neg 0 invalid 762 tFOTS 7.6150 tGradient 2.5950 tsec 10.7430 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.691883) vs oldopt=(dt=369.92,rms=0.69191) #GCMRL# 94 dt 295.936000 rms 0.692 0.083% neg 0 invalid 762 tFOTS 7.1610 tGradient 2.6750 tsec 10.3720 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.690777) vs oldopt=(dt=369.92,rms=0.691054) #GCMRL# 95 dt 221.952000 rms 0.691 0.160% neg 0 invalid 762 tFOTS 7.2720 tGradient 2.7020 tsec 10.5190 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.690375) vs oldopt=(dt=92.48,rms=0.690426) #GCMRL# 96 dt 129.472000 rms 0.690 0.058% neg 0 invalid 762 tFOTS 7.1510 tGradient 2.6690 tsec 10.3720 #GCMRL# 97 dt 1479.680000 rms 0.688 0.368% neg 0 invalid 762 tFOTS 7.1460 tGradient 2.6260 tsec 10.3070 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.685884) vs oldopt=(dt=92.48,rms=0.685885) #GCMRL# 98 dt 110.976000 rms 0.686 0.283% neg 0 invalid 762 tFOTS 7.6430 tGradient 2.6670 tsec 10.8430 #FOTS# QuadFit found better minimum quadopt=(dt=1775.62,rms=0.683043) vs oldopt=(dt=1479.68,rms=0.68316) #GCMRL# 99 dt 1775.616000 rms 0.683 0.414% neg 0 invalid 762 tFOTS 7.6380 tGradient 2.6900 tsec 10.8630 #GCMRL# 100 dt 92.480000 rms 0.682 0.191% neg 0 invalid 762 tFOTS 7.6800 tGradient 2.5860 tsec 10.8010 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.681575) vs oldopt=(dt=92.48,rms=0.681588) #GCMRL# 101 dt 129.472000 rms 0.682 0.000% neg 0 invalid 762 tFOTS 7.6590 tGradient 2.6680 tsec 10.8840 #GCMRL# 102 dt 129.472000 rms 0.681 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5850 tsec 3.1350 #GCMRL# 103 dt 129.472000 rms 0.681 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6190 tsec 3.1510 #GCMRL# 104 dt 129.472000 rms 0.681 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6080 tsec 3.1420 #GCMRL# 105 dt 129.472000 rms 0.680 0.097% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6910 tsec 3.2240 #GCMRL# 106 dt 129.472000 rms 0.679 0.106% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6720 tsec 3.2020 #GCMRL# 107 dt 129.472000 rms 0.678 0.122% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6550 tsec 3.1890 #GCMRL# 108 dt 129.472000 rms 0.677 0.127% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6050 tsec 3.1500 #GCMRL# 109 dt 129.472000 rms 0.677 0.130% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6010 tsec 3.1540 #GCMRL# 110 dt 129.472000 rms 0.676 0.132% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6800 tsec 3.2390 #GCMRL# 111 dt 129.472000 rms 0.675 0.135% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6360 tsec 3.1700 #GCMRL# 112 dt 129.472000 rms 0.674 0.132% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6020 tsec 3.1470 #GCMRL# 113 dt 129.472000 rms 0.673 0.128% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7000 tsec 3.2370 #GCMRL# 114 dt 129.472000 rms 0.672 0.112% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6700 tsec 3.2120 #GCMRL# 115 dt 129.472000 rms 0.672 0.106% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7360 tsec 3.3130 #GCMRL# 116 dt 129.472000 rms 0.671 0.107% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7160 tsec 3.2710 #GCMRL# 117 dt 129.472000 rms 0.670 0.115% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9860 tsec 3.5490 #GCMRL# 118 dt 129.472000 rms 0.669 0.110% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8380 tsec 3.4020 #GCMRL# 119 dt 129.472000 rms 0.669 0.096% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6490 tsec 3.1980 #GCMRL# 120 dt 129.472000 rms 0.668 0.092% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8200 tsec 3.3760 #GCMRL# 121 dt 129.472000 rms 0.667 0.086% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9610 tsec 3.4960 #GCMRL# 122 dt 129.472000 rms 0.667 0.077% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7160 tsec 3.2500 #GCMRL# 123 dt 129.472000 rms 0.666 0.081% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8650 tsec 3.4030 #GCMRL# 124 dt 129.472000 rms 0.666 0.077% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8580 tsec 3.3900 #GCMRL# 125 dt 129.472000 rms 0.665 0.076% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8830 tsec 3.4190 #GCMRL# 126 dt 129.472000 rms 0.665 0.077% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9650 tsec 3.4990 #GCMRL# 127 dt 129.472000 rms 0.664 0.068% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8350 tsec 3.3900 #GCMRL# 128 dt 129.472000 rms 0.664 0.076% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0060 tsec 3.5400 #GCMRL# 129 dt 129.472000 rms 0.663 0.071% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8360 tsec 3.3720 #GCMRL# 130 dt 129.472000 rms 0.663 0.069% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9390 tsec 3.4730 #GCMRL# 131 dt 129.472000 rms 0.663 0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0210 tsec 3.5580 #GCMRL# 132 dt 129.472000 rms 0.662 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9610 tsec 3.4980 #GCMRL# 133 dt 129.472000 rms 0.662 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9130 tsec 3.4460 #GCMRL# 134 dt 129.472000 rms 0.662 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7960 tsec 3.3340 #GCMRL# 135 dt 129.472000 rms 0.661 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8840 tsec 3.4450 #GCMRL# 136 dt 129.472000 rms 0.661 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6370 tsec 3.1920 #GCMRL# 137 dt 129.472000 rms 0.660 0.057% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6610 tsec 3.2120 #GCMRL# 138 dt 129.472000 rms 0.660 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6630 tsec 3.1970 #GCMRL# 139 dt 129.472000 rms 0.660 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6710 tsec 3.2080 #GCMRL# 140 dt 129.472000 rms 0.659 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7290 tsec 3.2630 #GCMRL# 141 dt 129.472000 rms 0.659 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6790 tsec 3.2150 #GCMRL# 142 dt 129.472000 rms 0.659 0.047% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6970 tsec 3.2340 #GCMRL# 143 dt 129.472000 rms 0.659 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6970 tsec 3.2280 #GCMRL# 144 dt 129.472000 rms 0.658 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7160 tsec 3.2510 #GCMRL# 145 dt 129.472000 rms 0.658 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7520 tsec 3.2840 #GCMRL# 146 dt 129.472000 rms 0.658 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6560 tsec 3.1900 #GCMRL# 147 dt 129.472000 rms 0.657 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5630 tsec 3.1020 #GCMRL# 148 dt 129.472000 rms 0.657 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6370 tsec 3.1700 #GCMRL# 149 dt 129.472000 rms 0.657 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6590 tsec 3.2270 #GCMRL# 150 dt 129.472000 rms 0.657 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6670 tsec 3.2140 #GCMRL# 151 dt 129.472000 rms 0.657 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6840 tsec 3.2420 #GCMRL# 152 dt 129.472000 rms 0.656 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6830 tsec 3.2330 #GCMRL# 153 dt 129.472000 rms 0.656 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6670 tsec 3.2180 #GCMRL# 154 dt 129.472000 rms 0.656 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6410 tsec 3.1760 #GCMRL# 155 dt 129.472000 rms 0.656 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7000 tsec 3.2370 #GCMRL# 156 dt 129.472000 rms 0.656 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6030 tsec 3.1430 #GCMRL# 157 dt 129.472000 rms 0.656 0.022% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6470 tsec 3.2050 #FOTS# QuadFit found better minimum quadopt=(dt=2071.55,rms=0.655524) vs oldopt=(dt=1479.68,rms=0.65553) #GCMRL# 158 dt 2071.552000 rms 0.656 0.000% neg 0 invalid 762 tFOTS 7.6640 tGradient 2.6800 tsec 10.9080 #GCAMreg# pass 0 level1 5 level2 1 tsec 368.809 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 0.65607 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.654935) vs oldopt=(dt=369.92,rms=0.655071) #GCMRL# 160 dt 221.952000 rms 0.655 0.173% neg 0 invalid 762 tFOTS 7.2100 tGradient 2.6160 tsec 10.3480 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.65419) vs oldopt=(dt=369.92,rms=0.654276) #GCMRL# 161 dt 517.888000 rms 0.654 0.114% neg 0 invalid 762 tFOTS 7.1970 tGradient 2.7720 tsec 10.5010 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.65412) vs oldopt=(dt=92.48,rms=0.654121) #GCMRL# 162 dt 110.976000 rms 0.654 0.000% neg 0 invalid 762 tFOTS 7.6560 tGradient 2.6750 tsec 10.8880 #GCMRL# 163 dt 110.976000 rms 0.654 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7090 tsec 3.2430 #GCMRL# 164 dt 110.976000 rms 0.654 0.012% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6570 tsec 3.1920 #GCMRL# 165 dt 110.976000 rms 0.654 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7400 tsec 3.2900 #GCMRL# 166 dt 110.976000 rms 0.654 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6530 tsec 3.2010 #GCMRL# 167 dt 110.976000 rms 0.653 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5980 tsec 3.1530 #GCMRL# 168 dt 110.976000 rms 0.653 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6530 tsec 3.2170 #GCMRL# 169 dt 110.976000 rms 0.653 0.020% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6110 tsec 3.1690 #GCMRL# 170 dt 110.976000 rms 0.653 0.018% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6860 tsec 3.2610 #GCMRL# 171 dt 1479.680000 rms 0.653 0.024% neg 0 invalid 762 tFOTS 7.6920 tGradient 2.6300 tsec 10.8680 #GCMRL# 172 dt 92.480000 rms 0.653 0.000% neg 0 invalid 762 tFOTS 7.6160 tGradient 2.6420 tsec 10.8360 #GCMRL# 173 dt 92.480000 rms 0.653 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6240 tsec 3.1580 #GCMRL# 174 dt 92.480000 rms 0.653 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6020 tsec 3.1360 #GCMRL# 175 dt 92.480000 rms 0.653 0.006% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5870 tsec 3.1260 #GCMRL# 176 dt 92.480000 rms 0.653 0.008% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6700 tsec 3.2070 #GCMRL# 177 dt 92.480000 rms 0.653 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5490 tsec 3.1100 #GCMRL# 178 dt 92.480000 rms 0.653 0.012% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8430 tsec 3.3820 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 0.655061 #FOTS# QuadFit found better minimum quadopt=(dt=192.941,rms=0.649878) vs oldopt=(dt=103.68,rms=0.650694) #GCMRL# 180 dt 192.941176 rms 0.650 0.791% neg 0 invalid 762 tFOTS 7.1780 tGradient 2.3630 tsec 10.0670 #FOTS# QuadFit found better minimum quadopt=(dt=233.205,rms=0.642849) vs oldopt=(dt=103.68,rms=0.644525) #GCMRL# 181 dt 233.205479 rms 0.643 1.082% neg 0 invalid 762 tFOTS 7.5930 tGradient 2.2620 tsec 10.3850 #FOTS# QuadFit found better minimum quadopt=(dt=69.6618,rms=0.638332) vs oldopt=(dt=103.68,rms=0.639302) #GCMRL# 182 dt 69.661814 rms 0.638 0.703% neg 0 invalid 762 tFOTS 7.1610 tGradient 2.2260 tsec 9.9260 #GCMRL# 183 dt 414.720000 rms 0.634 0.746% neg 0 invalid 762 tFOTS 7.1280 tGradient 2.1660 tsec 9.8260 #FOTS# QuadFit found better minimum quadopt=(dt=71.2295,rms=0.629167) vs oldopt=(dt=103.68,rms=0.63) #GCMRL# 184 dt 71.229489 rms 0.629 0.695% neg 0 invalid 762 tFOTS 7.1750 tGradient 2.3130 tsec 10.0510 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.627242) vs oldopt=(dt=103.68,rms=0.62742) #GCMRL# 185 dt 145.152000 rms 0.627 0.306% neg 0 invalid 762 tFOTS 7.3320 tGradient 2.1660 tsec 10.0450 #FOTS# QuadFit found better minimum quadopt=(dt=72.5171,rms=0.62559) vs oldopt=(dt=103.68,rms=0.625829) #GCMRL# 186 dt 72.517110 rms 0.626 0.263% neg 0 invalid 762 tFOTS 7.2270 tGradient 2.1710 tsec 9.9490 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.624104) vs oldopt=(dt=103.68,rms=0.624336) #GCMRL# 187 dt 145.152000 rms 0.624 0.237% neg 0 invalid 762 tFOTS 7.2520 tGradient 2.2020 tsec 10.0180 #FOTS# QuadFit found better minimum quadopt=(dt=82.6667,rms=0.622871) vs oldopt=(dt=103.68,rms=0.622935) #GCMRL# 188 dt 82.666667 rms 0.623 0.198% neg 0 invalid 762 tFOTS 7.6670 tGradient 2.1840 tsec 10.4000 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.621724) vs oldopt=(dt=103.68,rms=0.621782) #GCMRL# 189 dt 124.416000 rms 0.622 0.184% neg 0 invalid 762 tFOTS 7.3290 tGradient 2.1360 tsec 10.0250 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.620614) vs oldopt=(dt=103.68,rms=0.620686) #GCMRL# 190 dt 82.944000 rms 0.621 0.179% neg 0 invalid 762 tFOTS 7.2820 tGradient 2.3440 tsec 10.1710 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.619596) vs oldopt=(dt=103.68,rms=0.619635) #GCMRL# 191 dt 124.416000 rms 0.620 0.164% neg 0 invalid 762 tFOTS 7.3340 tGradient 2.1060 tsec 9.9900 #FOTS# QuadFit found better minimum quadopt=(dt=72.4341,rms=0.618619) vs oldopt=(dt=103.68,rms=0.61874) #GCMRL# 192 dt 72.434109 rms 0.619 0.158% neg 0 invalid 762 tFOTS 7.6430 tGradient 2.2570 tsec 10.4560 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.617534) vs oldopt=(dt=103.68,rms=0.617691) #GCMRL# 193 dt 145.152000 rms 0.618 0.175% neg 0 invalid 762 tFOTS 7.3040 tGradient 2.0920 tsec 9.9460 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.6167) vs oldopt=(dt=103.68,rms=0.616811) #GCMRL# 194 dt 82.944000 rms 0.617 0.135% neg 0 invalid 762 tFOTS 7.1960 tGradient 2.1790 tsec 9.9060 #GCMRL# 195 dt 103.680000 rms 0.616 0.137% neg 0 invalid 762 tFOTS 7.2740 tGradient 2.1730 tsec 9.9800 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.615137) vs oldopt=(dt=103.68,rms=0.615159) #GCMRL# 196 dt 82.944000 rms 0.615 0.116% neg 0 invalid 762 tFOTS 7.2370 tGradient 2.1590 tsec 9.9470 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.6143) vs oldopt=(dt=103.68,rms=0.614359) #GCMRL# 197 dt 145.152000 rms 0.614 0.136% neg 0 invalid 762 tFOTS 7.2490 tGradient 2.1370 tsec 9.9170 #FOTS# QuadFit found better minimum quadopt=(dt=62.208,rms=0.613527) vs oldopt=(dt=103.68,rms=0.613754) #GCMRL# 198 dt 62.208000 rms 0.614 0.126% neg 0 invalid 762 tFOTS 7.6630 tGradient 2.1760 tsec 10.3690 #FOTS# QuadFit found better minimum quadopt=(dt=331.776,rms=0.612194) vs oldopt=(dt=414.72,rms=0.612322) #GCMRL# 199 dt 331.776000 rms 0.612 0.217% neg 0 invalid 762 tFOTS 7.2720 tGradient 2.2820 tsec 10.1130 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.610992) vs oldopt=(dt=25.92,rms=0.611204) #GCMRL# 200 dt 36.288000 rms 0.611 0.196% neg 0 invalid 762 tFOTS 7.2320 tGradient 2.2280 tsec 10.0020 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.610397) vs oldopt=(dt=103.68,rms=0.610434) #GCMRL# 201 dt 145.152000 rms 0.610 0.097% neg 0 invalid 762 tFOTS 7.2340 tGradient 2.2120 tsec 9.9920 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.60973) vs oldopt=(dt=103.68,rms=0.609804) #GCMRL# 202 dt 82.944000 rms 0.610 0.109% neg 0 invalid 762 tFOTS 7.2070 tGradient 2.2330 tsec 9.9670 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.609231) vs oldopt=(dt=103.68,rms=0.60924) #GCMRL# 203 dt 82.944000 rms 0.609 0.082% neg 0 invalid 762 tFOTS 7.2220 tGradient 2.2210 tsec 9.9740 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.608563) vs oldopt=(dt=103.68,rms=0.608644) #GCMRL# 204 dt 145.152000 rms 0.609 0.110% neg 0 invalid 762 tFOTS 7.2830 tGradient 2.2310 tsec 10.0610 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.608168) vs oldopt=(dt=25.92,rms=0.608248) #GCMRL# 205 dt 36.288000 rms 0.608 0.065% neg 0 invalid 762 tFOTS 7.5970 tGradient 2.2380 tsec 10.3680 #GCMRL# 206 dt 414.720000 rms 0.607 0.173% neg 0 invalid 762 tFOTS 6.8150 tGradient 2.2370 tsec 9.5790 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.60609) vs oldopt=(dt=25.92,rms=0.606306) #GCMRL# 207 dt 36.288000 rms 0.606 0.169% neg 0 invalid 762 tFOTS 7.2410 tGradient 2.2130 tsec 9.9900 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.605556) vs oldopt=(dt=103.68,rms=0.605575) #GCMRL# 208 dt 124.416000 rms 0.606 0.088% neg 0 invalid 762 tFOTS 7.1680 tGradient 2.2100 tsec 9.9220 #FOTS# QuadFit found better minimum quadopt=(dt=62.208,rms=0.605162) vs oldopt=(dt=103.68,rms=0.60527) #GCMRL# 209 dt 62.208000 rms 0.605 0.065% neg 0 invalid 762 tFOTS 7.1830 tGradient 2.2250 tsec 9.9400 #GCMRL# 210 dt 414.720000 rms 0.604 0.169% neg 0 invalid 762 tFOTS 7.1710 tGradient 2.2840 tsec 9.9870 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.603245) vs oldopt=(dt=25.92,rms=0.603391) #GCMRL# 211 dt 36.288000 rms 0.603 0.148% neg 0 invalid 762 tFOTS 7.1600 tGradient 2.1630 tsec 9.8590 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.603012) vs oldopt=(dt=103.68,rms=0.603013) #GCMRL# 212 dt 82.944000 rms 0.603 0.000% neg 0 invalid 762 tFOTS 7.1530 tGradient 2.2330 tsec 9.9380 #GCMRL# 213 dt 82.944000 rms 0.603 0.064% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1890 tsec 2.7230 #GCMRL# 214 dt 82.944000 rms 0.602 0.101% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1880 tsec 2.7230 #GCMRL# 215 dt 82.944000 rms 0.601 0.134% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2390 tsec 2.7730 #GCMRL# 216 dt 82.944000 rms 0.600 0.152% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1620 tsec 2.6950 #GCMRL# 217 dt 82.944000 rms 0.599 0.205% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1370 tsec 2.6850 #GCMRL# 218 dt 82.944000 rms 0.598 0.214% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1700 tsec 2.7260 #GCMRL# 219 dt 82.944000 rms 0.596 0.232% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1580 tsec 2.6980 #GCMRL# 220 dt 82.944000 rms 0.595 0.212% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1970 tsec 2.7510 #GCMRL# 221 dt 82.944000 rms 0.594 0.212% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2500 tsec 2.8110 #GCMRL# 222 dt 82.944000 rms 0.593 0.204% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2310 tsec 2.7780 #GCMRL# 223 dt 82.944000 rms 0.591 0.209% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2510 tsec 2.8070 #GCMRL# 224 dt 82.944000 rms 0.590 0.180% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2510 tsec 2.8100 #GCMRL# 225 dt 82.944000 rms 0.589 0.175% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2320 tsec 2.7770 #GCMRL# 226 dt 82.944000 rms 0.588 0.183% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2040 tsec 2.7390 #GCMRL# 227 dt 82.944000 rms 0.587 0.171% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1810 tsec 2.7150 #GCMRL# 228 dt 82.944000 rms 0.586 0.153% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1370 tsec 2.6720 #GCMRL# 229 dt 82.944000 rms 0.586 0.144% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1720 tsec 2.7070 #GCMRL# 230 dt 82.944000 rms 0.585 0.139% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2430 tsec 2.7800 #GCMRL# 231 dt 82.944000 rms 0.584 0.122% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1680 tsec 2.7020 #GCMRL# 232 dt 82.944000 rms 0.583 0.117% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1510 tsec 2.6860 #GCMRL# 233 dt 82.944000 rms 0.583 0.130% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2220 tsec 2.7660 #GCMRL# 234 dt 82.944000 rms 0.582 0.121% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2190 tsec 2.7550 #GCMRL# 235 dt 82.944000 rms 0.581 0.095% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2390 tsec 2.7740 #GCMRL# 236 dt 82.944000 rms 0.581 0.096% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3120 tsec 2.8440 #GCMRL# 237 dt 82.944000 rms 0.580 0.105% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2730 tsec 2.8110 #GCMRL# 238 dt 82.944000 rms 0.580 0.103% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2200 tsec 2.7570 #GCMRL# 239 dt 82.944000 rms 0.579 0.082% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1960 tsec 2.7370 #GCMRL# 240 dt 82.944000 rms 0.579 0.082% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2170 tsec 2.7600 #GCMRL# 241 dt 82.944000 rms 0.578 0.081% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2230 tsec 2.7570 #GCMRL# 242 dt 82.944000 rms 0.578 0.080% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2950 tsec 2.8490 #GCMRL# 243 dt 82.944000 rms 0.577 0.062% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2660 tsec 2.8010 #GCMRL# 244 dt 82.944000 rms 0.577 0.057% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3250 tsec 2.8580 #GCMRL# 245 dt 82.944000 rms 0.577 0.073% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3850 tsec 2.9190 #GCMRL# 246 dt 82.944000 rms 0.576 0.076% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3710 tsec 2.9070 #GCMRL# 247 dt 82.944000 rms 0.576 0.075% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3390 tsec 2.8780 #GCMRL# 248 dt 82.944000 rms 0.575 0.065% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3510 tsec 2.8920 #GCMRL# 249 dt 82.944000 rms 0.575 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2670 tsec 2.8210 #GCMRL# 250 dt 82.944000 rms 0.575 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2690 tsec 2.8150 #GCMRL# 251 dt 82.944000 rms 0.574 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2630 tsec 2.8220 #GCMRL# 252 dt 82.944000 rms 0.574 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2420 tsec 2.7890 #GCMRL# 253 dt 82.944000 rms 0.574 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2780 tsec 2.8420 #GCMRL# 254 dt 82.944000 rms 0.574 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2060 tsec 2.7730 #GCMRL# 255 dt 82.944000 rms 0.573 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2520 tsec 2.7920 #GCMRL# 256 dt 82.944000 rms 0.573 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2600 tsec 2.8000 #GCMRL# 257 dt 82.944000 rms 0.573 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1990 tsec 2.7370 #GCMRL# 258 dt 82.944000 rms 0.573 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2640 tsec 2.8060 #GCMRL# 259 dt 82.944000 rms 0.572 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2910 tsec 2.8260 #GCMRL# 260 dt 82.944000 rms 0.572 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2950 tsec 2.8390 #GCMRL# 261 dt 82.944000 rms 0.572 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3390 tsec 2.8780 #GCMRL# 262 dt 82.944000 rms 0.572 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2190 tsec 2.7630 #GCMRL# 263 dt 82.944000 rms 0.571 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2750 tsec 2.8110 #GCMRL# 264 dt 82.944000 rms 0.571 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2460 tsec 2.7820 #GCMRL# 265 dt 82.944000 rms 0.571 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2370 tsec 2.7730 #GCMRL# 266 dt 82.944000 rms 0.571 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4370 tsec 2.9730 #GCMRL# 267 dt 82.944000 rms 0.571 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2690 tsec 3.0880 #GCMRL# 268 dt 82.944000 rms 0.571 0.008% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2150 tsec 3.0440 #GCMRL# 269 dt 41.472000 rms 0.571 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2600 tsec 3.3720 #GCMRL# 270 dt 25.920000 rms 0.571 0.000% neg 0 invalid 762 tFOTS 6.3580 tGradient 2.2440 tsec 9.1650 #GCMRL# 271 dt 1.620000 rms 0.571 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2500 tsec 4.1280 #GCMRL# 272 dt 0.202500 rms 0.571 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2460 tsec 3.8760 #GCAMreg# pass 0 level1 4 level2 1 tsec 514.337 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 0.571407 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.568645) vs oldopt=(dt=103.68,rms=0.568865) #GCMRL# 274 dt 145.152000 rms 0.569 0.483% neg 0 invalid 762 tFOTS 6.8190 tGradient 2.2530 tsec 9.6150 #GCMRL# 275 dt 103.680000 rms 0.568 0.131% neg 0 invalid 762 tFOTS 6.7660 tGradient 2.3270 tsec 9.6280 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.567531) vs oldopt=(dt=103.68,rms=0.567536) #GCMRL# 276 dt 82.944000 rms 0.568 0.065% neg 0 invalid 762 tFOTS 6.7360 tGradient 2.3150 tsec 9.5820 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.567146) vs oldopt=(dt=103.68,rms=0.567173) #GCMRL# 277 dt 145.152000 rms 0.567 0.068% neg 0 invalid 762 tFOTS 7.1850 tGradient 2.2910 tsec 10.0090 #FOTS# QuadFit found better minimum quadopt=(dt=62.208,rms=0.566864) vs oldopt=(dt=103.68,rms=0.566916) #GCMRL# 278 dt 62.208000 rms 0.567 0.000% neg 0 invalid 762 tFOTS 6.8630 tGradient 2.3730 tsec 9.8200 #GCMRL# 279 dt 62.208000 rms 0.567 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3060 tsec 2.8390 #GCMRL# 280 dt 62.208000 rms 0.566 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3350 tsec 2.8720 #GCMRL# 281 dt 62.208000 rms 0.566 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3170 tsec 2.8530 #GCMRL# 282 dt 62.208000 rms 0.566 0.047% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3110 tsec 2.8450 #GCMRL# 283 dt 62.208000 rms 0.566 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1690 tsec 2.7070 #GCMRL# 284 dt 62.208000 rms 0.565 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2160 tsec 2.7520 #GCMRL# 285 dt 62.208000 rms 0.565 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1990 tsec 2.7300 #GCMRL# 286 dt 62.208000 rms 0.565 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2240 tsec 2.7610 #GCMRL# 287 dt 62.208000 rms 0.565 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2220 tsec 2.7550 #GCMRL# 288 dt 62.208000 rms 0.565 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2240 tsec 2.7600 #GCMRL# 289 dt 62.208000 rms 0.564 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2370 tsec 2.7740 #GCMRL# 290 dt 62.208000 rms 0.564 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2000 tsec 2.7380 #GCMRL# 291 dt 62.208000 rms 0.564 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2210 tsec 2.7540 #GCMRL# 292 dt 62.208000 rms 0.563 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2840 tsec 2.8240 #GCMRL# 293 dt 62.208000 rms 0.563 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2970 tsec 2.8540 #GCMRL# 294 dt 62.208000 rms 0.563 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1980 tsec 2.7470 #GCMRL# 295 dt 62.208000 rms 0.563 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3010 tsec 2.8500 #GCMRL# 296 dt 62.208000 rms 0.563 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2490 tsec 2.8080 #GCMRL# 297 dt 62.208000 rms 0.562 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1770 tsec 2.7180 #GCMRL# 298 dt 62.208000 rms 0.562 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2200 tsec 2.7610 #GCMRL# 299 dt 62.208000 rms 0.562 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2130 tsec 2.7620 #GCMRL# 300 dt 62.208000 rms 0.562 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2850 tsec 2.8300 #GCMRL# 301 dt 62.208000 rms 0.562 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2680 tsec 2.8050 #GCMRL# 302 dt 62.208000 rms 0.561 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2570 tsec 2.8140 #GCMRL# 303 dt 62.208000 rms 0.561 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3080 tsec 2.8480 #GCMRL# 304 dt 62.208000 rms 0.561 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2780 tsec 2.8110 #GCMRL# 305 dt 62.208000 rms 0.561 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2310 tsec 2.7720 #GCMRL# 306 dt 62.208000 rms 0.561 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2730 tsec 2.8360 #GCMRL# 307 dt 62.208000 rms 0.561 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2910 tsec 2.8470 #GCMRL# 308 dt 62.208000 rms 0.561 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2350 tsec 2.7720 #GCMRL# 309 dt 62.208000 rms 0.560 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3570 tsec 2.9030 #GCMRL# 310 dt 62.208000 rms 0.560 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3370 tsec 2.8950 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.560186) vs oldopt=(dt=103.68,rms=0.560194) #GCMRL# 311 dt 145.152000 rms 0.560 0.000% neg 0 invalid 762 tFOTS 7.1860 tGradient 2.2640 tsec 10.0200 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 0.574892 #FOTS# QuadFit found better minimum quadopt=(dt=19.2,rms=0.573251) vs oldopt=(dt=32,rms=0.573448) #GCMRL# 313 dt 19.200000 rms 0.573 0.285% neg 0 invalid 762 tFOTS 7.3020 tGradient 2.1450 tsec 9.9860 #GCMRL# 314 dt 32.000000 rms 0.573 0.114% neg 0 invalid 762 tFOTS 7.2680 tGradient 2.1210 tsec 9.9400 #FOTS# QuadFit found better minimum quadopt=(dt=102.4,rms=0.570495) vs oldopt=(dt=128,rms=0.570684) #GCMRL# 315 dt 102.400000 rms 0.570 0.367% neg 0 invalid 762 tFOTS 7.2100 tGradient 2.0170 tsec 9.7640 #FOTS# QuadFit found better minimum quadopt=(dt=179.2,rms=0.560902) vs oldopt=(dt=128,rms=0.562643) #GCMRL# 316 dt 179.200000 rms 0.561 1.682% neg 0 invalid 762 tFOTS 7.2310 tGradient 2.1170 tsec 9.8820 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.559727) vs oldopt=(dt=32,rms=0.559729) #GCMRL# 317 dt 38.400000 rms 0.560 0.209% neg 0 invalid 762 tFOTS 6.8170 tGradient 2.2600 tsec 9.6210 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.558681) vs oldopt=(dt=32,rms=0.558849) #GCMRL# 318 dt 44.800000 rms 0.559 0.187% neg 0 invalid 762 tFOTS 7.1920 tGradient 2.2540 tsec 9.9770 #FOTS# QuadFit found better minimum quadopt=(dt=76.8,rms=0.557041) vs oldopt=(dt=128,rms=0.557276) #GCMRL# 319 dt 76.800000 rms 0.557 0.293% neg 0 invalid 762 tFOTS 6.7920 tGradient 2.1520 tsec 9.4790 #GCMRL# 320 dt 32.000000 rms 0.556 0.189% neg 0 invalid 762 tFOTS 7.1400 tGradient 2.1070 tsec 9.7780 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.554952) vs oldopt=(dt=32,rms=0.555056) #GCMRL# 321 dt 44.800000 rms 0.555 0.186% neg 0 invalid 762 tFOTS 7.1670 tGradient 2.0640 tsec 9.7610 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.554308) vs oldopt=(dt=32,rms=0.554335) #GCMRL# 322 dt 25.600000 rms 0.554 0.116% neg 0 invalid 762 tFOTS 7.1930 tGradient 1.9090 tsec 9.6370 #FOTS# QuadFit found better minimum quadopt=(dt=102.4,rms=0.55294) vs oldopt=(dt=128,rms=0.553114) #GCMRL# 323 dt 102.400000 rms 0.553 0.247% neg 0 invalid 762 tFOTS 6.7710 tGradient 1.9650 tsec 9.2810 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.551986) vs oldopt=(dt=8,rms=0.55219) #GCMRL# 324 dt 11.200000 rms 0.552 0.173% neg 0 invalid 762 tFOTS 7.2020 tGradient 1.9110 tsec 9.6440 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.551487) vs oldopt=(dt=32,rms=0.5515) #GCMRL# 325 dt 25.600000 rms 0.551 0.090% neg 0 invalid 762 tFOTS 7.1370 tGradient 1.9100 tsec 9.5800 #FOTS# QuadFit found better minimum quadopt=(dt=102.4,rms=0.550392) vs oldopt=(dt=128,rms=0.550516) #GCMRL# 326 dt 102.400000 rms 0.550 0.198% neg 0 invalid 762 tFOTS 7.2280 tGradient 1.9250 tsec 9.6880 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.549692) vs oldopt=(dt=8,rms=0.549838) #GCMRL# 327 dt 11.200000 rms 0.550 0.127% neg 0 invalid 762 tFOTS 6.7250 tGradient 1.9610 tsec 9.2180 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.549327) vs oldopt=(dt=32,rms=0.549344) #GCMRL# 328 dt 25.600000 rms 0.549 0.066% neg 0 invalid 762 tFOTS 7.1320 tGradient 1.9130 tsec 9.6030 #FOTS# QuadFit found better minimum quadopt=(dt=102.4,rms=0.548404) vs oldopt=(dt=128,rms=0.548476) #GCMRL# 329 dt 102.400000 rms 0.548 0.168% neg 0 invalid 762 tFOTS 7.1900 tGradient 1.9410 tsec 9.6610 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.547853) vs oldopt=(dt=8,rms=0.547971) #GCMRL# 330 dt 11.200000 rms 0.548 0.100% neg 0 invalid 762 tFOTS 6.8030 tGradient 1.9320 tsec 9.2930 #GCMRL# 331 dt 32.000000 rms 0.548 0.061% neg 0 invalid 762 tFOTS 7.1080 tGradient 1.9440 tsec 9.5810 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.546943) vs oldopt=(dt=32,rms=0.546999) #GCMRL# 332 dt 44.800000 rms 0.547 0.105% neg 0 invalid 762 tFOTS 7.1530 tGradient 1.9070 tsec 9.5920 #FOTS# QuadFit found better minimum quadopt=(dt=19.2,rms=0.546721) vs oldopt=(dt=32,rms=0.54682) #GCMRL# 333 dt 19.200000 rms 0.547 0.000% neg 0 invalid 762 tFOTS 6.7690 tGradient 1.9600 tsec 9.2850 #GCMRL# 334 dt 19.200000 rms 0.546 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9070 tsec 2.4400 #GCMRL# 335 dt 19.200000 rms 0.546 0.071% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9140 tsec 2.4700 #GCMRL# 336 dt 19.200000 rms 0.545 0.099% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9870 tsec 2.5270 #GCMRL# 337 dt 19.200000 rms 0.545 0.120% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9060 tsec 2.4370 #GCMRL# 338 dt 19.200000 rms 0.544 0.141% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9540 tsec 2.4890 #GCMRL# 339 dt 19.200000 rms 0.543 0.157% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9560 tsec 2.4940 #GCMRL# 340 dt 19.200000 rms 0.542 0.160% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9860 tsec 2.5340 #GCMRL# 341 dt 19.200000 rms 0.541 0.167% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9230 tsec 2.4640 #GCMRL# 342 dt 19.200000 rms 0.541 0.166% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9070 tsec 2.4420 #GCMRL# 343 dt 19.200000 rms 0.540 0.164% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9140 tsec 2.4470 #GCMRL# 344 dt 19.200000 rms 0.540 0.018% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8810 tsec 2.6860 #GCMRL# 345 dt 19.200000 rms 0.539 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9110 tsec 2.4480 #GCMRL# 346 dt 19.200000 rms 0.539 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9110 tsec 2.4470 #GCMRL# 347 dt 19.200000 rms 0.539 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9070 tsec 2.4410 #GCMRL# 348 dt 19.200000 rms 0.539 0.059% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9050 tsec 2.4430 #GCMRL# 349 dt 19.200000 rms 0.538 0.068% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8860 tsec 2.4210 #GCMRL# 350 dt 19.200000 rms 0.538 0.073% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8820 tsec 2.4320 #GCMRL# 351 dt 19.200000 rms 0.537 0.075% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8920 tsec 2.4640 #GCMRL# 352 dt 19.200000 rms 0.537 0.083% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8780 tsec 2.4140 #GCMRL# 353 dt 19.200000 rms 0.537 0.092% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8880 tsec 2.4200 #GCMRL# 354 dt 19.200000 rms 0.536 0.093% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8810 tsec 2.4180 #GCMRL# 355 dt 19.200000 rms 0.536 0.094% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8880 tsec 2.4390 #GCMRL# 356 dt 19.200000 rms 0.535 0.097% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8830 tsec 2.4280 #GCMRL# 357 dt 19.200000 rms 0.534 0.094% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8990 tsec 2.4390 #GCMRL# 358 dt 19.200000 rms 0.534 0.013% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8900 tsec 2.6950 #GCMRL# 359 dt 19.200000 rms 0.534 0.020% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8850 tsec 2.4200 #GCMRL# 360 dt 19.200000 rms 0.534 0.022% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8850 tsec 2.4310 #GCMRL# 361 dt 19.200000 rms 0.534 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8930 tsec 2.4300 #GCMRL# 362 dt 19.200000 rms 0.534 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9040 tsec 2.4400 #GCMRL# 363 dt 19.200000 rms 0.534 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8910 tsec 2.4270 #GCMRL# 364 dt 19.200000 rms 0.533 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8790 tsec 2.4380 #GCMRL# 365 dt 19.200000 rms 0.533 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9070 tsec 2.4400 #GCMRL# 366 dt 19.200000 rms 0.533 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9270 tsec 2.4610 #GCMRL# 367 dt 19.200000 rms 0.533 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9260 tsec 2.4610 #GCMRL# 368 dt 19.200000 rms 0.532 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9280 tsec 2.4640 #GCMRL# 369 dt 19.200000 rms 0.532 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8850 tsec 2.4190 #GCMRL# 370 dt 19.200000 rms 0.532 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8830 tsec 2.4150 #GCMRL# 371 dt 19.200000 rms 0.532 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8780 tsec 2.4110 #GCMRL# 372 dt 19.200000 rms 0.531 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8810 tsec 2.4120 #GCMRL# 373 dt 19.200000 rms 0.531 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8810 tsec 2.4140 #GCMRL# 374 dt 19.200000 rms 0.531 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8810 tsec 2.4140 #GCMRL# 375 dt 19.200000 rms 0.530 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8770 tsec 2.4130 #GCMRL# 376 dt 19.200000 rms 0.530 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8870 tsec 2.4250 #GCMRL# 377 dt 19.200000 rms 0.530 0.012% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8800 tsec 2.6820 #GCMRL# 378 dt 19.200000 rms 0.530 0.008% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8960 tsec 2.7520 #FOTS# QuadFit found better minimum quadopt=(dt=179.2,rms=0.529945) vs oldopt=(dt=128,rms=0.529956) #GCMRL# 379 dt 179.200000 rms 0.530 0.026% neg 0 invalid 762 tFOTS 6.8640 tGradient 1.8930 tsec 9.3180 #GCMRL# 380 dt 32.000000 rms 0.530 0.000% neg 0 invalid 762 tFOTS 6.8120 tGradient 1.9130 tsec 9.2800 #GCAMreg# pass 0 level1 3 level2 1 tsec 337.213 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 0.530387 #GCMRL# 382 dt 32.000000 rms 0.529 0.263% neg 0 invalid 762 tFOTS 7.2090 tGradient 1.9240 tsec 9.6620 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.52871) vs oldopt=(dt=32,rms=0.528723) #GCMRL# 383 dt 25.600000 rms 0.529 0.053% neg 0 invalid 762 tFOTS 7.1760 tGradient 1.8780 tsec 9.5900 #FOTS# QuadFit found better minimum quadopt=(dt=1.6,rms=0.528723) vs oldopt=(dt=2,rms=0.528723) #GCMRL# 384 dt 1.600000 rms 0.529 0.000% neg 0 invalid 762 tFOTS 7.2180 tGradient 1.8880 tsec 10.0820 #GCMRL# 385 dt 1.600000 rms 0.529 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9050 tsec 2.4530 #GCMRL# 386 dt 1.600000 rms 0.529 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8820 tsec 2.4170 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 0.56309 #GCMRL# 388 dt 0.000000 rms 0.563 0.093% neg 0 invalid 762 tFOTS 6.3300 tGradient 1.8120 tsec 8.6630 #GCMRL# 389 dt 0.150000 rms 0.563 0.000% neg 0 invalid 762 tFOTS 6.3370 tGradient 1.8320 tsec 9.1590 #GCAMreg# pass 0 level1 2 level2 1 tsec 23.105 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 0.56309 #GCMRL# 391 dt 0.000000 rms 0.563 0.093% neg 0 invalid 762 tFOTS 6.2970 tGradient 1.8150 tsec 8.6360 #GCMRL# 392 dt 0.150000 rms 0.563 0.000% neg 0 invalid 762 tFOTS 6.2810 tGradient 1.8070 tsec 9.0820 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 0.628947 #GCMRL# 394 dt 1.280000 rms 0.620 1.395% neg 0 invalid 762 tFOTS 6.3000 tGradient 1.7600 tsec 8.5830 #FOTS# QuadFit found better minimum quadopt=(dt=1.792,rms=0.616613) vs oldopt=(dt=1.28,rms=0.616921) #GCMRL# 395 dt 1.792000 rms 0.617 0.574% neg 0 invalid 762 tFOTS 6.4710 tGradient 1.7880 tsec 8.8130 #FOTS# QuadFit found better minimum quadopt=(dt=0.768,rms=0.616244) vs oldopt=(dt=1.28,rms=0.616341) #GCMRL# 396 dt 0.768000 rms 0.616 0.060% neg 0 invalid 762 tFOTS 6.3770 tGradient 1.7820 tsec 8.6920 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.616191) vs oldopt=(dt=0.32,rms=0.616193) #GCMRL# 397 dt 0.384000 rms 0.616 0.000% neg 0 invalid 762 tFOTS 6.8840 tGradient 1.7560 tsec 9.1990 #GCMRL# 398 dt 0.384000 rms 0.616 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.7640 tsec 2.3120 #GCAMreg# pass 0 level1 1 level2 1 tsec 42.853 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 0.616675 #FOTS# QuadFit found better minimum quadopt=(dt=1.792,rms=0.614747) vs oldopt=(dt=1.28,rms=0.614907) #GCMRL# 400 dt 1.792000 rms 0.615 0.313% neg 0 invalid 762 tFOTS 6.7340 tGradient 1.7730 tsec 9.0310 #FOTS# QuadFit found better minimum quadopt=(dt=1.536,rms=0.614343) vs oldopt=(dt=1.28,rms=0.614344) #GCMRL# 401 dt 1.536000 rms 0.614 0.066% neg 0 invalid 762 tFOTS 6.8210 tGradient 1.8010 tsec 9.1750 #FOTS# QuadFit found better minimum quadopt=(dt=0.768,rms=0.614274) vs oldopt=(dt=1.28,rms=0.614297) #GCMRL# 402 dt 0.768000 rms 0.614 0.000% neg 0 invalid 762 tFOTS 6.8470 tGradient 1.7860 tsec 9.1890 #GCMRL# 403 dt 0.768000 rms 0.614 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8080 tsec 2.3430 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 0.574497 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.558815) vs oldopt=(dt=0.32,rms=0.563002) #GCMRL# 405 dt 0.448000 rms 0.559 2.730% neg 0 invalid 762 tFOTS 6.3330 tGradient 1.2710 tsec 8.1270 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.554131) vs oldopt=(dt=0.32,rms=0.555448) #GCMRL# 406 dt 0.448000 rms 0.554 0.838% neg 0 invalid 762 tFOTS 6.4720 tGradient 1.2310 tsec 8.2700 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.55142) vs oldopt=(dt=0.32,rms=0.552181) #GCMRL# 407 dt 0.448000 rms 0.551 0.489% neg 0 invalid 762 tFOTS 6.4460 tGradient 1.2280 tsec 8.2170 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.54963) vs oldopt=(dt=0.32,rms=0.550134) #GCMRL# 408 dt 0.448000 rms 0.550 0.325% neg 0 invalid 762 tFOTS 6.3370 tGradient 1.2110 tsec 8.0830 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.548269) vs oldopt=(dt=0.32,rms=0.548653) #GCMRL# 409 dt 0.448000 rms 0.548 0.248% neg 0 invalid 762 tFOTS 6.3240 tGradient 1.1660 tsec 8.0230 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.547212) vs oldopt=(dt=0.32,rms=0.547507) #GCMRL# 410 dt 0.448000 rms 0.547 0.193% neg 0 invalid 762 tFOTS 6.4860 tGradient 1.2120 tsec 8.2540 #FOTS# QuadFit found better minimum quadopt=(dt=0.464286,rms=0.54631) vs oldopt=(dt=0.32,rms=0.546586) #GCMRL# 411 dt 0.464286 rms 0.546 0.165% neg 0 invalid 762 tFOTS 6.3650 tGradient 1.2120 tsec 8.1200 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.545606) vs oldopt=(dt=0.32,rms=0.545803) #GCMRL# 412 dt 0.448000 rms 0.546 0.129% neg 0 invalid 762 tFOTS 6.4500 tGradient 1.2130 tsec 8.2120 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.545) vs oldopt=(dt=0.32,rms=0.54517) #GCMRL# 413 dt 0.448000 rms 0.545 0.111% neg 0 invalid 762 tFOTS 6.3700 tGradient 1.2230 tsec 8.1210 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.544484) vs oldopt=(dt=0.32,rms=0.544625) #GCMRL# 414 dt 0.448000 rms 0.544 0.095% neg 0 invalid 762 tFOTS 6.3780 tGradient 1.2140 tsec 8.1240 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.54404) vs oldopt=(dt=0.32,rms=0.544166) #GCMRL# 415 dt 0.448000 rms 0.544 0.082% neg 0 invalid 762 tFOTS 6.4690 tGradient 1.2100 tsec 8.2210 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.543651) vs oldopt=(dt=0.32,rms=0.543756) #GCMRL# 416 dt 0.448000 rms 0.544 0.071% neg 0 invalid 762 tFOTS 6.4350 tGradient 1.2210 tsec 8.2040 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.543297) vs oldopt=(dt=0.32,rms=0.543395) #GCMRL# 417 dt 0.448000 rms 0.543 0.065% neg 0 invalid 762 tFOTS 6.3830 tGradient 1.2320 tsec 8.1680 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.543008) vs oldopt=(dt=0.32,rms=0.543088) #GCMRL# 418 dt 0.448000 rms 0.543 0.053% neg 0 invalid 762 tFOTS 6.4540 tGradient 1.2180 tsec 8.2230 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.542715) vs oldopt=(dt=0.32,rms=0.542793) #GCMRL# 419 dt 0.448000 rms 0.543 0.054% neg 0 invalid 762 tFOTS 6.4250 tGradient 1.2090 tsec 8.2040 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.542486) vs oldopt=(dt=0.32,rms=0.542548) #GCMRL# 420 dt 0.448000 rms 0.542 0.000% neg 0 invalid 762 tFOTS 6.4160 tGradient 1.2220 tsec 8.1930 #GCMRL# 421 dt 0.448000 rms 0.542 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.2510 tsec 1.8270 #GCMRL# 422 dt 0.448000 rms 0.542 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.2360 tsec 1.7740 #GCMRL# 423 dt 0.448000 rms 0.541 0.093% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.2140 tsec 1.7610 #GCMRL# 424 dt 0.448000 rms 0.541 0.106% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.2250 tsec 1.7650 #GCMRL# 425 dt 0.448000 rms 0.540 0.110% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.2290 tsec 1.7750 #GCMRL# 426 dt 0.448000 rms 0.540 0.097% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.2150 tsec 1.7750 #GCMRL# 427 dt 0.448000 rms 0.539 0.082% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.2230 tsec 1.7630 #GCMRL# 428 dt 0.448000 rms 0.539 0.065% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.2170 tsec 1.7580 #GCMRL# 429 dt 0.448000 rms 0.539 -0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.2270 tsec 2.4740 #FOTS# QuadFit found better minimum quadopt=(dt=0.028,rms=0.538859) vs oldopt=(dt=0.02,rms=0.538859) #GCMRL# 430 dt 0.028000 rms 0.539 0.000% neg 0 invalid 762 tFOTS 6.3260 tGradient 1.2140 tsec 8.0680 #GCMRL# 431 dt 0.005000 rms 0.539 0.000% neg 0 invalid 762 tFOTS 6.3750 tGradient 1.2210 tsec 8.1460 #GCAMreg# pass 0 level1 0 level2 1 tsec 176.895 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 0.539424 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.533043) vs oldopt=(dt=0.32,rms=0.534617) #GCMRL# 433 dt 0.448000 rms 0.533 1.183% neg 0 invalid 762 tFOTS 6.3700 tGradient 1.2220 tsec 8.1180 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.532428) vs oldopt=(dt=0.32,rms=0.532594) #GCMRL# 434 dt 0.448000 rms 0.532 0.115% neg 0 invalid 762 tFOTS 6.4580 tGradient 1.2050 tsec 8.2100 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.53232) vs oldopt=(dt=0.32,rms=0.532348) #GCMRL# 435 dt 0.448000 rms 0.532 0.000% neg 0 invalid 762 tFOTS 6.3870 tGradient 1.2280 tsec 8.1890 GCAMregister done in 30.7392 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 0.529401 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.528364) vs oldopt=(dt=92.48,rms=0.528434) #GCMRL# 437 dt 129.472000 rms 0.528 0.196% neg 0 invalid 762 tFOTS 7.5940 tGradient 2.4510 tsec 10.5730 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.528127) vs oldopt=(dt=92.48,rms=0.528176) #GCMRL# 438 dt 129.472000 rms 0.528 0.000% neg 0 invalid 762 tFOTS 7.6590 tGradient 2.4460 tsec 10.6710 #GCMRL# 439 dt 129.472000 rms 0.528 0.008% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4480 tsec 2.9910 #GCMRL# 440 dt 129.472000 rms 0.528 0.014% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4420 tsec 2.9780 #GCMRL# 441 dt 129.472000 rms 0.528 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4450 tsec 2.9810 #GCAMreg# pass 0 level1 5 level2 1 tsec 36.111 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 0.528562 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.527391) vs oldopt=(dt=92.48,rms=0.527512) #GCMRL# 443 dt 129.472000 rms 0.527 0.221% neg 0 invalid 762 tFOTS 7.6190 tGradient 2.4400 tsec 10.5830 #FOTS# QuadFit found better minimum quadopt=(dt=443.904,rms=0.526909) vs oldopt=(dt=369.92,rms=0.526917) #GCMRL# 444 dt 443.904000 rms 0.527 0.000% neg 0 invalid 762 tFOTS 7.6430 tGradient 2.4820 tsec 10.7080 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 0.527483 #FOTS# QuadFit found better minimum quadopt=(dt=82.1333,rms=0.525243) vs oldopt=(dt=103.68,rms=0.525362) iter 0, gcam->neg = 2 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 446 dt 82.133333 rms 0.525 0.424% neg 0 invalid 762 tFOTS 8.1680 tGradient 2.0910 tsec 11.4710 iter 0, gcam->neg = 3 after 9 iterations, nbhd size=1, neg = 0 #GCMRL# 447 dt 103.680000 rms 0.524 0.000% neg 0 invalid 762 tFOTS 7.6390 tGradient 2.0560 tsec 13.7550 iter 0, gcam->neg = 1 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 448 dt 103.680000 rms 0.524 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9920 tsec 4.1330 iter 0, gcam->neg = 4 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 449 dt 103.680000 rms 0.523 0.195% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0420 tsec 3.8720 iter 0, gcam->neg = 7 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 450 dt 103.680000 rms 0.523 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0270 tsec 4.1660 iter 0, gcam->neg = 12 after 13 iterations, nbhd size=1, neg = 0 #GCMRL# 451 dt 103.680000 rms 0.523 -0.109% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0060 tsec 7.8420 #FOTS# QuadFit found better minimum quadopt=(dt=62.208,rms=0.522057) vs oldopt=(dt=103.68,rms=0.522266) #GCMRL# 452 dt 62.208000 rms 0.522 0.116% neg 0 invalid 762 tFOTS 7.6330 tGradient 2.0140 tsec 10.1970 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.521684) vs oldopt=(dt=103.68,rms=0.521716) #GCAMreg# pass 0 level1 4 level2 1 tsec 68.23 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 0.522251 #FOTS# QuadFit found better minimum quadopt=(dt=96.3009,rms=0.518909) vs oldopt=(dt=103.68,rms=0.518931) #GCMRL# 454 dt 96.300940 rms 0.519 0.640% neg 0 invalid 762 tFOTS 8.0120 tGradient 2.0460 tsec 10.5830 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.517925) vs oldopt=(dt=25.92,rms=0.518098) #GCMRL# 455 dt 36.288000 rms 0.518 0.000% neg 0 invalid 762 tFOTS 8.2050 tGradient 2.1180 tsec 10.9050 #GCMRL# 456 dt 36.288000 rms 0.517 0.098% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0960 tsec 2.6470 #GCMRL# 457 dt 36.288000 rms 0.517 0.129% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0390 tsec 2.5760 #GCMRL# 458 dt 36.288000 rms 0.516 0.138% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0460 tsec 2.5820 #GCMRL# 459 dt 36.288000 rms 0.515 0.114% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0720 tsec 2.6080 #GCMRL# 460 dt 36.288000 rms 0.515 0.085% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0370 tsec 2.5960 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.514702) vs oldopt=(dt=103.68,rms=0.514761) #GCMRL# 461 dt 145.152000 rms 0.515 0.000% neg 0 invalid 762 tFOTS 7.6120 tGradient 2.0440 tsec 10.2170 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 0.515954 #FOTS# QuadFit found better minimum quadopt=(dt=61.9487,rms=0.509228) vs oldopt=(dt=32,rms=0.510447) iter 0, gcam->neg = 5 after 8 iterations, nbhd size=1, neg = 0 #GCMRL# 463 dt 61.948718 rms 0.509 1.294% neg 0 invalid 762 tFOTS 8.0990 tGradient 1.8740 tsec 13.7280 #FOTS# QuadFit found better minimum quadopt=(dt=66.9538,rms=0.503375) vs oldopt=(dt=32,rms=0.504781) iter 0, gcam->neg = 5 after 9 iterations, nbhd size=1, neg = 0 #GCMRL# 464 dt 66.953846 rms 0.503 1.159% neg 0 invalid 762 tFOTS 8.1810 tGradient 1.8770 tsec 14.1640 #FOTS# QuadFit found better minimum quadopt=(dt=27.3398,rms=0.500705) vs oldopt=(dt=32,rms=0.50083) iter 0, gcam->neg = 3 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 465 dt 27.339806 rms 0.501 0.530% neg 0 invalid 762 tFOTS 8.1250 tGradient 1.9430 tsec 11.9560 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.498855) vs oldopt=(dt=32,rms=0.499073) #GCMRL# 466 dt 44.800000 rms 0.499 0.370% neg 0 invalid 762 tFOTS 8.0870 tGradient 1.9300 tsec 10.5610 iter 0, gcam->neg = 3 after 7 iterations, nbhd size=1, neg = 0 #GCMRL# 467 dt 32.000000 rms 0.497 0.272% neg 0 invalid 762 tFOTS 8.0800 tGradient 1.8780 tsec 13.3950 #GCMRL# 468 dt 32.000000 rms 0.496 0.000% neg 0 invalid 762 tFOTS 8.0700 tGradient 1.9290 tsec 10.5530 #GCMRL# 469 dt 32.000000 rms 0.495 0.203% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8970 tsec 2.4310 iter 0, gcam->neg = 3 after 8 iterations, nbhd size=1, neg = 0 #GCMRL# 470 dt 32.000000 rms 0.494 0.317% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9570 tsec 5.7280 iter 0, gcam->neg = 4 after 9 iterations, nbhd size=1, neg = 0 #GCMRL# 471 dt 32.000000 rms 0.492 0.363% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9190 tsec 6.0140 iter 0, gcam->neg = 4 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 472 dt 32.000000 rms 0.490 0.435% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8800 tsec 4.0550 iter 0, gcam->neg = 8 after 11 iterations, nbhd size=1, neg = 0 #GCMRL# 473 dt 32.000000 rms 0.488 0.375% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8760 tsec 6.5590 iter 0, gcam->neg = 5 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 474 dt 32.000000 rms 0.486 0.329% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9530 tsec 4.1030 iter 0, gcam->neg = 10 after 7 iterations, nbhd size=0, neg = 0 #GCMRL# 475 dt 32.000000 rms 0.485 0.246% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9490 tsec 5.3680 iter 0, gcam->neg = 9 after 10 iterations, nbhd size=1, neg = 0 #GCMRL# 476 dt 32.000000 rms 0.484 0.261% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9310 tsec 6.3020 iter 0, gcam->neg = 11 after 10 iterations, nbhd size=1, neg = 0 #GCMRL# 477 dt 32.000000 rms 0.483 0.241% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9590 tsec 6.3290 iter 0, gcam->neg = 8 after 5 iterations, nbhd size=0, neg = 0 #GCMRL# 478 dt 32.000000 rms 0.482 0.161% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9040 tsec 4.6950 iter 0, gcam->neg = 11 after 13 iterations, nbhd size=1, neg = 0 #GCMRL# 479 dt 32.000000 rms 0.482 0.098% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8510 tsec 7.3040 iter 0, gcam->neg = 7 after 9 iterations, nbhd size=1, neg = 0 #GCMRL# 480 dt 32.000000 rms 0.481 0.106% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8790 tsec 5.9110 iter 0, gcam->neg = 6 after 8 iterations, nbhd size=1, neg = 0 #GCMRL# 481 dt 32.000000 rms 0.481 0.081% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9530 tsec 5.7100 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.480303) vs oldopt=(dt=32,rms=0.480334) iter 0, gcam->neg = 1 after 1 iterations, nbhd size=0, neg = 0 #GCMRL# 482 dt 25.600000 rms 0.480 0.000% neg 0 invalid 762 tFOTS 8.0920 tGradient 1.9370 tsec 11.6010 #GCMRL# 483 dt 25.600000 rms 0.480 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9430 tsec 2.4860 #GCMRL# 484 dt 25.600000 rms 0.480 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9440 tsec 2.5180 #GCAMreg# pass 0 level1 3 level2 1 tsec 166.475 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 0.480479 #FOTS# QuadFit found better minimum quadopt=(dt=58.0414,rms=0.476684) vs oldopt=(dt=32,rms=0.477119) iter 0, gcam->neg = 1 after 1 iterations, nbhd size=0, neg = 0 #GCMRL# 486 dt 58.041379 rms 0.477 0.788% neg 0 invalid 762 tFOTS 8.0960 tGradient 1.8850 tsec 11.5120 #GCMRL# 487 dt 32.000000 rms 0.476 0.000% neg 0 invalid 762 tFOTS 8.0080 tGradient 1.9250 tsec 10.5220 #GCMRL# 488 dt 32.000000 rms 0.475 0.085% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8870 tsec 2.4360 #GCMRL# 489 dt 32.000000 rms 0.475 0.154% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9350 tsec 2.4730 #GCMRL# 490 dt 32.000000 rms 0.474 0.087% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9590 tsec 2.5050 #GCMRL# 491 dt 32.000000 rms 0.474 0.060% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9160 tsec 2.4910 #GCMRL# 492 dt 32.000000 rms 0.474 0.000% neg 0 invalid 762 tFOTS 8.0300 tGradient 1.8900 tsec 10.4780 #GCMRL# 493 dt 32.000000 rms 0.473 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8590 tsec 2.3930 #GCMRL# 494 dt 32.000000 rms 0.473 0.068% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8900 tsec 2.4230 #GCMRL# 495 dt 32.000000 rms 0.473 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8810 tsec 2.4150 #GCMRL# 496 dt 32.000000 rms 0.472 0.067% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8650 tsec 2.4380 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 0.482736 #GCMRL# 498 dt 0.000000 rms 0.482 0.135% neg 0 invalid 762 tFOTS 7.5960 tGradient 1.8430 tsec 9.9730 #GCAMreg# pass 0 level1 2 level2 1 tsec 22.854 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 0.482736 #GCMRL# 500 dt 0.000000 rms 0.482 0.135% neg 0 invalid 762 tFOTS 7.6520 tGradient 1.8370 tsec 10.0170 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 0.502915 #GCMRL# 502 dt 1.280000 rms 0.500 0.505% neg 0 invalid 762 tFOTS 8.0120 tGradient 1.7840 tsec 10.3180 #FOTS# QuadFit found better minimum quadopt=(dt=0.192,rms=0.500338) vs oldopt=(dt=0.32,rms=0.500346) #GCMRL# 503 dt 0.192000 rms 0.500 0.000% neg 0 invalid 762 tFOTS 8.0340 tGradient 1.7830 tsec 10.3770 #GCAMreg# pass 0 level1 1 level2 1 tsec 25.919 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 0.500967 #FOTS# QuadFit found better minimum quadopt=(dt=1.024,rms=0.499552) vs oldopt=(dt=1.28,rms=0.499582) #GCMRL# 505 dt 1.024000 rms 0.500 0.282% neg 0 invalid 762 tFOTS 8.0480 tGradient 1.7860 tsec 10.3630 #GCMRL# 506 dt 0.320000 rms 0.499 0.000% neg 0 invalid 762 tFOTS 7.9880 tGradient 1.7400 tsec 10.2820 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 0.481589 #FOTS# QuadFit found better minimum quadopt=(dt=2.3587,rms=0.446344) vs oldopt=(dt=1.28,rms=0.453515) iter 0, gcam->neg = 588 after 21 iterations, nbhd size=1, neg = 0 #GCMRL# 508 dt 2.358702 rms 0.449 6.790% neg 0 invalid 762 tFOTS 8.1710 tGradient 1.2360 tsec 17.2800 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.448153) vs oldopt=(dt=0.08,rms=0.448261) #GCMRL# 509 dt 0.112000 rms 0.448 0.000% neg 0 invalid 762 tFOTS 8.0710 tGradient 1.2400 tsec 9.8800 #GCAMreg# pass 0 level1 0 level2 1 tsec 31.901 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 0.448904 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.447674) vs oldopt=(dt=0.08,rms=0.447729) #GCMRL# 511 dt 0.112000 rms 0.448 0.274% neg 0 invalid 762 tFOTS 8.2260 tGradient 1.2670 tsec 10.0440 #FOTS# QuadFit found better minimum quadopt=(dt=0.016,rms=0.447663) vs oldopt=(dt=0.02,rms=0.447663) #GCMRL# 512 dt 0.016000 rms 0.448 0.000% neg 0 invalid 762 tFOTS 8.1470 tGradient 1.2580 tsec 9.9800 label assignment complete, 0 changed (0.00%) GCAMregister done in 9.26202 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 0.432124 #GCAMreg# pass 0 level1 5 level2 1 tsec 12.709 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 0.432124 #FOTS# QuadFit found better minimum quadopt=(dt=32.368,rms=0.43212) vs oldopt=(dt=23.12,rms=0.43212) #GCMRL# 515 dt 32.368000 rms 0.432 0.001% neg 0 invalid 762 tFOTS 7.7670 tGradient 1.9030 tsec 10.1820 #FOTS# QuadFit found better minimum quadopt=(dt=32.368,rms=0.432117) vs oldopt=(dt=23.12,rms=0.432117) #GCMRL# 516 dt 32.368000 rms 0.432 0.000% neg 0 invalid 762 tFOTS 7.7670 tGradient 1.9030 tsec 10.2160 #GCMRL# 517 dt 32.368000 rms 0.432 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9010 tsec 2.4230 #GCMRL# 518 dt 32.368000 rms 0.432 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9000 tsec 2.4210 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 0.432305 #GCMRL# 520 dt 1.620000 rms 0.432 0.000% neg 0 invalid 762 tFOTS 7.7600 tGradient 1.4960 tsec 9.7800 #FOTS# QuadFit found better minimum quadopt=(dt=0.14175,rms=0.432305) vs oldopt=(dt=0.10125,rms=0.432305) #GCMRL# 521 dt 0.141750 rms 0.432 0.000% neg 0 invalid 762 tFOTS 7.8040 tGradient 1.4960 tsec 9.8460 #GCAMreg# pass 0 level1 4 level2 1 tsec 24.523 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 0.432305 #GCMRL# 523 dt 103.680000 rms 0.432 0.089% neg 0 invalid 762 tFOTS 7.4300 tGradient 1.5200 tsec 9.4680 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.431502) vs oldopt=(dt=103.68,rms=0.431554) #GCMRL# 524 dt 145.152000 rms 0.432 0.097% neg 0 invalid 762 tFOTS 7.5280 tGradient 1.5030 tsec 9.5680 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.4314) vs oldopt=(dt=25.92,rms=0.431413) #GCMRL# 525 dt 36.288000 rms 0.431 0.000% neg 0 invalid 762 tFOTS 7.8550 tGradient 1.5140 tsec 9.9120 #GCMRL# 526 dt 36.288000 rms 0.431 0.011% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.5100 tsec 2.0400 #GCMRL# 527 dt 36.288000 rms 0.431 0.019% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.5290 tsec 2.0510 #GCMRL# 528 dt 36.288000 rms 0.431 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.5100 tsec 2.0550 #GCMRL# 529 dt 36.288000 rms 0.431 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.4980 tsec 2.0210 #GCMRL# 530 dt 36.288000 rms 0.431 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.4990 tsec 2.0210 #GCMRL# 531 dt 36.288000 rms 0.431 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.5030 tsec 2.0260 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 0.431332 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.430802) vs oldopt=(dt=8,rms=0.430899) #GCMRL# 533 dt 11.200000 rms 0.431 0.123% neg 0 invalid 762 tFOTS 7.7590 tGradient 1.3140 tsec 9.5840 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.430588) vs oldopt=(dt=8,rms=0.430619) #GCMRL# 534 dt 11.200000 rms 0.431 0.000% neg 0 invalid 762 tFOTS 7.3960 tGradient 1.3200 tsec 9.2620 #GCMRL# 535 dt 11.200000 rms 0.430 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3190 tsec 1.8380 #GCAMreg# pass 0 level1 3 level2 1 tsec 25.446 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 0.430488 #FOTS# QuadFit found better minimum quadopt=(dt=61.265,rms=0.427619) vs oldopt=(dt=32,rms=0.428091) #GCMRL# 537 dt 61.264957 rms 0.428 0.666% neg 0 invalid 762 tFOTS 7.7400 tGradient 1.3220 tsec 9.5700 #GCMRL# 538 dt 32.000000 rms 0.426 0.340% neg 0 invalid 762 tFOTS 7.7360 tGradient 1.3180 tsec 9.5730 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.425499) vs oldopt=(dt=32,rms=0.425516) #GCMRL# 539 dt 38.400000 rms 0.425 0.000% neg 0 invalid 762 tFOTS 7.8670 tGradient 1.3180 tsec 9.7380 #GCMRL# 540 dt 38.400000 rms 0.425 0.158% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3330 tsec 1.8650 #GCMRL# 541 dt 38.400000 rms 0.424 0.285% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3170 tsec 1.8390 iter 0, gcam->neg = 1 after 8 iterations, nbhd size=1, neg = 0 #GCMRL# 542 dt 38.400000 rms 0.423 0.171% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3260 tsec 5.0520 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 543 dt 38.400000 rms 0.421 0.373% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3250 tsec 2.5240 iter 0, gcam->neg = 6 after 10 iterations, nbhd size=1, neg = 0 #GCMRL# 544 dt 38.400000 rms 0.421 0.181% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3260 tsec 5.7890 iter 0, gcam->neg = 8 after 9 iterations, nbhd size=1, neg = 0 #GCMRL# 545 dt 38.400000 rms 0.420 0.207% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3080 tsec 5.4060 iter 0, gcam->neg = 3 after 7 iterations, nbhd size=1, neg = 0 #GCMRL# 546 dt 38.400000 rms 0.419 0.138% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3100 tsec 4.7670 iter 0, gcam->neg = 5 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 547 dt 38.400000 rms 0.418 0.173% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3120 tsec 3.1530 iter 0, gcam->neg = 7 after 1 iterations, nbhd size=0, neg = 0 #GCMRL# 548 dt 38.400000 rms 0.418 0.141% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3600 tsec 2.8630 iter 0, gcam->neg = 2 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 549 dt 38.400000 rms 0.418 0.065% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3300 tsec 2.5410 iter 0, gcam->neg = 4 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 550 dt 38.400000 rms 0.417 0.117% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3150 tsec 3.1280 iter 0, gcam->neg = 3 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 551 dt 38.400000 rms 0.417 0.062% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3240 tsec 3.1540 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.416661) vs oldopt=(dt=8,rms=0.416682) #GCMRL# 552 dt 11.200000 rms 0.417 0.000% neg 0 invalid 762 tFOTS 7.7760 tGradient 1.3100 tsec 9.6390 #GCMRL# 553 dt 11.200000 rms 0.417 0.007% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3160 tsec 1.8330 #GCMRL# 554 dt 11.200000 rms 0.417 0.007% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3240 tsec 1.8500 #GCMRL# 555 dt 11.200000 rms 0.417 0.008% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3230 tsec 1.8440 #GCMRL# 556 dt 11.200000 rms 0.417 0.011% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3190 tsec 1.8410 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 0.421842 #GCAMreg# pass 0 level1 2 level2 1 tsec 11.931 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 0.421842 #GCMRL# 559 dt 0.000703 rms 0.422 0.000% neg 0 invalid 762 tFOTS 7.3020 tGradient 1.2480 tsec 9.0570 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 0.43301 #GCAMreg# pass 0 level1 1 level2 1 tsec 12.057 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 0.43301 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.432869) vs oldopt=(dt=0.32,rms=0.432878) #GCMRL# 562 dt 0.448000 rms 0.433 0.032% neg 0 invalid 762 tFOTS 7.7490 tGradient 1.2000 tsec 9.4640 #FOTS# QuadFit found better minimum quadopt=(dt=0.048,rms=0.432868) vs oldopt=(dt=0.08,rms=0.432868) #GCMRL# 563 dt 0.048000 rms 0.433 0.000% neg 0 invalid 762 tFOTS 7.7670 tGradient 1.1920 tsec 9.5100 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 0.41607 #FOTS# QuadFit found better minimum quadopt=(dt=1.33781,rms=0.404172) vs oldopt=(dt=1.28,rms=0.404223) iter 0, gcam->neg = 569 after 18 iterations, nbhd size=1, neg = 0 #GCMRL# 565 dt 1.337810 rms 0.406 2.466% neg 0 invalid 762 tFOTS 7.7710 tGradient 0.6790 tsec 15.3330 #GCMRL# 566 dt 0.000013 rms 0.406 0.000% neg 0 invalid 762 tFOTS 9.8860 tGradient 0.6700 tsec 11.1100 #GCMRL# 567 dt 0.000013 rms 0.406 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.6830 tsec 1.2310 #GCAMreg# pass 0 level1 0 level2 1 tsec 31.774 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 0.40581 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.40548) vs oldopt=(dt=0.08,rms=0.405517) #GCMRL# 569 dt 0.112000 rms 0.405 0.081% neg 0 invalid 762 tFOTS 7.8760 tGradient 0.6710 tsec 9.0800 #GCMRL# 570 dt 0.080000 rms 0.405 0.000% neg 0 invalid 762 tFOTS 7.7840 tGradient 0.6730 tsec 9.0050 #GCMRL# 571 dt 0.080000 rms 0.405 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.6750 tsec 1.2260 GCAMregister done in 5.8565 min writing output transformation to transforms/talairach.m3z... GCAMwrite Calls to gcamLogLikelihoodEnergy 4521 tmin = 7.9805 Calls to gcamLabelEnergy 3963 tmin = 0.926083 Calls to gcamJacobianEnergy 4521 tmin = 3.29707 Calls to gcamSmoothnessEnergy 4521 tmin = 5.46367 Calls to gcamLogLikelihoodTerm 573 tmin = 1.39327 Calls to gcamLabelTerm 514 tmin = 5.91602 Calls to gcamJacobianTerm 573 tmin = 2.82298 Calls to gcamSmoothnessTerm 573 tmin = 0.740933 Calls to gcamComputeGradient 573 tmin = 19.8251 Calls to gcamComputeMetricProperties 6293 tmin = 7.4523 mri_ca_register took 0 hours, 58 minutes and 50 seconds. #VMPC# mri_ca_register VmPeak 2535068 FSRUNTIME@ mri_ca_register 0.9807 hours 4 threads @#@FSTIME 2022:01:12:16:59:29 mri_ca_register N 9 e 3530.45 S 3.75 U 9746.82 P 276% M 1347488 F 1 R 919785 W 0 c 696531 w 38793 I 3448 O 63232 L 2.87 3.71 3.00 @#@FSLOADPOST 2022:01:12:17:58:20 mri_ca_register N 9 4.32 3.94 3.77 #-------------------------------------- #@# SubCort Seg Wed Jan 12 17:58:20 CST 2022 mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /usr/local/freesurfer/7.2.0-1/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz sysname Linux hostname faraday machine x86_64 setenv SUBJECTS_DIR /home/valia/mmvt_root/subjects cd /home/valia/mmvt_root/subjects/UMNC03/mri mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /usr/local/freesurfer/7.2.0-1/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 = 4 == reading 1 input volumes reading classifier array from /usr/local/freesurfer/7.2.0-1/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 8.04 Atlas used for the 3D morph was /usr/local/freesurfer/7.2.0-1/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 = 0.09227 (20) Left_Lateral_Ventricle (4): linear fit = 1.08 x + 0.0 (1996 voxels, overlap=0.923) Left_Lateral_Ventricle (4): linear fit = 1.08 x + 0.0 (1996 voxels, peak = 22), gca=21.5 gca peak = 0.20380 (13) mri peak = 0.08908 (21) Right_Lateral_Ventricle (43): linear fit = 1.43 x + 0.0 (1037 voxels, overlap=0.709) Right_Lateral_Ventricle (43): linear fit = 1.43 x + 0.0 (1037 voxels, peak = 19), gca=18.7 gca peak = 0.26283 (96) mri peak = 0.09174 (93) Right_Pallidum (52): linear fit = 0.95 x + 0.0 (764 voxels, overlap=1.010) Right_Pallidum (52): linear fit = 0.95 x + 0.0 (764 voxels, peak = 92), gca=91.7 gca peak = 0.15814 (97) mri peak = 0.07741 (92) Left_Pallidum (13): linear fit = 0.98 x + 0.0 (865 voxels, overlap=0.859) Left_Pallidum (13): linear fit = 0.98 x + 0.0 (865 voxels, peak = 95), gca=94.6 gca peak = 0.27624 (56) mri peak = 0.07689 (67) Right_Hippocampus (53): linear fit = 1.21 x + 0.0 (1251 voxels, overlap=0.489) Right_Hippocampus (53): linear fit = 1.21 x + 0.0 (1251 voxels, peak = 67), gca=67.5 gca peak = 0.28723 (59) mri peak = 0.08233 (69) Left_Hippocampus (17): linear fit = 1.14 x + 0.0 (870 voxels, overlap=0.774) Left_Hippocampus (17): linear fit = 1.14 x + 0.0 (870 voxels, peak = 68), gca=67.6 gca peak = 0.07623 (103) mri peak = 0.11906 (105) Right_Cerebral_White_Matter (41): linear fit = 1.01 x + 0.0 (48622 voxels, overlap=0.665) Right_Cerebral_White_Matter (41): linear fit = 1.01 x + 0.0 (48622 voxels, peak = 105), gca=104.5 gca peak = 0.07837 (105) mri peak = 0.11668 (107) Left_Cerebral_White_Matter (2): linear fit = 1.02 x + 0.0 (44713 voxels, overlap=0.572) Left_Cerebral_White_Matter (2): linear fit = 1.02 x + 0.0 (44713 voxels, peak = 108), gca=107.6 gca peak = 0.10165 (58) mri peak = 0.05344 (63) Left_Cerebral_Cortex (3): linear fit = 1.07 x + 0.0 (38679 voxels, overlap=0.803) Left_Cerebral_Cortex (3): linear fit = 1.07 x + 0.0 (38679 voxels, peak = 62), gca=61.8 gca peak = 0.11113 (58) mri peak = 0.04961 (63) Right_Cerebral_Cortex (42): linear fit = 1.04 x + 0.0 (38588 voxels, overlap=0.870) Right_Cerebral_Cortex (42): linear fit = 1.04 x + 0.0 (38588 voxels, peak = 61), gca=60.6 gca peak = 0.27796 (67) mri peak = 0.10279 (71) Right_Caudate (50): linear fit = 1.07 x + 0.0 (1001 voxels, overlap=0.730) Right_Caudate (50): linear fit = 1.07 x + 0.0 (1001 voxels, peak = 71), gca=71.4 gca peak = 0.14473 (69) mri peak = 0.09414 (72) Left_Caudate (11): linear fit = 1.00 x + 0.0 (1126 voxels, overlap=0.999) Left_Caudate (11): linear fit = 1.00 x + 0.0 (1126 voxels, peak = 69), gca=69.0 gca peak = 0.14301 (56) mri peak = 0.06234 (65) Left_Cerebellum_Cortex (8): linear fit = 1.15 x + 0.0 (34211 voxels, overlap=0.505) Left_Cerebellum_Cortex (8): linear fit = 1.15 x + 0.0 (34211 voxels, peak = 65), gca=64.7 gca peak = 0.14610 (55) mri peak = 0.06523 (66) Right_Cerebellum_Cortex (47): linear fit = 1.15 x + 0.0 (35440 voxels, overlap=0.358) Right_Cerebellum_Cortex (47): linear fit = 1.15 x + 0.0 (35440 voxels, peak = 64), gca=63.5 gca peak = 0.16309 (85) mri peak = 0.10757 (87) Left_Cerebellum_White_Matter (7): linear fit = 1.03 x + 0.0 (7193 voxels, overlap=0.915) Left_Cerebellum_White_Matter (7): linear fit = 1.03 x + 0.0 (7193 voxels, peak = 88), gca=88.0 gca peak = 0.15172 (84) mri peak = 0.09973 (85) Right_Cerebellum_White_Matter (46): linear fit = 1.02 x + 0.0 (7583 voxels, overlap=0.876) Right_Cerebellum_White_Matter (46): linear fit = 1.02 x + 0.0 (7583 voxels, peak = 86), gca=86.1 gca peak = 0.30461 (58) mri peak = 0.08443 (68) Left_Amygdala (18): linear fit = 1.16 x + 0.0 (522 voxels, overlap=0.214) Left_Amygdala (18): linear fit = 1.16 x + 0.0 (522 voxels, peak = 68), gca=67.6 gca peak = 0.32293 (57) mri peak = 0.08255 (67) Right_Amygdala (54): linear fit = 1.18 x + 0.0 (577 voxels, overlap=0.066) Right_Amygdala (54): linear fit = 1.18 x + 0.0 (577 voxels, peak = 68), gca=67.5 gca peak = 0.11083 (90) mri peak = 0.07178 (90) Left_Thalamus (10): linear fit = 1.01 x + 0.0 (4401 voxels, overlap=0.875) Left_Thalamus (10): linear fit = 1.01 x + 0.0 (4401 voxels, peak = 91), gca=91.3 gca peak = 0.11393 (83) mri peak = 0.07331 (89) Right_Thalamus (49): linear fit = 1.07 x + 0.0 (5031 voxels, overlap=0.779) Right_Thalamus (49): linear fit = 1.07 x + 0.0 (5031 voxels, peak = 88), gca=88.4 gca peak = 0.08575 (81) mri peak = 0.06737 (81) Left_Putamen (12): linear fit = 1.04 x + 0.0 (2219 voxels, overlap=0.820) Left_Putamen (12): linear fit = 1.04 x + 0.0 (2219 voxels, peak = 85), gca=84.6 gca peak = 0.08618 (78) mri peak = 0.08402 (84) Right_Putamen (51): linear fit = 1.04 x + 0.0 (2544 voxels, overlap=0.821) Right_Putamen (51): linear fit = 1.04 x + 0.0 (2544 voxels, peak = 82), gca=81.5 gca peak = 0.08005 (78) mri peak = 0.10162 (81) Brain_Stem (16): linear fit = 1.05 x + 0.0 (14850 voxels, overlap=0.491) Brain_Stem (16): linear fit = 1.05 x + 0.0 (14850 voxels, peak = 82), gca=82.3 gca peak = 0.12854 (88) mri peak = 0.08203 (96) Right_VentralDC (60): linear fit = 1.07 x + 0.0 (1714 voxels, overlap=0.782) Right_VentralDC (60): linear fit = 1.07 x + 0.0 (1714 voxels, peak = 94), gca=93.7 gca peak = 0.15703 (87) mri peak = 0.06871 (92) Left_VentralDC (28): linear fit = 1.07 x + 0.0 (1607 voxels, overlap=0.895) Left_VentralDC (28): linear fit = 1.07 x + 0.0 (1607 voxels, peak = 93), gca=92.7 gca peak = 0.17522 (25) mri peak = 0.17860 (28) gca peak = 0.17113 (14) mri peak = 0.13750 (20) Fourth_Ventricle (15): linear fit = 1.33 x + 0.0 (158 voxels, overlap=0.451) Fourth_Ventricle (15): linear fit = 1.33 x + 0.0 (158 voxels, peak = 19), gca=18.6 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.16627 (28) gca peak Third_Ventricle = 0.17522 (25) 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 estimating mean gm scale to be 1.13 x + 0.0 estimating mean wm scale to be 1.02 x + 0.0 estimating mean csf scale to be 1.28 x + 0.0 saving intensity scales to aseg.auto_noCCseg.label_intensities.txt renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.16800 (20) mri peak = 0.09227 (20) Left_Lateral_Ventricle (4): linear fit = 1.02 x + 0.0 (1996 voxels, overlap=0.914) Left_Lateral_Ventricle (4): linear fit = 1.02 x + 0.0 (1996 voxels, peak = 20), gca=20.5 gca peak = 0.15451 (18) mri peak = 0.08908 (21) Right_Lateral_Ventricle (43): linear fit = 1.09 x + 0.0 (1037 voxels, overlap=0.669) Right_Lateral_Ventricle (43): linear fit = 1.09 x + 0.0 (1037 voxels, peak = 20), gca=19.5 gca peak = 0.23398 (90) mri peak = 0.09174 (93) Right_Pallidum (52): linear fit = 1.01 x + 0.0 (764 voxels, overlap=1.008) Right_Pallidum (52): linear fit = 1.01 x + 0.0 (764 voxels, peak = 91), gca=91.3 gca peak = 0.18152 (94) mri peak = 0.07741 (92) Left_Pallidum (13): linear fit = 1.00 x + 0.0 (865 voxels, overlap=0.995) Left_Pallidum (13): linear fit = 1.00 x + 0.0 (865 voxels, peak = 94), gca=93.5 gca peak = 0.27486 (67) mri peak = 0.07689 (67) Right_Hippocampus (53): linear fit = 0.99 x + 0.0 (1251 voxels, overlap=0.982) Right_Hippocampus (53): linear fit = 0.99 x + 0.0 (1251 voxels, peak = 66), gca=66.0 gca peak = 0.25372 (65) mri peak = 0.08233 (69) Left_Hippocampus (17): linear fit = 0.99 x + 0.0 (870 voxels, overlap=1.009) Left_Hippocampus (17): linear fit = 0.99 x + 0.0 (870 voxels, peak = 64), gca=64.0 gca peak = 0.07891 (105) mri peak = 0.11906 (105) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (48622 voxels, overlap=0.722) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (48622 voxels, peak = 105), gca=105.0 gca peak = 0.07733 (107) mri peak = 0.11668 (107) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (44713 voxels, overlap=0.683) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (44713 voxels, peak = 107), gca=107.0 gca peak = 0.09444 (62) mri peak = 0.05344 (63) Left_Cerebral_Cortex (3): linear fit = 1.02 x + 0.0 (38679 voxels, overlap=0.954) Left_Cerebral_Cortex (3): linear fit = 1.02 x + 0.0 (38679 voxels, peak = 64), gca=63.5 gca peak = 0.10904 (61) mri peak = 0.04961 (63) Right_Cerebral_Cortex (42): linear fit = 1.02 x + 0.0 (38588 voxels, overlap=0.952) Right_Cerebral_Cortex (42): linear fit = 1.02 x + 0.0 (38588 voxels, peak = 63), gca=62.5 gca peak = 0.22831 (72) mri peak = 0.10279 (71) Right_Caudate (50): linear fit = 1.00 x + 0.0 (1001 voxels, overlap=1.006) Right_Caudate (50): linear fit = 1.00 x + 0.0 (1001 voxels, peak = 72), gca=72.0 gca peak = 0.14458 (69) mri peak = 0.09414 (72) Left_Caudate (11): linear fit = 1.00 x + 0.0 (1126 voxels, overlap=0.998) Left_Caudate (11): linear fit = 1.00 x + 0.0 (1126 voxels, peak = 69), gca=69.0 gca peak = 0.12025 (64) mri peak = 0.06234 (65) Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (34211 voxels, overlap=0.988) Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (34211 voxels, peak = 64), gca=64.0 gca peak = 0.13424 (64) mri peak = 0.06523 (66) Right_Cerebellum_Cortex (47): linear fit = 1.01 x + 0.0 (35440 voxels, overlap=0.983) Right_Cerebellum_Cortex (47): linear fit = 1.01 x + 0.0 (35440 voxels, peak = 65), gca=65.0 gca peak = 0.16129 (88) mri peak = 0.10757 (87) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (7193 voxels, overlap=0.977) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (7193 voxels, peak = 88), gca=87.6 gca peak = 0.15393 (86) mri peak = 0.09973 (85) Right_Cerebellum_White_Matter (46): linear fit = 1.00 x + 0.0 (7583 voxels, overlap=0.945) Right_Cerebellum_White_Matter (46): linear fit = 1.00 x + 0.0 (7583 voxels, peak = 86), gca=85.6 gca peak = 0.22586 (66) mri peak = 0.08443 (68) Left_Amygdala (18): linear fit = 1.00 x + 0.0 (522 voxels, overlap=1.018) Left_Amygdala (18): linear fit = 1.00 x + 0.0 (522 voxels, peak = 66), gca=66.0 gca peak = 0.28022 (68) mri peak = 0.08255 (67) Right_Amygdala (54): linear fit = 1.00 x + 0.0 (577 voxels, overlap=1.014) Right_Amygdala (54): linear fit = 1.00 x + 0.0 (577 voxels, peak = 68), gca=68.0 gca peak = 0.10711 (90) mri peak = 0.07178 (90) Left_Thalamus (10): linear fit = 1.00 x + 0.0 (4401 voxels, overlap=0.899) Left_Thalamus (10): linear fit = 1.00 x + 0.0 (4401 voxels, peak = 90), gca=89.6 gca peak = 0.10095 (86) mri peak = 0.07331 (89) Right_Thalamus (49): linear fit = 1.00 x + 0.0 (5031 voxels, overlap=0.953) Right_Thalamus (49): linear fit = 1.00 x + 0.0 (5031 voxels, peak = 86), gca=85.6 gca peak = 0.07724 (83) mri peak = 0.06737 (81) Left_Putamen (12): linear fit = 1.01 x + 0.0 (2219 voxels, overlap=0.911) Left_Putamen (12): linear fit = 1.01 x + 0.0 (2219 voxels, peak = 84), gca=84.2 gca peak = 0.11252 (79) mri peak = 0.08402 (84) Right_Putamen (51): linear fit = 1.00 x + 0.0 (2544 voxels, overlap=0.944) Right_Putamen (51): linear fit = 1.00 x + 0.0 (2544 voxels, peak = 79), gca=79.0 gca peak = 0.08351 (83) mri peak = 0.10162 (81) Brain_Stem (16): linear fit = 1.00 x + 0.0 (14850 voxels, overlap=0.723) Brain_Stem (16): linear fit = 1.00 x + 0.0 (14850 voxels, peak = 83), gca=82.6 gca peak = 0.12327 (95) mri peak = 0.08203 (96) Right_VentralDC (60): linear fit = 1.00 x + 0.0 (1714 voxels, overlap=0.860) Right_VentralDC (60): linear fit = 1.00 x + 0.0 (1714 voxels, peak = 95), gca=95.5 gca peak = 0.14695 (91) mri peak = 0.06871 (92) Left_VentralDC (28): linear fit = 0.98 x + 0.0 (1607 voxels, overlap=0.967) Left_VentralDC (28): linear fit = 0.98 x + 0.0 (1607 voxels, peak = 89), gca=88.7 gca peak = 0.15700 (34) mri peak = 0.17860 (28) gca peak = 0.16433 (21) mri peak = 0.13750 (20) Fourth_Ventricle (15): linear fit = 1.08 x + 0.0 (158 voxels, overlap=0.762) Fourth_Ventricle (15): linear fit = 1.08 x + 0.0 (158 voxels, peak = 23), gca=22.6 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.17659 (32) gca peak Third_Ventricle = 0.15700 (34) gca peak CSF = 0.17998 (46) gca peak Left_Accumbens_area = 0.67128 (62) gca peak Left_undetermined = 1.00000 (28) gca peak Left_vessel = 0.89837 (53) gca peak Left_choroid_plexus = 0.10623 (35) gca peak Right_Inf_Lat_Vent = 0.26261 (28) gca peak Right_Accumbens_area = 0.31505 (69) gca peak Right_vessel = 0.77268 (52) gca peak Right_choroid_plexus = 0.14030 (38) gca peak Fifth_Ventricle = 0.77755 (41) gca peak WM_hypointensities = 0.10450 (78) gca peak non_WM_hypointensities = 0.08220 (42) gca peak Optic_Chiasm = 0.51661 (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 1.00 x + 0.0 estimating mean csf scale to be 1.06 x + 0.0 saving intensity scales to aseg.auto_noCCseg.label_intensities.txt saving sequentially combined intensity scales to aseg.auto_noCCseg.label_intensities.txt 91255 voxels changed in iteration 0 of unlikely voxel relabeling 222 voxels changed in iteration 1 of unlikely voxel relabeling 2 voxels changed in iteration 2 of unlikely voxel relabeling 0 voxels changed in iteration 3 of unlikely voxel relabeling 53395 gm and wm labels changed (%24 to gray, %76 to white out of all changed labels) 559 hippocampal voxels changed. 0 amygdala voxels changed. Reclassifying using Gibbs Priors pass 1: 85591 changed. image ll: -2.162, PF=0.500 pass 2: 23908 changed. image ll: -2.161, PF=0.500 pass 3: 7085 changed. pass 4: 2413 changed. 57206 voxels changed in iteration 0 of unlikely voxel relabeling 511 voxels changed in iteration 1 of unlikely voxel relabeling 20 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 7185 voxels changed in iteration 0 of unlikely voxel relabeling 109 voxels changed in iteration 1 of unlikely voxel relabeling 0 voxels changed in iteration 2 of unlikely voxel relabeling 7502 voxels changed in iteration 0 of unlikely voxel relabeling 97 voxels changed in iteration 1 of unlikely voxel relabeling 4 voxels changed in iteration 2 of unlikely voxel relabeling 0 voxels changed in iteration 3 of unlikely voxel relabeling 5929 voxels changed in iteration 0 of unlikely voxel relabeling 21 voxels changed in iteration 1 of unlikely voxel relabeling 0 voxels changed in iteration 2 of unlikely voxel relabeling !!!!!!!!! ventricle segment 2 with volume 9149 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 1 with volume 214 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 4 with volume 6355 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 7 with volume 122 above threshold 100 - not erasing !!!!!!!!!! writing labeled volume to aseg.auto_noCCseg.mgz mri_ca_label utimesec 2084.516087 mri_ca_label stimesec 2.440085 mri_ca_label ru_maxrss 2074384 mri_ca_label ru_ixrss 0 mri_ca_label ru_idrss 0 mri_ca_label ru_isrss 0 mri_ca_label ru_minflt 2061928 mri_ca_label ru_majflt 2 mri_ca_label ru_nswap 0 mri_ca_label ru_inblock 4112 mri_ca_label ru_oublock 776 mri_ca_label ru_msgsnd 0 mri_ca_label ru_msgrcv 0 mri_ca_label ru_nsignals 0 mri_ca_label ru_nvcsw 242 mri_ca_label ru_nivcsw 32469 auto-labeling took 33 minutes and 58 seconds. @#@FSTIME 2022:01:12:17:58:20 mri_ca_label N 10 e 2037.76 S 2.52 U 2084.51 P 102% M 2074384 F 2 R 2061930 W 0 c 32470 w 243 I 4112 O 776 L 4.32 3.94 3.77 @#@FSLOADPOST 2022:01:12:18:32:17 mri_ca_label N 10 1.09 1.05 1.32 #-------------------------------------- #@# CC Seg Wed Jan 12 18:32:17 CST 2022 mri_cc -aseg aseg.auto_noCCseg.mgz -o aseg.auto.mgz -lta /home/valia/mmvt_root/subjects/UMNC03/mri/transforms/cc_up.lta UMNC03 will read input aseg from aseg.auto_noCCseg.mgz writing aseg with cc labels to aseg.auto.mgz will write lta as /home/valia/mmvt_root/subjects/UMNC03/mri/transforms/cc_up.lta reading aseg from /home/valia/mmvt_root/subjects/UMNC03/mri/aseg.auto_noCCseg.mgz reading norm from /home/valia/mmvt_root/subjects/UMNC03/mri/norm.mgz MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab 52446 voxels in left wm, 87853 in right wm, xrange [122, 135] searching rotation angles z=[-6 8], y=[-10 4] searching scale 1 Z rot -5.8 searching scale 1 Z rot -5.5 searching scale 1 Z rot -5.3 searching scale 1 Z rot -5.0 searching scale 1 Z rot -4.8 searching scale 1 Z rot -4.5 searching scale 1 Z rot -4.3 searching scale 1 Z rot -4.0 searching scale 1 Z rot -3.8 searching scale 1 Z rot -3.5 searching scale 1 Z rot -3.3 searching scale 1 Z rot -3.0 searching scale 1 Z rot -2.8 searching scale 1 Z rot -2.5 searching scale 1 Z rot -2.3 searching scale 1 Z rot -2.0 searching scale 1 Z rot -1.8 searching scale 1 Z rot -1.5 searching scale 1 Z rot -1.3 searching scale 1 Z rot -1.0 searching scale 1 Z rot -0.8 searching scale 1 Z rot -0.5 searching scale 1 Z rot -0.3 searching scale 1 Z rot -0.0 searching scale 1 Z rot 0.2 searching scale 1 Z rot 0.5 searching scale 1 Z rot 0.7 searching scale 1 Z rot 1.0 searching scale 1 Z rot 1.2 searching scale 1 Z rot 1.5 searching scale 1 Z rot 1.7 searching scale 1 Z rot 2.0 searching scale 1 Z rot 2.2 searching scale 1 Z rot 2.5 searching scale 1 Z rot 2.7 searching scale 1 Z rot 3.0 searching scale 1 Z rot 3.2 searching scale 1 Z rot 3.5 searching scale 1 Z rot 3.7 searching scale 1 Z rot 4.0 searching scale 1 Z rot 4.2 searching scale 1 Z rot 4.5 searching scale 1 Z rot 4.7 searching scale 1 Z rot 5.0 searching scale 1 Z rot 5.2 searching scale 1 Z rot 5.5 searching scale 1 Z rot 5.7 searching scale 1 Z rot 6.0 searching scale 1 Z rot 6.2 searching scale 1 Z rot 6.5 searching scale 1 Z rot 6.7 searching scale 1 Z rot 7.0 searching scale 1 Z rot 7.2 searching scale 1 Z rot 7.5 searching scale 1 Z rot 7.7 searching scale 1 Z rot 8.0 global minimum found at slice 129.6, rotations (-3.08, 1.24) final transformation (x=129.6, yr=-3.084, zr=1.237): 0.99832 -0.02159 -0.05379 7.71788; 0.02156 0.99977 -0.00116 17.37770; 0.05380 0.00000 0.99855 -3.78893; 0.00000 0.00000 0.00000 1.00000; updating x range to be [126, 131] in xformed coordinates best xformed slice 127 min_x_fornix = 136 min_x_fornix = 143 WARNING: min_x_fornix not set min_x_fornix = 146 min_x_fornix = 142 cc center is found at 127 108 125 eigenvectors: 0.00093 -0.01767 0.99984; 0.20592 -0.97841 -0.01748; 0.97857 0.20590 0.00273; MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab writing aseg with callosum to /home/valia/mmvt_root/subjects/UMNC03/mri/aseg.auto.mgz... corpus callosum segmentation took 0.7 minutes #VMPC# mri_cc VmPeak 688788 mri_cc done @#@FSTIME 2022:01:12:18:32:18 mri_cc N 7 e 42.06 S 0.20 U 42.93 P 102% M 343676 F 2 R 162731 W 0 c 1191 w 25 I 4424 O 760 L 1.09 1.05 1.32 @#@FSLOADPOST 2022:01:12:18:33:00 mri_cc N 7 1.05 1.05 1.30 #-------------------------------------- #@# Merge ASeg Wed Jan 12 18:33:00 CST 2022 cp aseg.auto.mgz aseg.presurf.mgz #-------------------------------------------- #@# Intensity Normalization2 Wed Jan 12 18:33:00 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/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 1217 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 109 gm peak at 67 (67), valley at 37 (37) csf peak at 34, setting threshold to 56 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 66 (66), valley at 37 (37) csf peak at 33, setting threshold to 55 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Done iterating --------------------------------- writing output to brain.mgz 3D bias adjustment took 1 minutes and 51 seconds. @#@FSTIME 2022:01:12:18:33:00 mri_normalize N 9 e 111.99 S 0.87 U 126.68 P 113% M 1205856 F 2 R 554663 W 0 c 3707 w 32 I 4936 O 3248 L 1.05 1.05 1.30 @#@FSLOADPOST 2022:01:12:18:34:52 mri_normalize N 9 1.20 1.11 1.29 #-------------------------------------------- #@# Mask BFS Wed Jan 12 18:34:52 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/mri mri_mask -T 5 brain.mgz brainmask.mgz brain.finalsurfs.mgz threshold mask volume at 5 DoAbs = 0 Found 1937281 voxels in mask (pct= 11.55) Writing masked volume to brain.finalsurfs.mgz...done. @#@FSTIME 2022:01:12:18:34:52 mri_mask N 5 e 0.89 S 0.01 U 1.73 P 195% M 73916 F 2 R 2926 W 0 c 105 w 10 I 4440 O 3208 L 1.20 1.11 1.29 @#@FSLOADPOST 2022:01:12:18:34:53 mri_mask N 5 1.43 1.16 1.31 #-------------------------------------------- #@# WM Segmentation Wed Jan 12 18:34:53 CST 2022 AntsDenoiseImageFs -i brain.mgz -o antsdn.brain.mgz @#@FSTIME 2022:01:12:18:34:53 AntsDenoiseImageFs N 4 e 51.89 S 0.07 U 52.13 P 100% M 350752 F 2 R 51218 W 0 c 947 w 8 I 6544 O 3256 L 1.43 1.16 1.31 @#@FSLOADPOST 2022:01:12:18:35:44 AntsDenoiseImageFs N 4 1.18 1.13 1.29 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): 102.8 +- 5.4 [79.0 --> 125.0] CCS GM (72.0) : 71.1 +- 10.2 [30.0 --> 95.0] white_mean 102.828 white_sigma 5.42425 gray_mean 71.0937 gray_sigma 10.1858 setting bottom of white matter range wm_low to 81.3 setting top of gray matter range gray_hi to 91.5 wm_low 81.2795 wm_hi 125 gray_low 30 gray_hi 91.4652 Redoing initial intensity segmentation... Recomputing local statistics to label ambiguous voxels... wm_low 81.2795 wm_hi 125 gray_low 30 gray_hi 91.4652 using local geometry to label remaining ambiguous voxels... polvwsize = 5, polvlen = 3, gray_hi = 91.4652, wm_low = 81.2795 MRIcpolvMedianCurveSegment(): wsize=5, len=3, gmhi=91.4652, wmlow=81.2795 165915 voxels processed (0.99%) 74829 voxels white (0.45%) 91086 voxels non-white (0.54%) Reclassifying voxels using Gaussian border classifier niter=1 MRIreclassify(): wm_low=76.2795, gray_hi=91.4652, wsize=13 298977 voxels tested (1.78%) 62847 voxels changed (0.37%) 69105 multi-scale searches (0.41%) Recovering bright white MRIrecoverBrightWhite() wm_low 81.2795 wm_hi 125 slack 5.42425 pct_thresh 0.33 intensity_thresh 130.424 nvox_thresh 8.58 369 voxels tested (0.00%) 203 voxels changed (0.00%) removing voxels with positive offset direction... MRIremoveWrongDirection() wsize=3, lowthr=76.2795, hithr=91.4652 smoothing input volume with sigma = 0.250 102433 voxels tested (0.61%) 19104 voxels changed (0.11%) thicken = 1 removing 1-dimensional structures... MRIremove1dStructures(): max_iter=10000, thresh=2, WM_MIN_VAL=5 5964 sparsely connected voxels removed in 1 iterations thickening thin strands.... thickness 4 nsegments 20 wm_hi 125 2846 diagonally connected voxels added... MRIthickenThinWMStrands(): thickness=4, nsegments=20 20 segments, 5993 filled MRIfindBrightNonWM(): 2563 bright non-wm voxels segmented. MRIfilterMorphology() WM_MIN_VAL=5, DIAGONAL_FILL=230 white matter segmentation took 1.1 minutes writing output to wm.seg.mgz... @#@FSTIME 2022:01:12:18:35:44 mri_segment N 5 e 65.77 S 0.15 U 66.43 P 101% M 159604 F 2 R 90247 W 0 c 1157 w 14 I 4368 O 1040 L 1.18 1.13 1.29 @#@FSLOADPOST 2022:01:12:18:36:50 mri_segment N 5 1.10 1.12 1.27 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 0.42 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 3986 additional wm voxels added 0 additional wm voxels added SEG EDIT: 53364 voxels turned on, 54508 voxels turned off. propagating editing to output volume from wm.seg.mgz writing edited volume to wm.asegedit.mgz.... @#@FSTIME 2022:01:12:18:36:50 mri_edit_wm_with_aseg N 5 e 25.22 S 0.14 U 27.44 P 109% M 461896 F 2 R 116952 W 0 c 694 w 47 I 4192 O 944 L 1.10 1.12 1.27 @#@FSLOADPOST 2022:01:12:18:37:16 mri_edit_wm_with_aseg N 5 1.20 1.14 1.27 mri_pretess wm.asegedit.mgz wm norm.mgz wm.mgz Iteration Number : 1 pass 1 (xy+): 8 found - 8 modified | TOTAL: 8 pass 2 (xy+): 0 found - 8 modified | TOTAL: 8 pass 1 (xy-): 26 found - 26 modified | TOTAL: 34 pass 2 (xy-): 0 found - 26 modified | TOTAL: 34 pass 1 (yz+): 21 found - 21 modified | TOTAL: 55 pass 2 (yz+): 0 found - 21 modified | TOTAL: 55 pass 1 (yz-): 32 found - 32 modified | TOTAL: 87 pass 2 (yz-): 0 found - 32 modified | TOTAL: 87 pass 1 (xz+): 14 found - 14 modified | TOTAL: 101 pass 2 (xz+): 0 found - 14 modified | TOTAL: 101 pass 1 (xz-): 27 found - 27 modified | TOTAL: 128 pass 2 (xz-): 0 found - 27 modified | TOTAL: 128 Iteration Number : 1 pass 1 (+++): 11 found - 11 modified | TOTAL: 11 pass 2 (+++): 0 found - 11 modified | TOTAL: 11 pass 1 (+++): 22 found - 22 modified | TOTAL: 33 pass 2 (+++): 0 found - 22 modified | TOTAL: 33 pass 1 (+++): 22 found - 22 modified | TOTAL: 55 pass 2 (+++): 0 found - 22 modified | TOTAL: 55 pass 1 (+++): 49 found - 49 modified | TOTAL: 104 pass 2 (+++): 0 found - 49 modified | TOTAL: 104 Iteration Number : 1 pass 1 (++): 204 found - 204 modified | TOTAL: 204 pass 2 (++): 0 found - 204 modified | TOTAL: 204 pass 1 (+-): 116 found - 116 modified | TOTAL: 320 pass 2 (+-): 0 found - 116 modified | TOTAL: 320 pass 1 (--): 134 found - 134 modified | TOTAL: 454 pass 2 (--): 0 found - 134 modified | TOTAL: 454 pass 1 (-+): 198 found - 198 modified | TOTAL: 652 pass 2 (-+): 0 found - 198 modified | TOTAL: 652 Iteration Number : 2 pass 1 (xy+): 9 found - 9 modified | TOTAL: 9 pass 2 (xy+): 0 found - 9 modified | TOTAL: 9 pass 1 (xy-): 7 found - 7 modified | TOTAL: 16 pass 2 (xy-): 0 found - 7 modified | TOTAL: 16 pass 1 (yz+): 9 found - 9 modified | TOTAL: 25 pass 2 (yz+): 0 found - 9 modified | TOTAL: 25 pass 1 (yz-): 11 found - 11 modified | TOTAL: 36 pass 2 (yz-): 0 found - 11 modified | TOTAL: 36 pass 1 (xz+): 6 found - 6 modified | TOTAL: 42 pass 2 (xz+): 0 found - 6 modified | TOTAL: 42 pass 1 (xz-): 6 found - 6 modified | TOTAL: 48 pass 2 (xz-): 0 found - 6 modified | TOTAL: 48 Iteration Number : 2 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 (+++): 4 found - 4 modified | TOTAL: 6 pass 2 (+++): 0 found - 4 modified | TOTAL: 6 Iteration Number : 2 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 (--): 2 found - 2 modified | TOTAL: 7 pass 2 (--): 0 found - 2 modified | TOTAL: 7 pass 1 (-+): 4 found - 4 modified | TOTAL: 11 pass 2 (-+): 0 found - 4 modified | TOTAL: 11 Iteration Number : 3 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 1 found - 1 modified | TOTAL: 1 pass 2 (xy-): 0 found - 1 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 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 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 = 951 (out of 603407: 0.157605) binarizing input wm segmentation... Ambiguous edge configurations... mri_pretess done @#@FSTIME 2022:01:12:18:37:16 mri_pretess N 4 e 4.25 S 0.00 U 5.06 P 119% M 56796 F 2 R 2204 W 0 c 298 w 11 I 5688 O 944 L 1.20 1.14 1.27 @#@FSLOADPOST 2022:01:12:18:37:20 mri_pretess N 4 1.18 1.14 1.27 #-------------------------------------------- #@# Fill Wed Jan 12 18:37:20 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/mri mri_fill -a ../scripts/ponscc.cut.log -xform transforms/talairach.lta -segmentation aseg.presurf.mgz -ctab /usr/local/freesurfer/7.2.0-1/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 0.98073 -0.06179 -0.05011 12.50586; 0.05010 1.06463 -0.07866 -7.87769; 0.04039 0.08444 0.94149 -22.93445; 0.00000 0.00000 0.00000 1.00000; voxel to talairach voxel transform 0.98073 -0.06179 -0.05011 12.50586; 0.05010 1.06463 -0.07866 -7.87769; 0.04039 0.08444 0.94149 -22.93445; 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] = 1063 (min = 350, max = 1400), aspect = 0.42 (min = 0.10, max = 0.75) no need to search using seed (125, 122, 148), TAL = (3.0, 20.0, 6.0) talairach voxel to voxel transform 1.01447 0.05424 0.05852 -10.91739; -0.05062 0.93040 0.07504 9.68336; -0.03899 -0.08577 1.05291 23.95976; 0.00000 0.00000 0.00000 1.00000; segmentation indicates cc at (125, 122, 148) --> (3.0, 20.0, 6.0) done. filling took 1.0 minutes talairach cc position changed to (3.00, 20.00, 6.00) Erasing brainstem...done. seed_search_size = 9, min_neighbors = 5 search rh wm seed point around talairach space:(21.00, 20.00, 6.00) SRC: (112.91, 128.88, 165.15) search lh wm seed point around talairach space (-15.00, 20.00, 6.00), SRC: (149.43, 127.06, 163.75) 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 2022:01:12:18:37:20 mri_fill N 10 e 63.43 S 0.87 U 63.32 P 101% M 1012132 F 2 R 434756 W 0 c 1406 w 13 I 4632 O 312 L 1.18 1.14 1.27 @#@FSLOADPOST 2022:01:12:18:38:23 mri_fill N 10 1.19 1.14 1.26 cp filled.mgz filled.auto.mgz #-------------------------------------------- #@# Tessellate lh Wed Jan 12 18:38:23 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mri_pretess ../mri/filled.mgz 255 ../mri/norm.mgz ../mri/filled-pretess255.mgz Iteration Number : 1 pass 1 (xy+): 1 found - 1 modified | TOTAL: 1 pass 2 (xy+): 0 found - 1 modified | TOTAL: 1 pass 1 (xy-): 2 found - 2 modified | TOTAL: 3 pass 2 (xy-): 0 found - 2 modified | TOTAL: 3 pass 1 (yz+): 11 found - 11 modified | TOTAL: 14 pass 2 (yz+): 0 found - 11 modified | TOTAL: 14 pass 1 (yz-): 10 found - 10 modified | TOTAL: 24 pass 2 (yz-): 0 found - 10 modified | TOTAL: 24 pass 1 (xz+): 5 found - 5 modified | TOTAL: 29 pass 2 (xz+): 0 found - 5 modified | TOTAL: 29 pass 1 (xz-): 0 found - 0 modified | TOTAL: 29 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 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 2 found - 2 modified | TOTAL: 2 pass 2 (+-): 0 found - 2 modified | TOTAL: 2 pass 1 (--): 1 found - 1 modified | TOTAL: 3 pass 2 (--): 0 found - 1 modified | TOTAL: 3 pass 1 (-+): 0 found - 0 modified | TOTAL: 3 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 = 33 (out of 286390: 0.011523) Ambiguous edge configurations... mri_pretess done @#@FSTIME 2022:01:12:18:38:23 mri_pretess N 4 e 2.40 S 0.00 U 3.30 P 137% M 42116 F 0 R 1113 W 0 c 140 w 7 I 0 O 304 L 1.19 1.14 1.26 @#@FSLOADPOST 2022:01:12:18:38:26 mri_pretess N 4 1.19 1.14 1.26 mri_tessellate ../mri/filled-pretess255.mgz 255 ../surf/lh.orig.nofix 7.2.0 7.2.0 slice 40: 2477 vertices, 2617 faces slice 50: 7841 vertices, 8062 faces slice 60: 15708 vertices, 15987 faces slice 70: 24572 vertices, 24907 faces slice 80: 34856 vertices, 35234 faces slice 90: 45927 vertices, 46287 faces slice 100: 56871 vertices, 57229 faces slice 110: 69534 vertices, 69971 faces slice 120: 83174 vertices, 83658 faces slice 130: 96695 vertices, 97182 faces slice 140: 109131 vertices, 109560 faces slice 150: 120161 vertices, 120541 faces slice 160: 128889 vertices, 129280 faces slice 170: 137882 vertices, 138274 faces slice 180: 146121 vertices, 146477 faces slice 190: 153531 vertices, 153891 faces slice 200: 159055 vertices, 159306 faces slice 210: 162556 vertices, 162694 faces slice 220: 162838 vertices, 162920 faces slice 230: 162838 vertices, 162920 faces slice 240: 162838 vertices, 162920 faces slice 250: 162838 vertices, 162920 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 2022:01:12:18:38:26 mri_tessellate N 3 e 1.31 S 0.00 U 1.72 P 131% M 42288 F 2 R 1683 W 0 c 61 w 7 I 4456 O 7640 L 1.19 1.14 1.26 @#@FSLOADPOST 2022:01:12:18:38:27 mri_tessellate N 3 1.18 1.14 1.26 rm -f ../mri/filled-pretess255.mgz mris_extract_main_component ../surf/lh.orig.nofix ../surf/lh.orig.nofix counting number of connected components... 162838 voxel in cpt #1: X=-82 [v=162838,e=488760,f=325840] located at (-29.748934, -9.453009, 12.007572) For the whole surface: X=-82 [v=162838,e=488760,f=325840] One single component has been found nothing to do done @#@FSTIME 2022:01:12:18:38:27 mris_extract_main_component N 2 e 0.82 S 0.09 U 1.06 P 139% M 322292 F 2 R 48905 W 0 c 62 w 62 I 5992 O 11456 L 1.18 1.14 1.26 @#@FSLOADPOST 2022:01:12:18:38:28 mris_extract_main_component N 2 1.18 1.14 1.26 #-------------------------------------------- #@# Tessellate rh Wed Jan 12 18:38:28 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mri_pretess ../mri/filled.mgz 127 ../mri/norm.mgz ../mri/filled-pretess127.mgz Iteration Number : 1 pass 1 (xy+): 2 found - 2 modified | TOTAL: 2 pass 2 (xy+): 0 found - 2 modified | TOTAL: 2 pass 1 (xy-): 4 found - 4 modified | TOTAL: 6 pass 2 (xy-): 0 found - 4 modified | TOTAL: 6 pass 1 (yz+): 16 found - 16 modified | TOTAL: 22 pass 2 (yz+): 0 found - 16 modified | TOTAL: 22 pass 1 (yz-): 12 found - 12 modified | TOTAL: 34 pass 2 (yz-): 0 found - 12 modified | TOTAL: 34 pass 1 (xz+): 2 found - 2 modified | TOTAL: 36 pass 2 (xz+): 0 found - 2 modified | TOTAL: 36 pass 1 (xz-): 1 found - 1 modified | TOTAL: 37 pass 2 (xz-): 0 found - 1 modified | TOTAL: 37 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 (++): 0 found - 0 modified | TOTAL: 0 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 (-+): 1 found - 1 modified | TOTAL: 3 pass 2 (-+): 0 found - 1 modified | TOTAL: 3 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+): 0 found - 0 modified | TOTAL: 0 pass 1 (xz-): 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 : 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 Total Number of Modified Voxels = 40 (out of 293197: 0.013643) Ambiguous edge configurations... mri_pretess done @#@FSTIME 2022:01:12:18:38:28 mri_pretess N 4 e 1.84 S 0.00 U 2.67 P 145% M 40056 F 0 R 1624 W 0 c 161 w 7 I 0 O 304 L 1.18 1.14 1.26 @#@FSLOADPOST 2022:01:12:18:38:30 mri_pretess N 4 1.18 1.14 1.26 mri_tessellate ../mri/filled-pretess127.mgz 127 ../surf/rh.orig.nofix 7.2.0 7.2.0 slice 40: 1428 vertices, 1550 faces slice 50: 6339 vertices, 6538 faces slice 60: 13766 vertices, 14033 faces slice 70: 22661 vertices, 23000 faces slice 80: 33691 vertices, 34139 faces slice 90: 45233 vertices, 45638 faces slice 100: 56614 vertices, 56973 faces slice 110: 69553 vertices, 69996 faces slice 120: 83111 vertices, 83593 faces slice 130: 96455 vertices, 96989 faces slice 140: 110057 vertices, 110554 faces slice 150: 122409 vertices, 122866 faces slice 160: 132477 vertices, 132930 faces slice 170: 140844 vertices, 141215 faces slice 180: 149875 vertices, 150283 faces slice 190: 157374 vertices, 157722 faces slice 200: 163466 vertices, 163792 faces slice 210: 167937 vertices, 168129 faces slice 220: 168332 vertices, 168450 faces slice 230: 168332 vertices, 168450 faces slice 240: 168332 vertices, 168450 faces slice 250: 168332 vertices, 168450 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 2022:01:12:18:38:30 mri_tessellate N 3 e 1.29 S 0.00 U 1.72 P 132% M 43064 F 0 R 1943 W 0 c 112 w 4 I 0 O 7896 L 1.18 1.14 1.26 @#@FSLOADPOST 2022:01:12:18:38:31 mri_tessellate N 3 1.18 1.14 1.26 rm -f ../mri/filled-pretess127.mgz mris_extract_main_component ../surf/rh.orig.nofix ../surf/rh.orig.nofix counting number of connected components... 168332 voxel in cpt #1: X=-118 [v=168332,e=505350,f=336900] located at (27.778658, -7.434386, 10.066529) For the whole surface: X=-118 [v=168332,e=505350,f=336900] One single component has been found nothing to do done @#@FSTIME 2022:01:12:18:38:31 mris_extract_main_component N 2 e 0.91 S 0.07 U 1.18 P 138% M 333276 F 0 R 51797 W 0 c 69 w 61 I 0 O 11848 L 1.18 1.14 1.26 @#@FSLOADPOST 2022:01:12:18:38:32 mris_extract_main_component N 2 1.16 1.14 1.26 #-------------------------------------------- #@# Smooth1 lh Wed Jan 12 18:38:32 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mris_smooth -nw -seed 1234 ../surf/lh.orig.nofix ../surf/lh.smoothwm.nofix #-------------------------------------------- #@# Smooth1 rh Wed Jan 12 18:38:32 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mris_smooth -nw -seed 1234 ../surf/rh.orig.nofix ../surf/rh.smoothwm.nofix Waiting for PID 3042368 of (3042368 3042371) to complete... Waiting for PID 3042371 of (3042368 3042371) to complete... 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... 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... PIDs (3042368 3042371) completed and logs appended. #-------------------------------------------- #@# Inflation1 lh Wed Jan 12 18:38:36 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mris_inflate -no-save-sulc ../surf/lh.smoothwm.nofix ../surf/lh.inflated.nofix #-------------------------------------------- #@# Inflation1 rh Wed Jan 12 18:38:36 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mris_inflate -no-save-sulc ../surf/rh.smoothwm.nofix ../surf/rh.inflated.nofix Waiting for PID 3042417 of (3042417 3042420) to complete... Waiting for PID 3042420 of (3042417 3042420) to complete... mris_inflate -no-save-sulc ../surf/lh.smoothwm.nofix ../surf/lh.inflated.nofix Not saving sulc Reading ../surf/lh.smoothwm.nofix avg radius = 49.8 mm, total surface area = 85573 mm^2 step 000: RMS=0.158 (target=0.015) step 005: RMS=0.121 (target=0.015) step 010: RMS=0.092 (target=0.015) step 015: RMS=0.078 (target=0.015) step 020: RMS=0.068 (target=0.015) step 025: RMS=0.060 (target=0.015) step 030: RMS=0.055 (target=0.015) step 035: RMS=0.051 (target=0.015) step 040: RMS=0.048 (target=0.015) step 045: RMS=0.047 (target=0.015) step 050: RMS=0.045 (target=0.015) step 055: RMS=0.045 (target=0.015) step 060: RMS=0.045 (target=0.015) writing inflated surface to ../surf/lh.inflated.nofix inflation took 0.6 minutes inflation complete. Not saving sulc mris_inflate utimesec 63.326539 mris_inflate stimesec 0.930927 mris_inflate ru_maxrss 259156 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 665115 mris_inflate ru_majflt 2 mris_inflate ru_nswap 0 mris_inflate ru_inblock 6744 mris_inflate ru_oublock 11472 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 1106 mris_inflate ru_nivcsw 5810 mris_inflate -no-save-sulc ../surf/rh.smoothwm.nofix ../surf/rh.inflated.nofix Not saving sulc Reading ../surf/rh.smoothwm.nofix avg radius = 49.8 mm, total surface area = 88607 mm^2 step 000: RMS=0.161 (target=0.015) step 005: RMS=0.123 (target=0.015) step 010: RMS=0.095 (target=0.015) step 015: RMS=0.081 (target=0.015) step 020: RMS=0.072 (target=0.015) step 025: RMS=0.065 (target=0.015) step 030: RMS=0.059 (target=0.015) step 035: RMS=0.054 (target=0.015) step 040: RMS=0.052 (target=0.015) step 045: RMS=0.050 (target=0.015) step 050: RMS=0.048 (target=0.015) step 055: RMS=0.048 (target=0.015) step 060: RMS=0.048 (target=0.015) writing inflated surface to ../surf/rh.inflated.nofix inflation took 0.6 minutes inflation complete. Not saving sulc mris_inflate utimesec 64.372667 mris_inflate stimesec 0.976211 mris_inflate ru_maxrss 267736 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 685385 mris_inflate ru_majflt 2 mris_inflate ru_nswap 0 mris_inflate ru_inblock 8 mris_inflate ru_oublock 11856 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 1365 mris_inflate ru_nivcsw 5835 PIDs (3042417 3042420) completed and logs appended. #-------------------------------------------- #@# QSphere lh Wed Jan 12 18:39:11 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mris_sphere -q -p 6 -a 128 -seed 1234 ../surf/lh.inflated.nofix ../surf/lh.qsphere.nofix #-------------------------------------------- #@# QSphere rh Wed Jan 12 18:39:11 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mris_sphere -q -p 6 -a 128 -seed 1234 ../surf/rh.inflated.nofix ../surf/rh.qsphere.nofix Waiting for PID 3042489 of (3042489 3042492) to complete... Waiting for PID 3042492 of (3042489 3042492) to complete... 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: 4 scaling brain by 0.270... inflating... projecting onto sphere... surface projected - minimizing metric distortion... vertex spacing 0.90 +- 0.57 (0.00-->7.23) (max @ vno 84308 --> 85755) face area 0.02 +- 0.03 (-0.26-->0.64) Entering MRISinflateToSphere() inflating to sphere (rms error < 2.00) 000: dt: 0.0000, rms radial error=177.951, avgs=0 005/300: dt: 0.9000, rms radial error=177.689, avgs=0 010/300: dt: 0.9000, rms radial error=177.127, avgs=0 015/300: dt: 0.9000, rms radial error=176.390, avgs=0 020/300: dt: 0.9000, rms radial error=175.554, avgs=0 025/300: dt: 0.9000, rms radial error=174.661, avgs=0 030/300: dt: 0.9000, rms radial error=173.736, avgs=0 035/300: dt: 0.9000, rms radial error=172.794, avgs=0 040/300: dt: 0.9000, rms radial error=171.844, avgs=0 045/300: dt: 0.9000, rms radial error=170.892, avgs=0 050/300: dt: 0.9000, rms radial error=169.940, avgs=0 055/300: dt: 0.9000, rms radial error=168.991, avgs=0 060/300: dt: 0.9000, rms radial error=168.046, avgs=0 065/300: dt: 0.9000, rms radial error=167.105, avgs=0 070/300: dt: 0.9000, rms radial error=166.168, avgs=0 075/300: dt: 0.9000, rms radial error=165.237, avgs=0 080/300: dt: 0.9000, rms radial error=164.310, avgs=0 085/300: dt: 0.9000, rms radial error=163.388, avgs=0 090/300: dt: 0.9000, rms radial error=162.471, avgs=0 095/300: dt: 0.9000, rms radial error=161.559, avgs=0 100/300: dt: 0.9000, rms radial error=160.652, avgs=0 105/300: dt: 0.9000, rms radial error=159.750, avgs=0 110/300: dt: 0.9000, rms radial error=158.853, avgs=0 115/300: dt: 0.9000, rms radial error=157.961, avgs=0 120/300: dt: 0.9000, rms radial error=157.073, avgs=0 125/300: dt: 0.9000, rms radial error=156.191, avgs=0 130/300: dt: 0.9000, rms radial error=155.313, avgs=0 135/300: dt: 0.9000, rms radial error=154.440, avgs=0 140/300: dt: 0.9000, rms radial error=153.572, avgs=0 145/300: dt: 0.9000, rms radial error=152.708, avgs=0 150/300: dt: 0.9000, rms radial error=151.849, avgs=0 155/300: dt: 0.9000, rms radial error=150.995, avgs=0 160/300: dt: 0.9000, rms radial error=150.146, avgs=0 165/300: dt: 0.9000, rms radial error=149.301, avgs=0 170/300: dt: 0.9000, rms radial error=148.461, avgs=0 175/300: dt: 0.9000, rms radial error=147.626, avgs=0 180/300: dt: 0.9000, rms radial error=146.795, avgs=0 185/300: dt: 0.9000, rms radial error=145.968, avgs=0 190/300: dt: 0.9000, rms radial error=145.147, avgs=0 195/300: dt: 0.9000, rms radial error=144.329, avgs=0 200/300: dt: 0.9000, rms radial error=143.517, avgs=0 205/300: dt: 0.9000, rms radial error=142.708, avgs=0 210/300: dt: 0.9000, rms radial error=141.904, avgs=0 215/300: dt: 0.9000, rms radial error=141.105, avgs=0 220/300: dt: 0.9000, rms radial error=140.310, avgs=0 225/300: dt: 0.9000, rms radial error=139.520, avgs=0 230/300: dt: 0.9000, rms radial error=138.733, avgs=0 235/300: dt: 0.9000, rms radial error=137.952, avgs=0 240/300: dt: 0.9000, rms radial error=137.174, avgs=0 245/300: dt: 0.9000, rms radial error=136.401, avgs=0 250/300: dt: 0.9000, rms radial error=135.632, avgs=0 255/300: dt: 0.9000, rms radial error=134.868, avgs=0 260/300: dt: 0.9000, rms radial error=134.108, avgs=0 265/300: dt: 0.9000, rms radial error=133.352, avgs=0 270/300: dt: 0.9000, rms radial error=132.600, avgs=0 275/300: dt: 0.9000, rms radial error=131.852, avgs=0 280/300: dt: 0.9000, rms radial error=131.109, avgs=0 285/300: dt: 0.9000, rms radial error=130.369, avgs=0 290/300: dt: 0.9000, rms radial error=129.634, avgs=0 295/300: dt: 0.9000, rms radial error=128.903, avgs=0 300/300: dt: 0.9000, rms radial error=128.178, avgs=0 spherical inflation complete. epoch 1 (K=10.0), pass 1, starting sse = 19853.58 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00020 epoch 2 (K=40.0), pass 1, starting sse = 3580.44 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00009 epoch 3 (K=160.0), pass 1, starting sse = 418.50 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.07/14 = 0.00499 epoch 4 (K=640.0), pass 1, starting sse = 30.09 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.07/17 = 0.00406 final distance error %100000.00 writing spherical brain to ../surf/lh.qsphere.nofix spherical transformation took 0.0789 hours FSRUNTIME@ mris_sphere 0.0789 hours 4 threads #VMPC# mris_sphere VmPeak 956444 mris_sphere done 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: 4 scaling brain by 0.270... inflating... projecting onto sphere... surface projected - minimizing metric distortion... vertex spacing 0.89 +- 0.55 (0.00-->8.27) (max @ vno 59545 --> 60805) face area 0.02 +- 0.02 (-0.21-->0.69) Entering MRISinflateToSphere() inflating to sphere (rms error < 2.00) 000: dt: 0.0000, rms radial error=177.806, avgs=0 005/300: dt: 0.9000, rms radial error=177.546, avgs=0 010/300: dt: 0.9000, rms radial error=176.988, avgs=0 015/300: dt: 0.9000, rms radial error=176.256, avgs=0 020/300: dt: 0.9000, rms radial error=175.422, avgs=0 025/300: dt: 0.9000, rms radial error=174.530, avgs=0 030/300: dt: 0.9000, rms radial error=173.605, avgs=0 035/300: dt: 0.9000, rms radial error=172.663, avgs=0 040/300: dt: 0.9000, rms radial error=171.712, avgs=0 045/300: dt: 0.9000, rms radial error=170.764, avgs=0 050/300: dt: 0.9000, rms radial error=169.817, avgs=0 055/300: dt: 0.9000, rms radial error=168.873, avgs=0 060/300: dt: 0.9000, rms radial error=167.932, avgs=0 065/300: dt: 0.9000, rms radial error=166.996, avgs=0 070/300: dt: 0.9000, rms radial error=166.063, avgs=0 075/300: dt: 0.9000, rms radial error=165.136, avgs=0 080/300: dt: 0.9000, rms radial error=164.213, avgs=0 085/300: dt: 0.9000, rms radial error=163.295, avgs=0 090/300: dt: 0.9000, rms radial error=162.381, avgs=0 095/300: dt: 0.9000, rms radial error=161.473, avgs=0 100/300: dt: 0.9000, rms radial error=160.569, avgs=0 105/300: dt: 0.9000, rms radial error=159.670, avgs=0 110/300: dt: 0.9000, rms radial error=158.775, avgs=0 115/300: dt: 0.9000, rms radial error=157.885, avgs=0 120/300: dt: 0.9000, rms radial error=157.000, avgs=0 125/300: dt: 0.9000, rms radial error=156.120, avgs=0 130/300: dt: 0.9000, rms radial error=155.244, avgs=0 135/300: dt: 0.9000, rms radial error=154.373, avgs=0 140/300: dt: 0.9000, rms radial error=153.507, avgs=0 145/300: dt: 0.9000, rms radial error=152.645, avgs=0 150/300: dt: 0.9000, rms radial error=151.788, avgs=0 155/300: dt: 0.9000, rms radial error=150.935, avgs=0 160/300: dt: 0.9000, rms radial error=150.087, avgs=0 165/300: dt: 0.9000, rms radial error=149.243, avgs=0 170/300: dt: 0.9000, rms radial error=148.405, avgs=0 175/300: dt: 0.9000, rms radial error=147.570, avgs=0 180/300: dt: 0.9000, rms radial error=146.740, avgs=0 185/300: dt: 0.9000, rms radial error=145.915, avgs=0 190/300: dt: 0.9000, rms radial error=145.094, avgs=0 195/300: dt: 0.9000, rms radial error=144.278, avgs=0 200/300: dt: 0.9000, rms radial error=143.467, avgs=0 205/300: dt: 0.9000, rms radial error=142.660, avgs=0 210/300: dt: 0.9000, rms radial error=141.858, avgs=0 215/300: dt: 0.9000, rms radial error=141.060, avgs=0 220/300: dt: 0.9000, rms radial error=140.266, avgs=0 225/300: dt: 0.9000, rms radial error=139.477, avgs=0 230/300: dt: 0.9000, rms radial error=138.692, avgs=0 235/300: dt: 0.9000, rms radial error=137.912, avgs=0 240/300: dt: 0.9000, rms radial error=137.136, avgs=0 245/300: dt: 0.9000, rms radial error=136.364, avgs=0 250/300: dt: 0.9000, rms radial error=135.596, avgs=0 255/300: dt: 0.9000, rms radial error=134.833, avgs=0 260/300: dt: 0.9000, rms radial error=134.074, avgs=0 265/300: dt: 0.9000, rms radial error=133.319, avgs=0 270/300: dt: 0.9000, rms radial error=132.568, avgs=0 275/300: dt: 0.9000, rms radial error=131.822, avgs=0 280/300: dt: 0.9000, rms radial error=131.079, avgs=0 285/300: dt: 0.9000, rms radial error=130.341, avgs=0 290/300: dt: 0.9000, rms radial error=129.607, avgs=0 295/300: dt: 0.9000, rms radial error=128.877, avgs=0 300/300: dt: 0.9000, rms radial error=128.151, avgs=0 spherical inflation complete. epoch 1 (K=10.0), pass 1, starting sse = 20518.54 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00022 epoch 2 (K=40.0), pass 1, starting sse = 3677.43 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00008 epoch 3 (K=160.0), pass 1, starting sse = 442.89 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.11/15 = 0.00703 epoch 4 (K=640.0), pass 1, starting sse = 33.57 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.12/19 = 0.00646 final distance error %100000.00 writing spherical brain to ../surf/rh.qsphere.nofix spherical transformation took 0.0798 hours FSRUNTIME@ mris_sphere 0.0798 hours 4 threads #VMPC# mris_sphere VmPeak 1054304 mris_sphere done PIDs (3042489 3042492) completed and logs appended. #@# Fix Topology lh Wed Jan 12 18:43:58 CST 2022 mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 UMNC03 lh #@# Fix Topology rh Wed Jan 12 18:43:58 CST 2022 mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 UMNC03 rh Waiting for PID 3042679 of (3042679 3042682) to complete... Waiting for PID 3042682 of (3042679 3042682) to complete... mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 UMNC03 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=-82 (nv=162838, nf=325840, ne=488760, g=42) 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 9 iterations marking ambiguous vertices... 9588 ambiguous faces found in tessellation segmenting defects... 50 defects found, arbitrating ambiguous regions... analyzing neighboring defects... -merging segment 10 into 7 49 defects to be corrected 0 vertices coincident reading input surface /home/valia/mmvt_root/subjects/UMNC03/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: -9.6044 (-4.8022) -vertex loglikelihood: -6.5072 (-3.2536) -normal dot loglikelihood: -3.6835 (-3.6835) -quad curv loglikelihood: -5.8346 (-2.9173) Total Loglikelihood : -25.6298 CORRECTING DEFECT 0 (vertices=76, convex hull=35, v0=515) After retessellation of defect 0 (v0=515), euler #=-43 (157079,469123,312001) : difference with theory (-46) = -3 CORRECTING DEFECT 1 (vertices=223, convex hull=212, v0=6246) After retessellation of defect 1 (v0=6246), euler #=-42 (157168,469491,312281) : difference with theory (-45) = -3 CORRECTING DEFECT 2 (vertices=29, convex hull=60, v0=6359) After retessellation of defect 2 (v0=6359), euler #=-41 (157180,469550,312329) : difference with theory (-44) = -3 CORRECTING DEFECT 3 (vertices=19, convex hull=56, v0=34209) After retessellation of defect 3 (v0=34209), euler #=-40 (157190,469600,312370) : difference with theory (-43) = -3 CORRECTING DEFECT 4 (vertices=48, convex hull=80, v0=44173) After retessellation of defect 4 (v0=44173), euler #=-39 (157198,469662,312425) : difference with theory (-42) = -3 CORRECTING DEFECT 5 (vertices=22, convex hull=59, v0=45185) After retessellation of defect 5 (v0=45185), euler #=-38 (157212,469726,312476) : difference with theory (-41) = -3 CORRECTING DEFECT 6 (vertices=13, convex hull=22, v0=48925) After retessellation of defect 6 (v0=48925), euler #=-37 (157214,469738,312487) : difference with theory (-40) = -3 CORRECTING DEFECT 7 (vertices=721, convex hull=537, v0=52215) 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 7 (v0=52215), euler #=-35 (157266,470197,312896) : difference with theory (-39) = -4 CORRECTING DEFECT 8 (vertices=25, convex hull=52, v0=57113) After retessellation of defect 8 (v0=57113), euler #=-34 (157274,470240,312932) : difference with theory (-38) = -4 CORRECTING DEFECT 9 (vertices=22, convex hull=46, v0=60869) After retessellation of defect 9 (v0=60869), euler #=-33 (157284,470283,312966) : difference with theory (-37) = -4 CORRECTING DEFECT 10 (vertices=15, convex hull=27, v0=65090) After retessellation of defect 10 (v0=65090), euler #=-32 (157291,470313,312990) : difference with theory (-36) = -4 CORRECTING DEFECT 11 (vertices=123, convex hull=68, v0=66320) After retessellation of defect 11 (v0=66320), euler #=-31 (157309,470394,313054) : difference with theory (-35) = -4 CORRECTING DEFECT 12 (vertices=571, convex hull=380, v0=68939) After retessellation of defect 12 (v0=68939), euler #=-30 (157488,471110,313592) : difference with theory (-34) = -4 CORRECTING DEFECT 13 (vertices=23, convex hull=27, v0=70416) After retessellation of defect 13 (v0=70416), euler #=-29 (157492,471130,313609) : difference with theory (-33) = -4 CORRECTING DEFECT 14 (vertices=6, convex hull=26, v0=71980) After retessellation of defect 14 (v0=71980), euler #=-28 (157495,471146,313623) : difference with theory (-32) = -4 CORRECTING DEFECT 15 (vertices=5, convex hull=26, v0=74466) After retessellation of defect 15 (v0=74466), euler #=-27 (157497,471158,313634) : difference with theory (-31) = -4 CORRECTING DEFECT 16 (vertices=235, convex hull=333, v0=75874) After retessellation of defect 16 (v0=75874), euler #=-27 (157629,471722,314066) : difference with theory (-30) = -3 CORRECTING DEFECT 17 (vertices=16, convex hull=37, v0=78663) After retessellation of defect 17 (v0=78663), euler #=-26 (157635,471752,314091) : difference with theory (-29) = -3 CORRECTING DEFECT 18 (vertices=50, convex hull=82, v0=78914) After retessellation of defect 18 (v0=78914), euler #=-25 (157659,471858,314174) : difference with theory (-28) = -3 CORRECTING DEFECT 19 (vertices=45, convex hull=92, v0=80367) After retessellation of defect 19 (v0=80367), euler #=-24 (157686,471973,314263) : difference with theory (-27) = -3 CORRECTING DEFECT 20 (vertices=60, convex hull=84, v0=84132) After retessellation of defect 20 (v0=84132), euler #=-23 (157713,472092,314356) : difference with theory (-26) = -3 CORRECTING DEFECT 21 (vertices=38, convex hull=28, v0=84259) After retessellation of defect 21 (v0=84259), euler #=-22 (157717,472115,314376) : difference with theory (-25) = -3 CORRECTING DEFECT 22 (vertices=161, convex hull=56, v0=88663) After retessellation of defect 22 (v0=88663), euler #=-21 (157735,472189,314433) : difference with theory (-24) = -3 CORRECTING DEFECT 23 (vertices=33, convex hull=63, v0=91487) After retessellation of defect 23 (v0=91487), euler #=-20 (157755,472276,314501) : difference with theory (-23) = -3 CORRECTING DEFECT 24 (vertices=20, convex hull=25, v0=101174) After retessellation of defect 24 (v0=101174), euler #=-19 (157759,472297,314519) : difference with theory (-22) = -3 CORRECTING DEFECT 25 (vertices=28, convex hull=35, v0=101497) After retessellation of defect 25 (v0=101497), euler #=-18 (157762,472318,314538) : difference with theory (-21) = -3 CORRECTING DEFECT 26 (vertices=24, convex hull=21, v0=102509) After retessellation of defect 26 (v0=102509), euler #=-17 (157767,472341,314557) : difference with theory (-20) = -3 CORRECTING DEFECT 27 (vertices=52, convex hull=55, v0=103052) After retessellation of defect 27 (v0=103052), euler #=-16 (157772,472377,314589) : difference with theory (-19) = -3 CORRECTING DEFECT 28 (vertices=609, convex hull=259, v0=104107) After retessellation of defect 28 (v0=104107), euler #=-15 (157811,472616,314790) : difference with theory (-18) = -3 CORRECTING DEFECT 29 (vertices=63, convex hull=100, v0=107512) After retessellation of defect 29 (v0=107512), euler #=-14 (157848,472772,314910) : difference with theory (-17) = -3 CORRECTING DEFECT 30 (vertices=287, convex hull=209, v0=110165) After retessellation of defect 30 (v0=110165), euler #=-16 (157957,473211,315238) : difference with theory (-16) = 0 CORRECTING DEFECT 31 (vertices=870, convex hull=522, v0=111969) 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 31 (v0=111969), euler #=-15 (158004,473622,315603) : difference with theory (-15) = 0 CORRECTING DEFECT 32 (vertices=108, convex hull=64, v0=112107) After retessellation of defect 32 (v0=112107), euler #=-14 (158022,473699,315663) : difference with theory (-14) = 0 CORRECTING DEFECT 33 (vertices=67, convex hull=42, v0=114332) After retessellation of defect 33 (v0=114332), euler #=-13 (158030,473739,315696) : difference with theory (-13) = 0 CORRECTING DEFECT 34 (vertices=17, convex hull=25, v0=116822) After retessellation of defect 34 (v0=116822), euler #=-12 (158033,473754,315709) : difference with theory (-12) = 0 CORRECTING DEFECT 35 (vertices=53, convex hull=65, v0=117825) After retessellation of defect 35 (v0=117825), euler #=-11 (158053,473840,315776) : difference with theory (-11) = 0 CORRECTING DEFECT 36 (vertices=226, convex hull=63, v0=117966) After retessellation of defect 36 (v0=117966), euler #=-10 (158085,473961,315866) : difference with theory (-10) = 0 CORRECTING DEFECT 37 (vertices=232, convex hull=135, v0=118016) After retessellation of defect 37 (v0=118016), euler #=-9 (158135,474170,316026) : difference with theory (-9) = 0 CORRECTING DEFECT 38 (vertices=32, convex hull=34, v0=119913) After retessellation of defect 38 (v0=119913), euler #=-8 (158141,474199,316050) : difference with theory (-8) = 0 CORRECTING DEFECT 39 (vertices=17, convex hull=49, v0=121064) After retessellation of defect 39 (v0=121064), euler #=-7 (158152,474254,316095) : difference with theory (-7) = 0 CORRECTING DEFECT 40 (vertices=121, convex hull=125, v0=122842) After retessellation of defect 40 (v0=122842), euler #=-6 (158192,474432,316234) : difference with theory (-6) = 0 CORRECTING DEFECT 41 (vertices=5, convex hull=17, v0=128759) After retessellation of defect 41 (v0=128759), euler #=-5 (158192,474436,316239) : difference with theory (-5) = 0 CORRECTING DEFECT 42 (vertices=33, convex hull=67, v0=130653) After retessellation of defect 42 (v0=130653), euler #=-4 (158201,474492,316287) : difference with theory (-4) = 0 CORRECTING DEFECT 43 (vertices=7, convex hull=16, v0=141705) After retessellation of defect 43 (v0=141705), euler #=-3 (158202,474497,316292) : difference with theory (-3) = 0 CORRECTING DEFECT 44 (vertices=147, convex hull=50, v0=145270) After retessellation of defect 44 (v0=145270), euler #=-2 (158211,474542,316329) : difference with theory (-2) = 0 CORRECTING DEFECT 45 (vertices=31, convex hull=47, v0=146900) After retessellation of defect 45 (v0=146900), euler #=-1 (158214,474569,316354) : difference with theory (-1) = 0 CORRECTING DEFECT 46 (vertices=62, convex hull=78, v0=147833) After retessellation of defect 46 (v0=147833), euler #=0 (158239,474676,316437) : difference with theory (0) = 0 CORRECTING DEFECT 47 (vertices=22, convex hull=48, v0=157871) After retessellation of defect 47 (v0=157871), euler #=1 (158249,474724,316476) : difference with theory (1) = 0 CORRECTING DEFECT 48 (vertices=49, convex hull=39, v0=162175) After retessellation of defect 48 (v0=162175), euler #=2 (158251,474747,316498) : difference with theory (2) = 0 computing original vertex metric properties... storing new metric properties... computing tessellation statistics... vertex spacing 0.89 +- 0.25 (0.07-->12.30) (max @ vno 110385 --> 117752) face area -nan +- -nan (1000.00-->-1.00) performing soap bubble on retessellated vertices for 0 iterations... vertex spacing 0.89 +- 0.25 (0.07-->12.30) (max @ vno 110385 --> 117752) face area -nan +- -nan (1000.00-->-1.00) tessellation finished, orienting corrected surface... 175 mutations (34.6%), 331 crossovers (65.4%), 616 vertices were eliminated building final representation... 4587 vertices and 0 faces have been removed from triangulation after topology correction, eno=2 (nv=158251, nf=316498, ne=474747, g=0) writing corrected surface to /home/valia/mmvt_root/subjects/UMNC03/surf/lh.orig.premesh... 0.000 % of the vertices (0 vertices) exhibit an orientation change removing intersecting faces 000: 390 intersecting 001: 71 intersecting 002: 6 intersecting terminating search with 0 intersecting topology fixing took 39.2 minutes FSRUNTIME@ mris_fix_topology lh 0.6528 hours 4 threads #VMPC# mris_fix_topology VmPeak 1238312 mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 UMNC03 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=-118 (nv=168332, nf=336900, ne=505350, g=60) 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 9 iterations marking ambiguous vertices... 12213 ambiguous faces found in tessellation segmenting defects... 62 defects found, arbitrating ambiguous regions... analyzing neighboring defects... -merging segment 21 into 19 -merging segment 31 into 26 -merging segment 44 into 36 -merging segment 41 into 40 58 defects to be corrected 0 vertices coincident reading input surface /home/valia/mmvt_root/subjects/UMNC03/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: -9.5688 (-4.7844) -vertex loglikelihood: -6.5208 (-3.2604) -normal dot loglikelihood: -3.5927 (-3.5927) -quad curv loglikelihood: -6.0250 (-3.0125) Total Loglikelihood : -25.7073 CORRECTING DEFECT 0 (vertices=19, convex hull=65, v0=15989) After retessellation of defect 0 (v0=15989), euler #=-59 (161225,481561,320277) : difference with theory (-55) = 4 CORRECTING DEFECT 1 (vertices=722, convex hull=270, v0=18965) After retessellation of defect 1 (v0=18965), euler #=-58 (161271,481834,320505) : difference with theory (-54) = 4 CORRECTING DEFECT 2 (vertices=27, convex hull=48, v0=19894) After retessellation of defect 2 (v0=19894), euler #=-57 (161284,481890,320549) : difference with theory (-53) = 4 CORRECTING DEFECT 3 (vertices=29, convex hull=65, v0=25641) After retessellation of defect 3 (v0=25641), euler #=-56 (161299,481959,320604) : difference with theory (-52) = 4 CORRECTING DEFECT 4 (vertices=29, convex hull=73, v0=32940) After retessellation of defect 4 (v0=32940), euler #=-55 (161315,482038,320668) : difference with theory (-51) = 4 CORRECTING DEFECT 5 (vertices=31, convex hull=24, v0=34399) After retessellation of defect 5 (v0=34399), euler #=-54 (161319,482057,320684) : difference with theory (-50) = 4 CORRECTING DEFECT 6 (vertices=52, convex hull=80, v0=37938) After retessellation of defect 6 (v0=37938), euler #=-53 (161337,482145,320755) : difference with theory (-49) = 4 CORRECTING DEFECT 7 (vertices=28, convex hull=58, v0=46458) After retessellation of defect 7 (v0=46458), euler #=-52 (161353,482217,320812) : difference with theory (-48) = 4 CORRECTING DEFECT 8 (vertices=68, convex hull=88, v0=46694) After retessellation of defect 8 (v0=46694), euler #=-51 (161383,482348,320914) : difference with theory (-47) = 4 CORRECTING DEFECT 9 (vertices=38, convex hull=34, v0=49017) After retessellation of defect 9 (v0=49017), euler #=-50 (161386,482368,320932) : difference with theory (-46) = 4 CORRECTING DEFECT 10 (vertices=573, convex hull=243, v0=58293) After retessellation of defect 10 (v0=58293), euler #=-49 (161403,482543,321091) : difference with theory (-45) = 4 CORRECTING DEFECT 11 (vertices=69, convex hull=101, v0=64076) After retessellation of defect 11 (v0=64076), euler #=-48 (161443,482704,321213) : difference with theory (-44) = 4 CORRECTING DEFECT 12 (vertices=32, convex hull=63, v0=64318) After retessellation of defect 12 (v0=64318), euler #=-47 (161456,482769,321266) : difference with theory (-43) = 4 CORRECTING DEFECT 13 (vertices=34, convex hull=65, v0=66232) After retessellation of defect 13 (v0=66232), euler #=-46 (161477,482858,321335) : difference with theory (-42) = 4 CORRECTING DEFECT 14 (vertices=292, convex hull=192, v0=68286) After retessellation of defect 14 (v0=68286), euler #=-45 (161567,483224,321612) : difference with theory (-41) = 4 CORRECTING DEFECT 15 (vertices=219, convex hull=151, v0=68975) After retessellation of defect 15 (v0=68975), euler #=-44 (161651,483544,321849) : difference with theory (-40) = 4 CORRECTING DEFECT 16 (vertices=31, convex hull=73, v0=69468) After retessellation of defect 16 (v0=69468), euler #=-43 (161666,483614,321905) : difference with theory (-39) = 4 CORRECTING DEFECT 17 (vertices=35, convex hull=26, v0=71908) After retessellation of defect 17 (v0=71908), euler #=-42 (161673,483642,321927) : difference with theory (-38) = 4 CORRECTING DEFECT 18 (vertices=14, convex hull=33, v0=79773) After retessellation of defect 18 (v0=79773), euler #=-41 (161679,483669,321949) : difference with theory (-37) = 4 CORRECTING DEFECT 19 (vertices=197, convex hull=61, v0=81522) After retessellation of defect 19 (v0=81522), euler #=-39 (161707,483780,322034) : difference with theory (-36) = 3 CORRECTING DEFECT 20 (vertices=19, convex hull=55, v0=81733) After retessellation of defect 20 (v0=81733), euler #=-38 (161716,483829,322075) : difference with theory (-35) = 3 CORRECTING DEFECT 21 (vertices=58, convex hull=107, v0=84329) After retessellation of defect 21 (v0=84329), euler #=-37 (161754,483990,322199) : difference with theory (-34) = 3 CORRECTING DEFECT 22 (vertices=159, convex hull=50, v0=85344) After retessellation of defect 22 (v0=85344), euler #=-36 (161777,484081,322268) : difference with theory (-33) = 3 CORRECTING DEFECT 23 (vertices=48, convex hull=65, v0=87918) After retessellation of defect 23 (v0=87918), euler #=-35 (161805,484188,322348) : difference with theory (-32) = 3 CORRECTING DEFECT 24 (vertices=265, convex hull=70, v0=94476) After retessellation of defect 24 (v0=94476), euler #=-34 (161836,484315,322445) : difference with theory (-31) = 3 CORRECTING DEFECT 25 (vertices=199, convex hull=200, v0=100077) After retessellation of defect 25 (v0=100077), euler #=-32 (161923,484676,322721) : difference with theory (-30) = 2 CORRECTING DEFECT 26 (vertices=17, convex hull=22, v0=101666) After retessellation of defect 26 (v0=101666), euler #=-31 (161925,484690,322734) : difference with theory (-29) = 2 CORRECTING DEFECT 27 (vertices=82, convex hull=69, v0=102915) After retessellation of defect 27 (v0=102915), euler #=-30 (161943,484772,322799) : difference with theory (-28) = 2 CORRECTING DEFECT 28 (vertices=295, convex hull=134, v0=104730) After retessellation of defect 28 (v0=104730), euler #=-29 (161986,484967,322952) : difference with theory (-27) = 2 CORRECTING DEFECT 29 (vertices=67, convex hull=81, v0=106858) After retessellation of defect 29 (v0=106858), euler #=-28 (161998,485043,323017) : difference with theory (-26) = 2 CORRECTING DEFECT 30 (vertices=33, convex hull=67, v0=109295) After retessellation of defect 30 (v0=109295), euler #=-27 (162012,485110,323071) : difference with theory (-25) = 2 CORRECTING DEFECT 31 (vertices=1019, convex hull=352, v0=109874) After retessellation of defect 31 (v0=109874), euler #=-26 (162167,485754,323561) : difference with theory (-24) = 2 CORRECTING DEFECT 32 (vertices=12, convex hull=23, v0=110879) After retessellation of defect 32 (v0=110879), euler #=-25 (162170,485769,323574) : difference with theory (-23) = 2 CORRECTING DEFECT 33 (vertices=21, convex hull=74, v0=113093) After retessellation of defect 33 (v0=113093), euler #=-24 (162178,485820,323618) : difference with theory (-22) = 2 CORRECTING DEFECT 34 (vertices=128, convex hull=133, v0=113299) After retessellation of defect 34 (v0=113299), euler #=-22 (162240,486068,323806) : difference with theory (-21) = 1 CORRECTING DEFECT 35 (vertices=115, convex hull=48, v0=117057) After retessellation of defect 35 (v0=117057), euler #=-21 (162251,486119,323847) : difference with theory (-20) = 1 CORRECTING DEFECT 36 (vertices=86, convex hull=57, v0=117418) After retessellation of defect 36 (v0=117418), euler #=-20 (162260,486171,323891) : difference with theory (-19) = 1 CORRECTING DEFECT 37 (vertices=9, convex hull=28, v0=122030) After retessellation of defect 37 (v0=122030), euler #=-19 (162261,486182,323902) : difference with theory (-18) = 1 CORRECTING DEFECT 38 (vertices=455, convex hull=226, v0=122170) After retessellation of defect 38 (v0=122170), euler #=-17 (162309,486429,324103) : difference with theory (-17) = 0 CORRECTING DEFECT 39 (vertices=29, convex hull=60, v0=124099) After retessellation of defect 39 (v0=124099), euler #=-16 (162323,486493,324154) : difference with theory (-16) = 0 CORRECTING DEFECT 40 (vertices=130, convex hull=124, v0=124108) After retessellation of defect 40 (v0=124108), euler #=-15 (162358,486653,324280) : difference with theory (-15) = 0 CORRECTING DEFECT 41 (vertices=82, convex hull=61, v0=125102) After retessellation of defect 41 (v0=125102), euler #=-14 (162373,486725,324338) : difference with theory (-14) = 0 CORRECTING DEFECT 42 (vertices=222, convex hull=95, v0=125441) After retessellation of defect 42 (v0=125441), euler #=-13 (162421,486908,324474) : difference with theory (-13) = 0 CORRECTING DEFECT 43 (vertices=27, convex hull=51, v0=129223) After retessellation of defect 43 (v0=129223), euler #=-12 (162430,486954,324512) : difference with theory (-12) = 0 CORRECTING DEFECT 44 (vertices=13, convex hull=20, v0=129324) After retessellation of defect 44 (v0=129324), euler #=-11 (162430,486959,324518) : difference with theory (-11) = 0 CORRECTING DEFECT 45 (vertices=83, convex hull=103, v0=129440) After retessellation of defect 45 (v0=129440), euler #=-10 (162456,487079,324613) : difference with theory (-10) = 0 CORRECTING DEFECT 46 (vertices=75, convex hull=66, v0=130276) After retessellation of defect 46 (v0=130276), euler #=-9 (162474,487161,324678) : difference with theory (-9) = 0 CORRECTING DEFECT 47 (vertices=6, convex hull=14, v0=136698) After retessellation of defect 47 (v0=136698), euler #=-8 (162476,487171,324687) : difference with theory (-8) = 0 CORRECTING DEFECT 48 (vertices=9, convex hull=28, v0=137014) After retessellation of defect 48 (v0=137014), euler #=-7 (162477,487183,324699) : difference with theory (-7) = 0 CORRECTING DEFECT 49 (vertices=42, convex hull=45, v0=138410) After retessellation of defect 49 (v0=138410), euler #=-6 (162485,487225,324734) : difference with theory (-6) = 0 CORRECTING DEFECT 50 (vertices=10, convex hull=21, v0=145073) After retessellation of defect 50 (v0=145073), euler #=-5 (162485,487232,324742) : difference with theory (-5) = 0 CORRECTING DEFECT 51 (vertices=576, convex hull=212, v0=148095) After retessellation of defect 51 (v0=148095), euler #=-4 (162540,487510,324966) : difference with theory (-4) = 0 CORRECTING DEFECT 52 (vertices=49, convex hull=56, v0=154750) After retessellation of defect 52 (v0=154750), euler #=-3 (162561,487592,325028) : difference with theory (-3) = 0 CORRECTING DEFECT 53 (vertices=7, convex hull=24, v0=160220) After retessellation of defect 53 (v0=160220), euler #=-2 (162561,487599,325036) : difference with theory (-2) = 0 CORRECTING DEFECT 54 (vertices=57, convex hull=76, v0=162433) After retessellation of defect 54 (v0=162433), euler #=-1 (162581,487690,325108) : difference with theory (-1) = 0 CORRECTING DEFECT 55 (vertices=31, convex hull=57, v0=163358) After retessellation of defect 55 (v0=163358), euler #=0 (162590,487739,325149) : difference with theory (0) = 0 CORRECTING DEFECT 56 (vertices=14, convex hull=26, v0=164441) After retessellation of defect 56 (v0=164441), euler #=1 (162592,487755,325164) : difference with theory (1) = 0 CORRECTING DEFECT 57 (vertices=23, convex hull=37, v0=165824) After retessellation of defect 57 (v0=165824), euler #=2 (162597,487785,325190) : difference with theory (2) = 0 computing original vertex metric properties... storing new metric properties... computing tessellation statistics... vertex spacing 0.89 +- 0.25 (0.06-->21.30) (max @ vno 25345 --> 44560) face area -nan +- -nan (1000.00-->-1.00) performing soap bubble on retessellated vertices for 0 iterations... vertex spacing 0.89 +- 0.25 (0.06-->21.30) (max @ vno 25345 --> 44560) face area -nan +- -nan (1000.00-->-1.00) tessellation finished, orienting corrected surface... 215 mutations (35.1%), 397 crossovers (64.9%), 644 vertices were eliminated building final representation... 5735 vertices and 0 faces have been removed from triangulation after topology correction, eno=2 (nv=162597, nf=325190, ne=487785, g=0) writing corrected surface to /home/valia/mmvt_root/subjects/UMNC03/surf/rh.orig.premesh... 0.000 % of the vertices (0 vertices) exhibit an orientation change removing intersecting faces 000: 510 intersecting 001: 76 intersecting 002: 35 intersecting 003: 12 intersecting step 1 with no progress (num=12, old_num=12) 004: 12 intersecting step 2 with no progress (num=12, old_num=12) 005: 12 intersecting step 3 with no progress (num=12, old_num=12) 006: 12 intersecting step 4 with no progress (num=12, old_num=12) 007: 12 intersecting step 5 with no progress (num=12, old_num=12) 008: 12 intersecting step 6 with no progress (num=12, old_num=12) 009: 12 intersecting step 7 with no progress (num=12, old_num=12) 010: 12 intersecting step 8 with no progress (num=12, old_num=12) 011: 12 intersecting step 9 with no progress (num=12, old_num=12) 012: 12 intersecting step 10 with no progress (num=12, old_num=12) 013: 12 intersecting step 11 with no progress (num=12, old_num=12) 014: 12 intersecting step 12 with no progress (num=12, old_num=12) 015: 12 intersecting step 13 with no progress (num=12, old_num=12) 016: 12 intersecting step 14 with no progress (num=12, old_num=12) 017: 12 intersecting step 15 with no progress (num=12, old_num=12) 018: 12 intersecting step 16 with no progress (num=12, old_num=12) terminating search with 12 intersecting topology fixing took 41.0 minutes FSRUNTIME@ mris_fix_topology rh 0.6831 hours 4 threads #VMPC# mris_fix_topology VmPeak 1244008 PIDs (3042679 3042682) completed and logs appended. mris_euler_number ../surf/lh.orig.premesh euler # = v-e+f = 2g-2: 158251 - 474747 + 316498 = 2 --> 0 holes F =2V-4: 316498 = 316502-4 (0) 2E=3F: 949494 = 949494 (0) total defect index = 0 mris_euler_number ../surf/rh.orig.premesh euler # = v-e+f = 2g-2: 162597 - 487785 + 325190 = 2 --> 0 holes F =2V-4: 325190 = 325194-4 (0) 2E=3F: 975570 = 975570 (0) total defect index = 0 Wed Jan 12 19:24:59 CST 2022 setenv SUBJECTS_DIR /home/valia/mmvt_root/subjects cd /home/valia/mmvt_root/subjects/UMNC03/scripts /usr/local/freesurfer/7.2.0-1/bin/defect2seg --s UMNC03 freesurfer-linux-centos8_x86_64-7.2.0-20210720-aa8f76b defect2seg 7.2.0 Linux faraday 4.18.0-326.el8.x86_64 #1 SMP Wed Jul 28 21:21:05 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux pid 3044142 mri_label2vol --defects /home/valia/mmvt_root/subjects/UMNC03/surf/lh.orig.nofix /home/valia/mmvt_root/subjects/UMNC03/surf/lh.defect_labels /home/valia/mmvt_root/subjects/UMNC03/mri/orig.mgz 1000 0 /home/valia/mmvt_root/subjects/UMNC03/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 /home/valia/mmvt_root/subjects/UMNC03/mri/surface.defects.mgz mris_defects_pointset -s /home/valia/mmvt_root/subjects/UMNC03/surf/lh.orig.nofix -d /home/valia/mmvt_root/subjects/UMNC03/surf/lh.defect_labels -o /home/valia/mmvt_root/subjects/UMNC03/surf/lh.defects.pointset Reading in surface /home/valia/mmvt_root/subjects/UMNC03/surf/lh.orig.nofix Reading in defect segmentation /home/valia/mmvt_root/subjects/UMNC03/surf/lh.defect_labels #VMPC# mris_defects_pointset 587332 mris_defects_pointset done mri_label2vol --defects /home/valia/mmvt_root/subjects/UMNC03/surf/rh.orig.nofix /home/valia/mmvt_root/subjects/UMNC03/surf/rh.defect_labels /home/valia/mmvt_root/subjects/UMNC03/mri/surface.defects.mgz 2000 1 /home/valia/mmvt_root/subjects/UMNC03/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 /home/valia/mmvt_root/subjects/UMNC03/mri/surface.defects.mgz mris_defects_pointset -s /home/valia/mmvt_root/subjects/UMNC03/surf/rh.orig.nofix -d /home/valia/mmvt_root/subjects/UMNC03/surf/rh.defect_labels -o /home/valia/mmvt_root/subjects/UMNC03/surf/rh.defects.pointset Reading in surface /home/valia/mmvt_root/subjects/UMNC03/surf/rh.orig.nofix Reading in defect segmentation /home/valia/mmvt_root/subjects/UMNC03/surf/rh.defect_labels #VMPC# mris_defects_pointset 591736 mris_defects_pointset done Started at Wed Jan 12 19:24:59 CST 2022 Ended at Wed Jan 12 19:25:03 CST 2022 Defect2seg-Run-Time-Sec 4 Defect2seg-Run-Time-Min 0.08 Defect2seg-Run-Time-Hours 0.00 tkmeditfv UMNC03 brain.finalsurfs.mgz -defect defect2seg Done @#@FSTIME 2022:01:12:19:24:59 defect2seg N 2 e 4.14 S 0.24 U 6.33 P 158% M 274948 F 5 R 112141 W 0 c 346 w 411 I 10248 O 448 L 3.83 7.50 8.19 @#@FSLOADPOST 2022:01:12:19:25:03 defect2seg N 2 3.92 7.45 8.17 mris_remesh --remesh --iters 3 --input /home/valia/mmvt_root/subjects/UMNC03/surf/lh.orig.premesh --output /home/valia/mmvt_root/subjects/UMNC03/surf/lh.orig iters = 3 standard remeshing without target adjusted l: 0.710953 remeshing to edge length 0.710953 with 3 iterations avg qual before : 0.885449 after: 0.971042 Removing intersections Remeshed surface quality stats nv0 = 158251 nv = 163927 1.03587 Area 327850 0.30241 0.03373 0.084058 0.4972 Corner 983550 60.00000 8.82221 17.853333 144.1913 Edge 491775 0.84370 0.08258 0.484835 1.2920 Hinge 491775 10.09376 10.90682 0.000028 157.6612 mris_remesh done @#@FSTIME 2022:01:12:19:25:03 mris_remesh N 7 e 29.33 S 0.32 U 31.28 P 107% M 913296 F 2 R 227779 W 0 c 645 w 221 I 6384 O 11528 L 3.92 7.45 8.17 @#@FSLOADPOST 2022:01:12:19:25:32 mris_remesh N 7 2.92 6.93 7.98 mris_remesh --remesh --iters 3 --input /home/valia/mmvt_root/subjects/UMNC03/surf/rh.orig.premesh --output /home/valia/mmvt_root/subjects/UMNC03/surf/rh.orig iters = 3 standard remeshing without target adjusted l: 0.710949 remeshing to edge length 0.710949 with 3 iterations avg qual before : 0.887811 after: 0.971004 Removing intersections removing intersecting faces 000: 14 intersecting 001: 7 intersecting step 1 with no progress (num=7, old_num=7) 002: 7 intersecting step 2 with no progress (num=7, old_num=7) 003: 7 intersecting step 3 with no progress (num=7, old_num=7) 004: 7 intersecting step 4 with no progress (num=7, old_num=7) 005: 7 intersecting step 5 with no progress (num=7, old_num=7) 006: 7 intersecting step 6 with no progress (num=7, old_num=7) 007: 7 intersecting step 7 with no progress (num=7, old_num=7) 008: 7 intersecting step 8 with no progress (num=7, old_num=7) 009: 7 intersecting step 9 with no progress (num=7, old_num=7) 010: 7 intersecting step 10 with no progress (num=7, old_num=7) 011: 7 intersecting step 11 with no progress (num=7, old_num=7) 012: 7 intersecting step 12 with no progress (num=7, old_num=7) 013: 7 intersecting step 13 with no progress (num=7, old_num=7) 014: 7 intersecting step 14 with no progress (num=7, old_num=7) 015: 7 intersecting step 15 with no progress (num=7, old_num=7) 016: 7 intersecting step 16 with no progress (num=7, old_num=7) terminating search with 7 intersecting Remeshed surface quality stats nv0 = 162597 nv = 168790 1.03809 Area 337576 0.30218 0.03410 0.003031 0.4825 Corner 1012728 60.00000 8.87139 0.757848 177.6915 Edge 506364 0.84334 0.08272 0.045189 1.3062 Hinge 506364 10.19532 11.12042 0.000011 172.9117 mris_remesh done @#@FSTIME 2022:01:12:19:25:32 mris_remesh N 7 e 65.49 S 0.53 U 67.24 P 103% M 1199012 F 0 R 401457 W 0 c 830 w 318 I 0 O 11872 L 2.92 6.93 7.98 @#@FSLOADPOST 2022:01:12:19:26:38 mris_remesh N 7 1.78 5.75 7.49 /home/valia/mmvt_root/subjects/UMNC03/scripts mris_remove_intersection ../surf/lh.orig ../surf/lh.orig intersection removal took 0.00 hours writing corrected surface to ../surf/lh.orig @#@FSTIME 2022:01:12:19:26:38 mris_remove_intersection N 2 e 2.60 S 0.10 U 2.98 P 118% M 384680 F 2 R 66208 W 0 c 139 w 53 I 6088 O 11528 L 1.78 5.75 7.49 @#@FSLOADPOST 2022:01:12:19:26:40 mris_remove_intersection N 2 1.78 5.75 7.49 rm -f ../surf/lh.inflated /home/valia/mmvt_root/subjects/UMNC03/scripts mris_remove_intersection ../surf/rh.orig ../surf/rh.orig intersection removal took 0.01 hours removing intersecting faces 000: 7 intersecting step 1 with no progress (num=7, old_num=7) 001: 7 intersecting step 2 with no progress (num=7, old_num=7) 002: 7 intersecting step 3 with no progress (num=7, old_num=7) 003: 7 intersecting step 4 with no progress (num=7, old_num=7) 004: 7 intersecting step 5 with no progress (num=7, old_num=7) 005: 7 intersecting step 6 with no progress (num=7, old_num=7) 006: 7 intersecting step 7 with no progress (num=7, old_num=7) 007: 7 intersecting step 8 with no progress (num=7, old_num=7) 008: 7 intersecting step 9 with no progress (num=7, old_num=7) 009: 7 intersecting step 10 with no progress (num=7, old_num=7) 010: 7 intersecting step 11 with no progress (num=7, old_num=7) 011: 7 intersecting step 12 with no progress (num=7, old_num=7) 012: 7 intersecting step 13 with no progress (num=7, old_num=7) 013: 7 intersecting step 14 with no progress (num=7, old_num=7) 014: 7 intersecting step 15 with no progress (num=7, old_num=7) 015: 7 intersecting step 16 with no progress (num=7, old_num=7) terminating search with 7 intersecting writing corrected surface to ../surf/rh.orig @#@FSTIME 2022:01:12:19:26:40 mris_remove_intersection N 2 e 32.27 S 0.15 U 32.53 P 101% M 461156 F 0 R 92799 W 0 c 800 w 43 I 0 O 11872 L 1.78 5.75 7.49 @#@FSLOADPOST 2022:01:12:19:27:13 mris_remove_intersection N 2 1.52 5.24 7.25 rm -f ../surf/rh.inflated #-------------------------------------------- #@# AutoDetGWStats lh Wed Jan 12 19:27:13 CST 2022 cd /home/valia/mmvt_root/subjects/UMNC03/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 /home/valia/mmvt_root/subjects/UMNC03/mri setenv SUBJECTS_DIR /home/valia/mmvt_root/subjects mris_autodet_gwstats --o ../surf/autodet.gw.stats.lh.dat --i brain.finalsurfs.mgz --wm wm.mgz --surf ../surf/lh.orig.premesh border white: 292941 voxels (1.75%) border gray 349490 voxels (2.08%) 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=30022, wmmin=5, clip=110 Binarizing thresholding at 5 computing class statistics... low=30, hi=110.000000 CCS WM (99.0): 98.9 +- 8.4 [70.0 --> 110.0] CCS GM (72.0) : 72.4 +- 10.5 [30.0 --> 110.0] white_mean = 98.8565 +/- 8.38896, gray_mean = 72.3516 +/- 10.4867 using class modes intead of means, discounting robust sigmas.... MRIScomputeClassModes(): min=0 max=225 nbins=226 intensity peaks found at WM=103+-6.1, GM=67+-8.7 white_mode = 103, gray_mode = 67 std_scale = 1 Applying sanity checks, max_scale_down = 0.2 setting MIN_GRAY_AT_WHITE_BORDER to 56.5 (was 70.000000) setting MAX_BORDER_WHITE to 111.4 (was 105.000000) setting MIN_BORDER_WHITE to 67.0 (was 85.000000) setting MAX_CSF to 46.0 (was 40.000000) setting MAX_GRAY to 94.6 (was 95.000000) setting MAX_GRAY_AT_CSF_BORDER to 56.5 (was 75.000000) setting MIN_GRAY_AT_CSF_BORDER to 35.5 (was 40.000000) When placing the white surface white_border_hi = 111.389; white_border_low = 67; white_outside_low = 56.5133; white_inside_hi = 120; white_outside_hi = 111.389; When placing the pial surface pial_border_hi = 56.5133; pial_border_low = 35.5398; pial_outside_low = 10; pial_inside_hi = 94.611; pial_outside_hi = 51.2699; #VMPC# mris_autodet_gwstats VmPeak 829888 mris_autodet_gwstats done @#@FSTIME 2022:01:12:19:27:13 mris_autodet_gwstats N 8 e 3.67 S 0.07 U 5.04 P 139% M 272712 F 2 R 44645 W 0 c 309 w 55 I 5392 O 8 L 1.52 5.24 7.25 @#@FSLOADPOST 2022:01:12:19:27:16 mris_autodet_gwstats N 8 1.52 5.24 7.25 #-------------------------------------------- #@# AutoDetGWStats rh Wed Jan 12 19:27:16 CST 2022 cd /home/valia/mmvt_root/subjects/UMNC03/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 /home/valia/mmvt_root/subjects/UMNC03/mri setenv SUBJECTS_DIR /home/valia/mmvt_root/subjects mris_autodet_gwstats --o ../surf/autodet.gw.stats.rh.dat --i brain.finalsurfs.mgz --wm wm.mgz --surf ../surf/rh.orig.premesh border white: 292941 voxels (1.75%) border gray 349490 voxels (2.08%) 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=30022, wmmin=5, clip=110 Binarizing thresholding at 5 computing class statistics... low=30, hi=110.000000 CCS WM (99.0): 98.9 +- 8.4 [70.0 --> 110.0] CCS GM (72.0) : 72.4 +- 10.5 [30.0 --> 110.0] white_mean = 98.8565 +/- 8.38896, gray_mean = 72.3516 +/- 10.4867 using class modes intead of means, discounting robust sigmas.... MRIScomputeClassModes(): min=0 max=225 nbins=226 intensity peaks found at WM=103+-7.0, GM=67+-8.7 white_mode = 103, gray_mode = 67 std_scale = 1 Applying sanity checks, max_scale_down = 0.2 setting MIN_GRAY_AT_WHITE_BORDER to 56.5 (was 70.000000) setting MAX_BORDER_WHITE to 111.4 (was 105.000000) setting MIN_BORDER_WHITE to 67.0 (was 85.000000) setting MAX_CSF to 46.0 (was 40.000000) setting MAX_GRAY to 94.6 (was 95.000000) setting MAX_GRAY_AT_CSF_BORDER to 56.5 (was 75.000000) setting MIN_GRAY_AT_CSF_BORDER to 35.5 (was 40.000000) When placing the white surface white_border_hi = 111.389; white_border_low = 67; white_outside_low = 56.5133; white_inside_hi = 120; white_outside_hi = 111.389; When placing the pial surface pial_border_hi = 56.5133; pial_border_low = 35.5398; pial_outside_low = 10; pial_inside_hi = 94.611; pial_outside_hi = 51.2699; #VMPC# mris_autodet_gwstats VmPeak 833348 mris_autodet_gwstats done @#@FSTIME 2022:01:12:19:27:16 mris_autodet_gwstats N 8 e 3.66 S 0.08 U 5.04 P 140% M 278360 F 0 R 44563 W 0 c 187 w 62 I 32 O 8 L 1.52 5.24 7.25 @#@FSLOADPOST 2022:01:12:19:27:20 mris_autodet_gwstats N 8 1.55 5.19 7.22 #-------------------------------------------- #@# WhitePreAparc lh Wed Jan 12 19:27:20 CST 2022 cd /home/valia/mmvt_root/subjects/UMNC03/mri mris_place_surface --adgws-in ../surf/autodet.gw.stats.lh.dat --wm wm.mgz --threads 4 --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 /home/valia/mmvt_root/subjects/UMNC03/mri setenv SUBJECTS_DIR /home/valia/mmvt_root/subjects mris_place_surface --adgws-in ../surf/autodet.gw.stats.lh.dat --wm wm.mgz --threads 4 --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 Area 327850 0.26452 0.06539 0.000447 0.6684 Corner 983550 60.00000 9.86217 4.908212 145.6630 Edge 491775 0.78525 0.11914 0.025689 1.3326 Hinge 491775 6.66407 6.92960 0.000004 127.4984 Not reading in aparc Reading in input volume brain.finalsurfs.mgz Reading in wm volume wm.mgz MRIclipBrightWM(): nthresh=30022, wmmin=5, clip=110 MRIfindBrightNonWM(): 3170 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=6469, nmarked2=103, nripped=6469 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 MRISripSegs(): -2 2 0.5 ripped 0 vertex 81964: xyz = (-1.23003,2.16784,12.4573) oxyz = (-1.23003,2.16784,12.4573) wxzy = (-1.23003,2.16784,12.4573) pxyz = (0,0,0) CBVO Creating mask 163927 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=6469 #FML# MRISripMidline(): nmarked=6469, nmarked2=103, nripped=6469 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 = 111.3889620; border_low = 67.0000000; outside_low = 56.5132600; outside_hi = 111.3889620; sigma = 2; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=163927 Gdiag_no=-1 vno start=0, stop=163927 Replacing 255s with 0s #SI# sigma=2 had to be increased for 227 vertices, nripped=6469 mean border=80.0, 132 (132) missing vertices, mean dist 0.3 [0.5 (%35.2)->0.8 (%64.8))] %80 local maxima, %16 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0734 min Finding expansion regions mean absolute distance = 0.67 +- 0.77 5404 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=farad, 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=3924893.2, rms=10.906 001: dt: 0.5000, sse=2193150.2, rms=8.009 (26.559%) 002: dt: 0.5000, sse=1424427.9, rms=6.308 (21.245%) 003: dt: 0.5000, sse=1047506.7, rms=5.272 (16.414%) 004: dt: 0.5000, sse=838577.1, rms=4.593 (12.888%) 005: dt: 0.5000, sse=723217.5, rms=4.169 (9.230%) 006: dt: 0.5000, sse=659061.1, rms=3.912 (6.165%) 007: dt: 0.5000, sse=628683.0, rms=3.776 (3.468%) 008: dt: 0.5000, sse=608414.6, rms=3.686 (2.392%) rms = 3.6398/3.6859, sse=600119.7/608414.6, time step reduction 1 of 3 to 0.250 0 0 1 009: dt: 0.5000, sse=600119.7, rms=3.640 (1.249%) 010: dt: 0.2500, sse=363470.2, rms=2.356 (35.272%) 011: dt: 0.2500, sse=315611.4, rms=2.015 (14.464%) 012: dt: 0.2500, sse=304467.6, rms=1.920 (4.748%) 013: dt: 0.2500, sse=297621.2, rms=1.855 (3.385%) rms = 1.8209/1.8545, sse=294714.4/297621.2, time step reduction 2 of 3 to 0.125 0 0 1 014: dt: 0.2500, sse=294714.4, rms=1.821 (1.813%) 015: dt: 0.1250, sse=286709.5, rms=1.757 (3.492%) rms = 1.7430/1.7573, sse=285856.9/286709.5, time step reduction 3 of 3 to 0.062 0 0 1 016: dt: 0.1250, sse=285856.9, rms=1.743 (0.816%) maximum number of reductions reached, breaking from loop positioning took 1.2 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=6469 removing 2 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 2 vertices from ripped group in thread:0 removing 2 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6690, nmarked2=104, nripped=6690 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 = 111.3889620; border_low = 67.0000000; outside_low = 56.5132600; outside_hi = 111.3889620; sigma = 1; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=163927 Gdiag_no=-1 vno start=0, stop=163927 Replacing 255s with 0s #SI# sigma=1 had to be increased for 254 vertices, nripped=6690 mean border=83.0, 160 (55) missing vertices, mean dist -0.2 [0.3 (%73.5)->0.3 (%26.5))] %86 local maxima, % 9 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0511 min Finding expansion regions mean absolute distance = 0.31 +- 0.48 3496 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=farad, 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=898649.5, rms=4.518 017: dt: 0.5000, sse=541962.6, rms=3.006 (33.459%) rms = 3.3205/3.0063, sse=601432.2/541962.6, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 018: dt: 0.2500, sse=409561.5, rms=2.197 (26.916%) 019: dt: 0.2500, sse=355802.1, rms=1.771 (19.388%) 020: dt: 0.2500, sse=341365.8, rms=1.602 (9.524%) 021: dt: 0.2500, sse=332288.6, rms=1.540 (3.873%) rms = 1.5058/1.5404, sse=329432.0/332288.6, time step reduction 2 of 3 to 0.125 0 0 1 022: dt: 0.2500, sse=329432.0, rms=1.506 (2.244%) 023: dt: 0.1250, sse=324859.2, rms=1.451 (3.671%) rms = 1.4407/1.4505, sse=323466.9/324859.2, time step reduction 3 of 3 to 0.062 0 0 1 024: dt: 0.1250, sse=323466.9, rms=1.441 (0.680%) 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=6690 removing 4 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 2 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 4 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6742, nmarked2=103, nripped=6742 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 = 111.3889620; border_low = 67.0000000; outside_low = 56.5132600; outside_hi = 111.3889620; 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=163927 Gdiag_no=-1 vno start=0, stop=163927 Replacing 255s with 0s #SI# sigma=0.5 had to be increased for 255 vertices, nripped=6742 mean border=84.8, 202 (43) missing vertices, mean dist -0.1 [0.2 (%66.3)->0.2 (%33.7))] %90 local maxima, % 5 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0261 min Finding expansion regions mean absolute distance = 0.24 +- 0.37 3782 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=farad, 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=492143.6, rms=2.739 rms = 2.7708/2.7392, sse=501895.3/492143.5, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 025: dt: 0.2500, sse=378329.0, rms=1.969 (28.113%) 026: dt: 0.2500, sse=323759.6, rms=1.445 (26.610%) 027: dt: 0.2500, sse=315222.3, rms=1.351 (6.517%) rms = 1.3298/1.3510, sse=313221.9/315222.3, time step reduction 2 of 3 to 0.125 0 0 1 028: dt: 0.2500, sse=313221.9, rms=1.330 (1.572%) 029: dt: 0.1250, sse=308281.3, rms=1.273 (4.235%) rms = 1.2690/1.2734, sse=308011.1/308281.3, time step reduction 3 of 3 to 0.062 0 0 1 030: dt: 0.1250, sse=308011.1, rms=1.269 (0.352%) maximum number of reductions reached, breaking from loop positioning took 0.5 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=6742 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 removing 3 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 2 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6805, nmarked2=103, nripped=6805 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 = 111.3889620; border_low = 67.0000000; outside_low = 56.5132600; outside_hi = 111.3889620; 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=163927 Gdiag_no=-1 vno start=0, stop=163927 Replacing 255s with 0s #SI# sigma=0.25 had to be increased for 314 vertices, nripped=6805 mean border=85.3, 237 (38) missing vertices, mean dist -0.0 [0.2 (%54.3)->0.2 (%45.7))] %92 local maxima, % 4 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0166 min Finding expansion regions mean absolute distance = 0.22 +- 0.32 3879 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=farad, 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=329589.8, rms=1.527 rms = 1.6824/1.5271, sse=349654.7/329589.8, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 031: dt: 0.2500, sse=297464.2, rms=1.139 (25.446%) 032: dt: 0.2500, sse=288920.1, rms=0.962 (15.533%) rms = 0.9767/0.9617, sse=285437.5/288920.1, time step reduction 2 of 3 to 0.125 0 0 1 RMS increased, rejecting step rms = 0.9562/0.9617, sse=285691.4/288920.1, time step reduction 3 of 3 to 0.062 0 0 1 033: dt: 0.1250, sse=285691.4, rms=0.956 (0.569%) maximum number of reductions reached, breaking from loop positioning took 0.3 minutes Writing output to ../surf/lh.white.preaparc #ET# mris_place_surface 2.84 minutes #VMPC# mris_place_surfaces VmPeak 2771736 mris_place_surface done @#@FSTIME 2022:01:12:19:27:20 mris_place_surface N 18 e 176.94 S 0.72 U 352.04 P 199% M 2126532 F 2 R 544300 W 0 c 19120 w 1135 I 5504 O 11528 L 1.55 5.19 7.22 @#@FSLOADPOST 2022:01:12:19:30:17 mris_place_surface N 18 2.33 3.97 6.40 #-------------------------------------------- #@# WhitePreAparc rh Wed Jan 12 19:30:17 CST 2022 cd /home/valia/mmvt_root/subjects/UMNC03/mri mris_place_surface --adgws-in ../surf/autodet.gw.stats.rh.dat --wm wm.mgz --threads 4 --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 /home/valia/mmvt_root/subjects/UMNC03/mri setenv SUBJECTS_DIR /home/valia/mmvt_root/subjects mris_place_surface --adgws-in ../surf/autodet.gw.stats.rh.dat --wm wm.mgz --threads 4 --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: 68 intersecting step 1 with no progress (num=72, old_num=68) 001: 72 intersecting 002: 68 intersecting 003: 66 intersecting 004: 58 intersecting 005: 48 intersecting step 1 with no progress (num=50, old_num=48) 006: 50 intersecting step 2 with no progress (num=51, old_num=50) 007: 51 intersecting 008: 48 intersecting 009: 40 intersecting step 1 with no progress (num=48, old_num=40) 010: 48 intersecting step 2 with no progress (num=52, old_num=48) 011: 52 intersecting 012: 44 intersecting step 1 with no progress (num=45, old_num=44) 013: 45 intersecting 014: 44 intersecting step 1 with no progress (num=46, old_num=44) 015: 46 intersecting step 2 with no progress (num=48, old_num=46) 016: 48 intersecting step 3 with no progress (num=49, old_num=48) 017: 49 intersecting 018: 46 intersecting 019: 41 intersecting step 1 with no progress (num=49, old_num=41) 020: 49 intersecting 021: 47 intersecting 022: 43 intersecting step 1 with no progress (num=44, old_num=43) 023: 44 intersecting step 2 with no progress (num=49, old_num=44) 024: 49 intersecting 025: 43 intersecting step 1 with no progress (num=60, old_num=43) 026: 60 intersecting 027: 44 intersecting step 1 with no progress (num=61, old_num=44) 028: 61 intersecting step 2 with no progress (num=67, old_num=61) 029: 67 intersecting 030: 42 intersecting 031: 32 intersecting step 1 with no progress (num=43, old_num=32) 032: 43 intersecting 033: 36 intersecting step 1 with no progress (num=43, old_num=36) 034: 43 intersecting step 2 with no progress (num=64, old_num=43) 035: 64 intersecting step 3 with no progress (num=68, old_num=64) 036: 68 intersecting 037: 60 intersecting 038: 55 intersecting 039: 45 intersecting step 1 with no progress (num=59, old_num=45) 040: 59 intersecting 041: 48 intersecting 042: 39 intersecting 043: 38 intersecting step 1 with no progress (num=40, old_num=38) 044: 40 intersecting step 2 with no progress (num=61, old_num=40) 045: 61 intersecting step 3 with no progress (num=64, old_num=61) 046: 64 intersecting 047: 56 intersecting 048: 38 intersecting 049: 37 intersecting step 1 with no progress (num=47, old_num=37) 050: 47 intersecting 051: 42 intersecting 052: 31 intersecting step 1 with no progress (num=40, old_num=31) 053: 40 intersecting 054: 37 intersecting step 1 with no progress (num=38, old_num=37) 055: 38 intersecting step 2 with no progress (num=40, old_num=38) 056: 40 intersecting step 3 with no progress (num=40, old_num=40) 057: 40 intersecting 058: 34 intersecting step 1 with no progress (num=45, old_num=34) 059: 45 intersecting 060: 38 intersecting step 1 with no progress (num=43, old_num=38) 061: 43 intersecting 062: 35 intersecting step 1 with no progress (num=39, old_num=35) 063: 39 intersecting 064: 32 intersecting step 1 with no progress (num=49, old_num=32) 065: 49 intersecting 066: 44 intersecting 067: 36 intersecting step 1 with no progress (num=36, old_num=36) 068: 36 intersecting step 2 with no progress (num=37, old_num=36) 069: 37 intersecting step 3 with no progress (num=38, old_num=37) 070: 38 intersecting 071: 31 intersecting step 1 with no progress (num=35, old_num=31) 072: 35 intersecting step 2 with no progress (num=35, old_num=35) 073: 35 intersecting step 3 with no progress (num=36, old_num=35) 074: 36 intersecting step 4 with no progress (num=39, old_num=36) 075: 39 intersecting step 5 with no progress (num=40, old_num=39) 076: 40 intersecting step 6 with no progress (num=45, old_num=40) 077: 45 intersecting 078: 41 intersecting step 1 with no progress (num=41, old_num=41) 079: 41 intersecting step 2 with no progress (num=41, old_num=41) 080: 41 intersecting step 3 with no progress (num=41, old_num=41) 081: 41 intersecting step 4 with no progress (num=41, old_num=41) 082: 41 intersecting step 5 with no progress (num=41, old_num=41) 083: 41 intersecting step 6 with no progress (num=41, old_num=41) 084: 41 intersecting step 7 with no progress (num=41, old_num=41) 085: 41 intersecting step 8 with no progress (num=41, old_num=41) 086: 41 intersecting step 9 with no progress (num=41, old_num=41) 087: 41 intersecting step 10 with no progress (num=41, old_num=41) 088: 41 intersecting step 11 with no progress (num=41, old_num=41) 089: 41 intersecting step 12 with no progress (num=41, old_num=41) 090: 41 intersecting step 13 with no progress (num=41, old_num=41) 091: 41 intersecting step 14 with no progress (num=41, old_num=41) 092: 41 intersecting step 15 with no progress (num=41, old_num=41) 093: 41 intersecting step 16 with no progress (num=41, old_num=41) terminating search with 31 intersecting Area 337576 0.26408 0.06588 0.000000 0.6113 Corner 1012728 60.00000 9.95689 0.000791 179.9982 Edge 506364 0.78429 0.12048 0.000911 1.5437 Hinge 506364 6.75014 7.18096 0.000013 178.9229 Not reading in aparc Reading in input volume brain.finalsurfs.mgz Reading in wm volume wm.mgz MRIclipBrightWM(): nthresh=30022, wmmin=5, clip=110 MRIfindBrightNonWM(): 3170 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 3 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=6889, nmarked2=142, nripped=6889 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 MRISripSegs(): -2 2 0.5 ripped 0 vertex 84395: xyz = (9.33109,5.772,43.6705) oxyz = (9.33109,5.772,43.6705) wxzy = (9.33109,5.772,43.6705) pxyz = (0,0,0) CBVO Creating mask 168790 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=6889 removing 3 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=6889, nmarked2=142, nripped=6889 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 = 111.3889620; border_low = 67.0000000; outside_low = 56.5132600; outside_hi = 111.3889620; sigma = 2; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=168790 Gdiag_no=-1 vno start=0, stop=168790 Replacing 255s with 0s #SI# sigma=2 had to be increased for 362 vertices, nripped=6889 mean border=80.1, 69 (69) missing vertices, mean dist 0.3 [0.5 (%36.4)->0.8 (%63.6))] %81 local maxima, %15 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0748 min Finding expansion regions mean absolute distance = 0.67 +- 0.79 5610 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=farad, 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=6049337.5, rms=10.802 001: dt: 0.5000, sse=4760438.0, rms=7.957 (26.340%) 002: dt: 0.5000, sse=3824371.0, rms=6.354 (20.142%) 003: dt: 0.5000, sse=3364156.2, rms=5.376 (15.396%) 004: dt: 0.5000, sse=2234486.0, rms=4.715 (12.291%) rms = 4.2780/4.7152, sse=2353876.7/2234486.1, time step reduction 1 of 3 to 0.250 0 1 0 005: dt: 0.5000, sse=2353876.8, rms=4.278 (9.272%) 006: dt: 0.2500, sse=2255216.2, rms=3.057 (28.550%) 007: dt: 0.2500, sse=1677075.1, rms=2.643 (13.529%) rms = 2.4452/2.6431, sse=2526430.0/1677075.2, time step reduction 2 of 3 to 0.125 0 1 0 008: dt: 0.2500, sse=2526430.0, rms=2.445 (7.487%) 009: dt: 0.1250, sse=1757756.4, rms=2.304 (5.792%) rms = 2.2401/2.3036, sse=2531558.6/1757756.3, time step reduction 3 of 3 to 0.062 0 1 0 010: dt: 0.1250, sse=2531558.5, rms=2.240 (2.753%) maximum number of reductions reached, breaking from loop positioning took 0.7 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=6889 removing 2 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 3 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 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 #FML# MRISripMidline(): nmarked=7153, nmarked2=150, nripped=7153 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 = 111.3889620; border_low = 67.0000000; outside_low = 56.5132600; outside_hi = 111.3889620; sigma = 1; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=168790 Gdiag_no=-1 vno start=0, stop=168790 Replacing 255s with 0s #SI# sigma=1 had to be increased for 200 vertices, nripped=7153 mean border=83.2, 124 (16) missing vertices, mean dist -0.1 [0.3 (%73.1)->0.3 (%26.9))] %86 local maxima, % 9 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0498 min Finding expansion regions mean absolute distance = 0.30 +- 0.48 3595 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=farad, 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=3080696.2, rms=4.489 011: dt: 0.5000, sse=2474119.0, rms=3.143 (29.980%) rms = 3.4573/3.1432, sse=2779060.8/2474119.0, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step rms = 2.3342/3.1432, sse=2605844.0/2474119.0, time step reduction 2 of 3 to 0.125 0 1 0 012: dt: 0.2500, sse=2605844.0, rms=2.334 (25.736%) 013: dt: 0.1250, sse=2557399.0, rms=1.958 (16.114%) 014: dt: 0.1250, sse=2540632.5, rms=1.812 (7.447%) 015: dt: 0.1250, sse=2242202.8, rms=1.744 (3.772%) rms = 1.7056/1.7439, sse=2532789.0/2242202.7, time step reduction 3 of 3 to 0.062 0 1 1 016: dt: 0.1250, sse=2532789.0, rms=1.706 (2.197%) maximum number of reductions reached, breaking from loop positioning took 0.5 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=7153 removing 2 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 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 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 4 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=7213, nmarked2=151, nripped=7213 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 = 111.3889620; border_low = 67.0000000; outside_low = 56.5132600; outside_hi = 111.3889620; 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=168790 Gdiag_no=-1 vno start=0, stop=168790 Replacing 255s with 0s #SI# sigma=0.5 had to be increased for 269 vertices, nripped=7213 mean border=84.8, 136 (12) missing vertices, mean dist -0.1 [0.2 (%64.6)->0.2 (%35.4))] %90 local maxima, % 6 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0265 min Finding expansion regions mean absolute distance = 0.24 +- 0.38 4422 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=farad, 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=2693864.0, rms=2.815 rms = 2.8205/2.8145, sse=2694220.6/2693863.9, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 017: dt: 0.2500, sse=2583561.8, rms=2.121 (24.631%) 018: dt: 0.2500, sse=2525109.5, rms=1.630 (23.162%) 019: dt: 0.2500, sse=2224910.0, rms=1.533 (5.940%) rms = 1.4991/1.5331, sse=2518050.9/2224910.1, time step reduction 2 of 3 to 0.125 0 1 1 020: dt: 0.2500, sse=2518051.0, rms=1.499 (2.219%) 021: dt: 0.1250, sse=2511806.8, rms=1.441 (3.904%) rms = 1.4302/1.4406, sse=2510742.9/2511806.9, time step reduction 3 of 3 to 0.062 0 0 1 022: dt: 0.1250, sse=2510743.0, rms=1.430 (0.719%) maximum number of reductions reached, breaking from loop positioning took 0.5 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=7213 removing 2 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 removing 3 vertices from ripped group in thread:0 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 removing 3 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 #FML# MRISripMidline(): nmarked=7229, nmarked2=150, nripped=7229 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 = 111.3889620; border_low = 67.0000000; outside_low = 56.5132600; outside_hi = 111.3889620; 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=168790 Gdiag_no=-1 vno start=0, stop=168790 Replacing 255s with 0s #SI# sigma=0.25 had to be increased for 321 vertices, nripped=7229 mean border=85.3, 198 (11) missing vertices, mean dist -0.0 [0.2 (%54.0)->0.2 (%46.0))] %91 local maxima, % 4 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0165 min Finding expansion regions mean absolute distance = 0.23 +- 0.34 4483 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=farad, 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=2532354.8, rms=1.650 rms = 1.7622/1.6503, sse=2541581.1/2532354.7, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 023: dt: 0.2500, sse=2494325.5, rms=1.270 (23.066%) 024: dt: 0.2500, sse=1918434.2, rms=1.080 (14.934%) rms = 1.0833/1.0800, sse=1749212.2/1918434.3, time step reduction 2 of 3 to 0.125 0 0 1 RMS increased, rejecting step rms = 1.0718/1.0800, sse=1706368.7/1918434.3, time step reduction 3 of 3 to 0.062 0 0 1 025: dt: 0.1250, sse=1706368.8, rms=1.072 (0.758%) maximum number of reductions reached, breaking from loop positioning took 0.3 minutes removing intersecting faces 000: 27 intersecting step 1 with no progress (num=40, old_num=27) 001: 40 intersecting step 2 with no progress (num=56, old_num=40) 002: 56 intersecting 003: 54 intersecting 004: 50 intersecting 005: 48 intersecting step 1 with no progress (num=49, old_num=48) 006: 49 intersecting 007: 39 intersecting 008: 36 intersecting step 1 with no progress (num=36, old_num=36) 009: 36 intersecting step 2 with no progress (num=36, old_num=36) 010: 36 intersecting step 3 with no progress (num=36, old_num=36) 011: 36 intersecting step 4 with no progress (num=36, old_num=36) 012: 36 intersecting step 5 with no progress (num=36, old_num=36) 013: 36 intersecting step 6 with no progress (num=36, old_num=36) 014: 36 intersecting step 7 with no progress (num=36, old_num=36) 015: 36 intersecting step 8 with no progress (num=36, old_num=36) 016: 36 intersecting step 9 with no progress (num=36, old_num=36) 017: 36 intersecting step 10 with no progress (num=36, old_num=36) 018: 36 intersecting step 11 with no progress (num=36, old_num=36) 019: 36 intersecting step 12 with no progress (num=36, old_num=36) 020: 36 intersecting step 13 with no progress (num=36, old_num=36) 021: 36 intersecting step 14 with no progress (num=36, old_num=36) 022: 36 intersecting step 15 with no progress (num=36, old_num=36) 023: 36 intersecting step 16 with no progress (num=36, old_num=36) terminating search with 27 intersecting Writing output to ../surf/rh.white.preaparc #ET# mris_place_surface 3.00 minutes #VMPC# mris_place_surfaces VmPeak 3025824 mris_place_surface done @#@FSTIME 2022:01:12:19:30:17 mris_place_surface N 18 e 348.61 S 0.78 U 496.14 P 142% M 2382492 F 1 R 542941 W 0 c 18148 w 1031 I 16 O 11872 L 2.33 3.97 6.40 @#@FSLOADPOST 2022:01:12:19:36:06 mris_place_surface N 18 1.51 2.41 4.91 #-------------------------------------------- #@# CortexLabel lh Wed Jan 12 19:36:06 CST 2022 cd /home/valia/mmvt_root/subjects/UMNC03/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 4 non-cortical segments detected only using segment with 8285 vertices erasing segment 1 (vno[0] = 94900) erasing segment 2 (vno[0] = 98759) erasing segment 3 (vno[0] = 153377) @#@FSTIME 2022:01:12:19:36:06 mri_label2label N 5 e 17.09 S 0.07 U 18.15 P 106% M 350396 F 2 R 36556 W 0 c 488 w 53 I 4808 O 13576 L 1.51 2.41 4.91 @#@FSLOADPOST 2022:01:12:19:36:23 mri_label2label N 5 1.44 2.34 4.83 #-------------------------------------------- #@# CortexLabel+HipAmyg lh Wed Jan 12 19:36:23 CST 2022 cd /home/valia/mmvt_root/subjects/UMNC03/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 11 non-cortical segments detected only using segment with 5892 vertices erasing segment 1 (vno[0] = 52485) erasing segment 2 (vno[0] = 56819) erasing segment 3 (vno[0] = 57943) erasing segment 4 (vno[0] = 68088) erasing segment 5 (vno[0] = 93041) erasing segment 6 (vno[0] = 94900) erasing segment 7 (vno[0] = 98759) erasing segment 8 (vno[0] = 134387) erasing segment 9 (vno[0] = 135117) erasing segment 10 (vno[0] = 153431) @#@FSTIME 2022:01:12:19:36:23 mri_label2label N 5 e 17.15 S 0.08 U 18.21 P 106% M 390592 F 0 R 43055 W 0 c 644 w 46 I 0 O 13768 L 1.44 2.34 4.83 @#@FSLOADPOST 2022:01:12:19:36:40 mri_label2label N 5 1.34 2.27 4.77 #-------------------------------------------- #@# CortexLabel rh Wed Jan 12 19:36:40 CST 2022 cd /home/valia/mmvt_root/subjects/UMNC03/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 9 non-cortical segments detected only using segment with 8396 vertices erasing segment 1 (vno[0] = 59201) erasing segment 2 (vno[0] = 68118) erasing segment 3 (vno[0] = 72621) erasing segment 4 (vno[0] = 73736) erasing segment 5 (vno[0] = 89191) erasing segment 6 (vno[0] = 92969) erasing segment 7 (vno[0] = 102204) erasing segment 8 (vno[0] = 114886) @#@FSTIME 2022:01:12:19:36:40 mri_label2label N 5 e 18.94 S 0.09 U 20.00 P 106% M 386112 F 0 R 45089 W 0 c 696 w 61 I 0 O 13664 L 1.34 2.27 4.77 @#@FSLOADPOST 2022:01:12:19:36:59 mri_label2label N 5 1.24 2.19 4.68 #-------------------------------------------- #@# CortexLabel+HipAmyg rh Wed Jan 12 19:36:59 CST 2022 cd /home/valia/mmvt_root/subjects/UMNC03/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 5571 vertices erasing segment 1 (vno[0] = 50659) erasing segment 2 (vno[0] = 51710) erasing segment 3 (vno[0] = 58541) erasing segment 4 (vno[0] = 59201) erasing segment 5 (vno[0] = 60641) erasing segment 6 (vno[0] = 61652) erasing segment 7 (vno[0] = 65113) erasing segment 8 (vno[0] = 68118) erasing segment 9 (vno[0] = 70785) erasing segment 10 (vno[0] = 72621) erasing segment 11 (vno[0] = 73736) erasing segment 12 (vno[0] = 89191) erasing segment 13 (vno[0] = 92347) erasing segment 14 (vno[0] = 92969) erasing segment 15 (vno[0] = 94217) erasing segment 16 (vno[0] = 102204) erasing segment 17 (vno[0] = 114886) @#@FSTIME 2022:01:12:19:36:59 mri_label2label N 5 e 19.10 S 0.11 U 20.12 P 105% M 439576 F 0 R 62571 W 0 c 988 w 64 I 0 O 13912 L 1.24 2.19 4.68 @#@FSLOADPOST 2022:01:12:19:37:18 mri_label2label N 5 1.17 2.11 4.61 #-------------------------------------------- #@# Smooth2 lh Wed Jan 12 19:37:18 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mris_smooth -n 3 -nw -seed 1234 ../surf/lh.white.preaparc ../surf/lh.smoothwm #-------------------------------------------- #@# Smooth2 rh Wed Jan 12 19:37:18 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mris_smooth -n 3 -nw -seed 1234 ../surf/rh.white.preaparc ../surf/rh.smoothwm Waiting for PID 3045149 of (3045149 3045152) to complete... Waiting for PID 3045152 of (3045149 3045152) to complete... 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... 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... PIDs (3045149 3045152) completed and logs appended. #-------------------------------------------- #@# Inflation2 lh Wed Jan 12 19:37:22 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mris_inflate ../surf/lh.smoothwm ../surf/lh.inflated #-------------------------------------------- #@# Inflation2 rh Wed Jan 12 19:37:22 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/scripts mris_inflate ../surf/rh.smoothwm ../surf/rh.inflated Waiting for PID 3045199 of (3045199 3045202) to complete... Waiting for PID 3045202 of (3045199 3045202) to complete... mris_inflate ../surf/lh.smoothwm ../surf/lh.inflated Reading ../surf/lh.smoothwm avg radius = 49.8 mm, total surface area = 103703 mm^2 step 000: RMS=0.181 (target=0.015) step 005: RMS=0.120 (target=0.015) step 010: RMS=0.092 (target=0.015) step 015: RMS=0.078 (target=0.015) step 020: RMS=0.063 (target=0.015) step 025: RMS=0.053 (target=0.015) step 030: RMS=0.044 (target=0.015) step 035: RMS=0.037 (target=0.015) step 040: RMS=0.030 (target=0.015) step 045: RMS=0.027 (target=0.015) step 050: RMS=0.024 (target=0.015) step 055: RMS=0.022 (target=0.015) step 060: RMS=0.021 (target=0.015) writing inflated surface to ../surf/lh.inflated writing sulcal depths to ../surf/lh.sulc inflation complete. inflation took 0.6 minutes mris_inflate utimesec 70.196466 mris_inflate stimesec 0.118464 mris_inflate ru_maxrss 253680 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 37735 mris_inflate ru_majflt 0 mris_inflate ru_nswap 0 mris_inflate ru_inblock 0 mris_inflate ru_oublock 12832 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 1241 mris_inflate ru_nivcsw 6087 mris_inflate ../surf/rh.smoothwm ../surf/rh.inflated Reading ../surf/rh.smoothwm avg radius = 49.7 mm, total surface area = 104306 mm^2 step 000: RMS=0.181 (target=0.015) step 005: RMS=0.119 (target=0.015) step 010: RMS=0.092 (target=0.015) step 015: RMS=0.078 (target=0.015) step 020: RMS=0.064 (target=0.015) step 025: RMS=0.053 (target=0.015) step 030: RMS=0.045 (target=0.015) step 035: RMS=0.038 (target=0.015) step 040: RMS=0.033 (target=0.015) step 045: RMS=0.029 (target=0.015) step 050: RMS=0.026 (target=0.015) step 055: RMS=0.025 (target=0.015) step 060: RMS=0.024 (target=0.015) writing inflated surface to ../surf/rh.inflated writing sulcal depths to ../surf/rh.sulc inflation complete. inflation took 0.6 minutes mris_inflate utimesec 70.456939 mris_inflate stimesec 0.805463 mris_inflate ru_maxrss 259956 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 497553 mris_inflate ru_majflt 0 mris_inflate ru_nswap 0 mris_inflate ru_inblock 0 mris_inflate ru_oublock 13200 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 1070 mris_inflate ru_nivcsw 6471 PIDs (3045199 3045202) completed and logs appended. #-------------------------------------------- #@# Curv .H and .K lh Wed Jan 12 19:38:01 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/surf mris_curvature -w -seed 1234 lh.white.preaparc 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 #-------------------------------------------- #@# Curv .H and .K rh Wed Jan 12 19:38:01 CST 2022 /home/valia/mmvt_root/subjects/UMNC03/surf mris_curvature -w -seed 1234 rh.white.preaparc rm -f rh.white.H ln -s rh.white.preaparc.H rh.white.H rm -f rh.white.K ln -s rh.white.preaparc.K rh.white.K mris_curvature -seed 1234 -thresh .999 -n -a 5 -w -distances 10 10 rh.inflated cd /home/valia/mmvt_root/subjects/UMNC03/surf reconbatchjobs /home/valia/mmvt_root/subjects/UMNC03/scripts/recon-all.log mris_curvature_white_lh.cmd rm_curvature_white_lh.H.cmd ln_curvature_white_lh.H.cmd rm_curvature_white_lh.K.cmd ln_curvature_white_lh.K.cmd mris_curvature_inflated_lh.cmd mris_curvature_white_rh.cmd rm_curvature_white_rh.H.cmd ln_curvature_white_rh.H.cmd rm_curvature_white_rh.K.cmd ln_curvature_white_rh.K.cmd mris_curvature_inflated_rh.cmd Waiting for PID 3045306 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... Waiting for PID 3045309 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... Waiting for PID 3045312 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... Waiting for PID 3045315 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... Waiting for PID 3045318 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... Waiting for PID 3045321 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... Waiting for PID 3045324 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... Waiting for PID 3045327 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... Waiting for PID 3045330 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... Waiting for PID 3045333 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... Waiting for PID 3045336 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... Waiting for PID 3045339 of (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) to complete... mris_curvature -w -seed 1234 lh.white.preaparc setting seed for random number generator to 1234 total integrated curvature = 16.997*4pi (213.594) --> -16 handles ICI = 228.1, FI = 2053.5, variation=33313.306 writing Gaussian curvature to ./lh.white.preaparc.K...done. writing mean curvature to ./lh.white.preaparc.H...done. 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 219 vertices thresholded to be in k1 ~ [-0.45 0.63], k2 ~ [-0.23 0.09] total integrated curvature = 0.403*4pi (5.059) --> 1 handles ICI = 1.5, FI = 13.1, variation=210.880 186 vertices thresholded to be in [-0.02 0.05] writing Gaussian curvature to ./lh.inflated.K...thresholding curvature at 99.90% level curvature mean = 0.000, std = 0.002 174 vertices thresholded to be in [-0.27 0.19] done. writing mean curvature to ./lh.inflated.H...curvature mean = -0.016, std = 0.025 done. mris_curvature -w -seed 1234 rh.white.preaparc setting seed for random number generator to 1234 total integrated curvature = 58.282*4pi (732.388) --> -57 handles ICI = 276.8, FI = 2247.7, variation=36723.484 writing Gaussian curvature to ./rh.white.preaparc.K...done. writing mean curvature to ./rh.white.preaparc.H...done. rm -f rh.white.H ln -s rh.white.preaparc.H rh.white.H rm -f rh.white.K ln -s rh.white.preaparc.K rh.white.K mris_curvature -seed 1234 -thresh .999 -n -a 5 -w -distances 10 10 rh.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 298 vertices thresholded to be in k1 ~ [-0.90 0.30], k2 ~ [-0.20 0.15] total integrated curvature = 0.231*4pi (2.902) --> 1 handles ICI = 1.5, FI = 13.0, variation=211.170 191 vertices thresholded to be in [-0.04 0.09] writing Gaussian curvature to ./rh.inflated.K...thresholding curvature at 99.90% level curvature mean = 0.000, std = 0.002 165 vertices thresholded to be in [-0.36 0.14] done. writing mean curvature to ./rh.inflated.H...curvature mean = -0.016, std = 0.026 done. PIDs (3045306 3045309 3045312 3045315 3045318 3045321 3045324 3045327 3045330 3045333 3045336 3045339) completed and logs appended. /home/valia/mmvt_root/subjects/UMNC03/surf Cannot find rh.white.H Linux faraday 4.18.0-326.el8.x86_64 #1 SMP Wed Jul 28 21:21:05 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux recon-all -s UMNC03 exited with ERRORS at Wed Jan 12 19:39:00 CST 2022 To report a problem, see http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting