Wed Jun 14 16:07:23 BST 2023 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2 setenv SUBJECTS_DIR /media/sf_VBOX/T1_FreeSurfer /home/varun/freesurfer/bin/recon-all -s T1_260423_2 -i T1_260423.nii -all -qcache subjid T1_260423_2 setenv SUBJECTS_DIR /media/sf_VBOX/T1_FreeSurfer FREESURFER_HOME /home/varun/freesurfer Actual FREESURFER_HOME /home/varun/freesurfer build-stamp.txt: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b Linux Ubuntu 5.19.0-43-generic #44~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon May 22 13:39:36 UTC 2 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 1017472 kbytes maxproc 31484 maxlocks unlimited maxsignal 31484 maxmessage 819200 maxnice 0 maxrtprio 0 maxrttime unlimited total used free shared buff/cache available Mem: 7.8Gi 3.3Gi 175Mi 131Mi 4.3Gi 4.1Gi Swap: 2.0Gi 529Mi 1.5Gi ######################################## program versions used 7.2.0 (freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b) 7.2.0 ProgramName: lta_convert ProgramArguments: lta_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_and ProgramArguments: mri_and -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_annotation2label ProgramArguments: mri_annotation2label -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_aparc2aseg ProgramArguments: mri_aparc2aseg -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_surf2volseg ProgramArguments: mri_surf2volseg -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_binarize ProgramArguments: mri_binarize -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_ca_label ProgramArguments: mri_ca_label -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_ca_normalize ProgramArguments: mri_ca_normalize -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_ca_register ProgramArguments: mri_ca_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_cc ProgramArguments: mri_cc -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_compute_overlap ProgramArguments: mri_compute_overlap -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_compute_seg_overlap ProgramArguments: mri_compute_seg_overlap -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_concat ProgramArguments: mri_concat -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_concatenate_lta ProgramArguments: mri_concatenate_lta -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 mri_convert -all-info ProgramName: mri_convert ProgramArguments: mri_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_diff ProgramArguments: mri_diff -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_edit_wm_with_aseg ProgramArguments: mri_edit_wm_with_aseg -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_em_register ProgramArguments: mri_em_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_fill ProgramArguments: mri_fill -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:23-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_fuse_segmentations ProgramArguments: mri_fuse_segmentations -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_fwhm ProgramArguments: mri_fwhm -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_gcut ProgramArguments: mri_gcut -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_info ProgramArguments: mri_info -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_label2label ProgramArguments: mri_label2label -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_label2vol ProgramArguments: mri_label2vol -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_log_likelihood ProgramArguments: mri_log_likelihood -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_mask ProgramArguments: mri_mask -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_matrix_multiply ProgramArguments: mri_matrix_multiply -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_normalize ProgramArguments: mri_normalize -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_normalize_tp2 ProgramArguments: mri_normalize_tp2 -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_or ProgramArguments: mri_or -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_relabel_hypointensities ProgramArguments: mri_relabel_hypointensities -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_relabel_nonwm_hypos ProgramArguments: mri_relabel_nonwm_hypos -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_remove_neck ProgramArguments: mri_remove_neck -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 7.2.0 ProgramName: mri_robust_register ProgramArguments: mri_robust_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 7.2.0 ProgramName: mri_robust_template ProgramArguments: mri_robust_template -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_anatomical_stats ProgramArguments: mris_anatomical_stats -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_ca_label ProgramArguments: mris_ca_label -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_calc ProgramArguments: mris_calc -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_convert ProgramArguments: mris_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_curvature ProgramArguments: mris_curvature -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_curvature_stats ProgramArguments: mris_curvature_stats -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_diff ProgramArguments: mris_diff -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_divide_parcellation ProgramArguments: mris_divide_parcellation -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_segment ProgramArguments: mri_segment -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_segstats ProgramArguments: mri_segstats -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_euler_number ProgramArguments: mris_euler_number -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_fix_topology ProgramArguments: mris_fix_topology -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_topo_fixer ProgramArguments: mris_topo_fixer -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_jacobian ProgramArguments: mris_jacobian -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_label2annot ProgramArguments: mris_label2annot -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_left_right_register ProgramArguments: mris_left_right_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_place_surface ProgramArguments: mris_place_surface -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mrisp_paint ProgramArguments: mrisp_paint -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_register ProgramArguments: mris_register -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_smooth ProgramArguments: mris_smooth -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_sphere ProgramArguments: mris_sphere -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_surface_stats ProgramArguments: mris_surface_stats -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_stats2seg ProgramArguments: mri_stats2seg -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_thickness ProgramArguments: mris_thickness -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_thickness_diff ProgramArguments: mris_thickness_diff -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_topo_fixer ProgramArguments: mris_topo_fixer -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_surf2surf ProgramArguments: mri_surf2surf -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_surf2vol ProgramArguments: mri_surf2vol -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_surfcluster ProgramArguments: mri_surfcluster -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mris_volmask ProgramArguments: mris_volmask -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_tessellate ProgramArguments: mri_tessellate -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_vol2surf ProgramArguments: mri_vol2surf -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_vol2vol ProgramArguments: mri_vol2vol -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_voldiff ProgramArguments: mri_voldiff -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: mri_watershed ProgramArguments: mri_watershed -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 ProgramName: tkregister2 ProgramArguments: tkregister2_cmdl -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 mri_motion_correct.fsl 7.2.0 mri_convert -all-info ProgramName: mri_convert ProgramArguments: mri_convert -all-info ProgramVersion: 7.2.0 TimeStamp: 2023/06/14-15:07:24-GMT BuildTime: Jul 21 2021 02:56:14 BuildStamp: freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b User: varun Machine: Ubuntu Platform: Linux PlatformVersion: 5.19.0-43-generic CompilerName: GCC CompilerVersion: 4.8.5 Program nu_correct, built from: Package MNI N3, version 1.12.0, compiled by nicks@terrier (x86_64-unknown-linux-gnu) on 2015-06-19 at 01:25:34 ####################################### GCADIR /home/varun/freesurfer/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 /home/varun/freesurfer/average GCS DKaparc.atlas.acfb40.noaparc.i12.2016-08-02.gcs ####################################### /media/sf_VBOX/T1_FreeSurfer/T1_260423_2 mri_convert /media/sf_VBOX/T1_FreeSurfer/T1_260423.nii /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/orig/001.mgz mri_convert /media/sf_VBOX/T1_FreeSurfer/T1_260423.nii /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/orig/001.mgz reading from /media/sf_VBOX/T1_FreeSurfer/T1_260423.nii... TR=8.40, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-0.997951, -0.0348039, -0.0536819) j_ras = (-0.0504997, 0.943685, 0.326968) k_ras = (-0.039279, -0.32901, 0.943509) writing to /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/orig/001.mgz... @#@FSTIME 2023:06:14:16:07:24 mri_convert N 2 e 2.22 S 0.30 U 1.43 P 78% M 28312 F 0 R 5714 W 0 c 62 w 3309 I 0 O 0 L 0.47 0.48 0.33 @#@FSLOADPOST 2023:06:14:16:07:26 mri_convert N 2 0.47 0.48 0.33 #-------------------------------------------- #@# MotionCor Wed Jun 14 16:07:27 BST 2023 Found 1 runs /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/orig/001.mgz /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/rawavg.mgz /media/sf_VBOX/T1_FreeSurfer/T1_260423_2 mri_convert /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/rawavg.mgz /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/orig.mgz --conform mri_convert /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/rawavg.mgz /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/orig.mgz --conform reading from /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/rawavg.mgz... TR=8.40, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-0.997951, -0.0348039, -0.0536819) j_ras = (-0.0504997, 0.943685, 0.326968) k_ras = (-0.039279, -0.32901, 0.943509) changing data type from short to uchar (noscale = 0)... MRIchangeType: Building histogram 0 1933 1000, flo=0, fhi=0.999, dest_type=0 Reslicing using trilinear interpolation writing to /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/orig.mgz... @#@FSTIME 2023:06:14:16:07:31 mri_convert N 3 e 2.77 S 1.98 U 0.64 P 94% M 38952 F 0 R 16640 W 0 c 35 w 1228 I 0 O 0 L 0.43 0.47 0.33 @#@FSLOADPOST 2023:06:14:16:07:33 mri_convert N 3 0.48 0.48 0.33 mri_add_xform_to_header -c /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/transforms/talairach.xfm /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/orig.mgz /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/orig.mgz INFO: extension is mgz @#@FSTIME 2023:06:14:16:07:34 mri_add_xform_to_header N 4 e 0.72 S 0.43 U 0.20 P 87% M 23336 F 0 R 4576 W 0 c 10 w 717 I 0 O 0 L 0.48 0.48 0.33 @#@FSLOADPOST 2023:06:14:16:07:34 mri_add_xform_to_header N 4 0.48 0.48 0.33 #-------------------------------------------- #@# Talairach Wed Jun 14 16:07:34 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri /home/varun/freesurfer/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 Ubuntu 5.19.0-43-generic #44~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon May 22 13:39:36 UTC 2 x86_64 x86_64 x86_64 GNU/Linux Wed Jun 14 16:07:35 BST 2023 tmpdir is ./tmp.mri_nu_correct.mni.24719 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri AntsN4BiasFieldCorrectionFs -i orig.mgz -o ./tmp.mri_nu_correct.mni.24719/nu0.mgz --dtype uchar AntsN4BiasFieldCorrectionFs done mri_convert ./tmp.mri_nu_correct.mni.24719/nu0.mgz orig_nu.mgz --like orig.mgz --conform mri_convert ./tmp.mri_nu_correct.mni.24719/nu0.mgz orig_nu.mgz --like orig.mgz --conform reading from ./tmp.mri_nu_correct.mni.24719/nu0.mgz... TR=8.40, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 1.86265e-09, 0) j_ras = (0, 2.98023e-08, -1) k_ras = (-4.65661e-09, 1, 0) INFO: transform src into the like-volume: orig.mgz writing to orig_nu.mgz... Wed Jun 14 16:10:03 BST 2023 mri_nu_correct.mni done @#@FSTIME 2023:06:14:16:07:34 mri_nu_correct.mni N 12 e 148.57 S 0.89 U 147.23 P 99% M 506872 F 1 R 147984 W 0 c 1448 w 2328 I 0 O 0 L 0.48 0.48 0.33 @#@FSLOADPOST 2023:06:14:16:10:03 mri_nu_correct.mni N 12 1.00 0.70 0.44 talairach_avi --i orig_nu.mgz --xfm transforms/talairach.auto.xfm talairach_avi log file is transforms/talairach_avi.log... Started at Wed Jun 14 16:10:03 BST 2023 Ended at Wed Jun 14 16:10:21 BST 2023 talairach_avi done @#@FSTIME 2023:06:14:16:10:03 talairach_avi N 4 e 18.01 S 0.80 U 10.01 P 60% M 255236 F 30 R 397766 W 0 c 367 w 3791 I 272064 O 32 L 1.00 0.70 0.44 @#@FSLOADPOST 2023:06:14:16:10:21 talairach_avi N 4 1.05 0.73 0.46 cp transforms/talairach.auto.xfm transforms/talairach.xfm lta_convert --src orig.mgz --trg /home/varun/freesurfer/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: /home/varun/freesurfer/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 1.10338 0.00501 0.01081 -0.03055; 0.04011 1.06077 0.29738 2.10856; -0.00668 -0.31153 1.12847 -2.92569; 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 Jun 14 16:10:23 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri talairach_afd -T 0.005 -xfm transforms/talairach.xfm talairach_afd: Talairach Transform: transforms/talairach.xfm OK (p=0.7788, pval=0.8469 >= threshold=0.0050) @#@FSTIME 2023:06:14:16:10:23 talairach_afd N 4 e 0.01 S 0.00 U 0.00 P 41% M 5772 F 14 R 222 W 0 c 0 w 37 I 2504 O 0 L 0.96 0.72 0.45 @#@FSLOADPOST 2023:06:14:16:10:24 talairach_afd N 4 0.96 0.72 0.45 awk -f /home/varun/freesurfer/bin/extract_talairach_avi_QA.awk /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/transforms/talairach_avi.log tal_QC_AZS /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/transforms/talairach_avi.log TalAviQA: 0.95500 z-score: -5 #-------------------------------------------- #@# Nu Intensity Correction Wed Jun 14 16:10:24 BST 2023 mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --n 2 --ants-n4 /usr/bin/bc /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri /home/varun/freesurfer/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 Ubuntu 5.19.0-43-generic #44~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon May 22 13:39:36 UTC 2 x86_64 x86_64 x86_64 GNU/Linux Wed Jun 14 16:10:24 BST 2023 tmpdir is ./tmp.mri_nu_correct.mni.24956 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri AntsN4BiasFieldCorrectionFs -i orig.mgz -o ./tmp.mri_nu_correct.mni.24956/nu0.mgz --dtype uchar AntsN4BiasFieldCorrectionFs done mri_binarize --i ./tmp.mri_nu_correct.mni.24956/nu0.mgz --min -1 --o ./tmp.mri_nu_correct.mni.24956/ones.mgz 7.2.0 cwd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri cmdline mri_binarize --i ./tmp.mri_nu_correct.mni.24956/nu0.mgz --min -1 --o ./tmp.mri_nu_correct.mni.24956/ones.mgz sysname Linux hostname Ubuntu machine x86_64 user varun input ./tmp.mri_nu_correct.mni.24956/nu0.mgz frame 0 nErode3d 0 nErode2d 0 output ./tmp.mri_nu_correct.mni.24956/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.24956/ones.mgz Count: 16777215 16777215.000000 16777216 99.999994 mri_binarize done mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.24956/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.24956/sum.junk --avgwf ./tmp.mri_nu_correct.mni.24956/input.mean.dat 7.2.0 cwd cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.24956/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.24956/sum.junk --avgwf ./tmp.mri_nu_correct.mni.24956/input.mean.dat sysname Linux hostname Ubuntu machine x86_64 user varun whitesurfname white UseRobust 0 Loading ./tmp.mri_nu_correct.mni.24956/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.24956/input.mean.dat mri_segstats done mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.24956/ones.mgz --i ./tmp.mri_nu_correct.mni.24956/nu0.mgz --sum ./tmp.mri_nu_correct.mni.24956/sum.junk --avgwf ./tmp.mri_nu_correct.mni.24956/output.mean.dat 7.2.0 cwd cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.24956/ones.mgz --i ./tmp.mri_nu_correct.mni.24956/nu0.mgz --sum ./tmp.mri_nu_correct.mni.24956/sum.junk --avgwf ./tmp.mri_nu_correct.mni.24956/output.mean.dat sysname Linux hostname Ubuntu machine x86_64 user varun whitesurfname white UseRobust 0 Loading ./tmp.mri_nu_correct.mni.24956/ones.mgz Loading ./tmp.mri_nu_correct.mni.24956/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.24956/output.mean.dat mri_segstats done mris_calc -o ./tmp.mri_nu_correct.mni.24956/nu0.mgz ./tmp.mri_nu_correct.mni.24956/nu0.mgz mul 1.15357901057042582606 Saving result to './tmp.mri_nu_correct.mni.24956/nu0.mgz' (type = MGH ) [ ok ] mri_convert ./tmp.mri_nu_correct.mni.24956/nu0.mgz nu.mgz --like orig.mgz mri_convert ./tmp.mri_nu_correct.mni.24956/nu0.mgz nu.mgz --like orig.mgz reading from ./tmp.mri_nu_correct.mni.24956/nu0.mgz... TR=8.40, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 1.86265e-09, 0) j_ras = (0, 2.98023e-08, -1) k_ras = (-4.65661e-09, 1, 0) INFO: transform src into the like-volume: orig.mgz writing to nu.mgz... mri_make_uchar nu.mgz transforms/talairach.xfm nu.mgz type change took 0 minutes and 4 seconds. mapping ( 3, 201) to ( 3, 110) Wed Jun 14 16:13:10 BST 2023 mri_nu_correct.mni done @#@FSTIME 2023:06:14:16:10:24 mri_nu_correct.mni N 9 e 166.58 S 2.17 U 162.86 P 99% M 614176 F 43 R 514529 W 0 c 2110 w 6574 I 7560 O 0 L 0.96 0.72 0.45 @#@FSLOADPOST 2023:06:14:16:13:10 mri_nu_correct.mni N 9 1.00 0.86 0.56 mri_add_xform_to_header -c /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/transforms/talairach.xfm nu.mgz nu.mgz INFO: extension is mgz @#@FSTIME 2023:06:14:16:13:10 mri_add_xform_to_header N 4 e 0.71 S 0.04 U 0.53 P 81% M 23432 F 2 R 4576 W 0 c 31 w 736 I 512 O 0 L 1.00 0.86 0.56 @#@FSLOADPOST 2023:06:14:16:13:11 mri_add_xform_to_header N 4 1.00 0.86 0.56 #-------------------------------------------- #@# Intensity Normalization Wed Jun 14 16:13:11 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 1.10338 0.00501 0.01081 -0.03055; 0.04011 1.06077 0.29738 2.10856; -0.00668 -0.31153 1.12847 -2.92569; 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 = 20 Starting OpenSpline(): npoints = 20 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Iterating 2 times --------------------------------- 3d normalization pass 1 of 2 white matter peak found at 110 white matter peak found at 110 gm peak at 54 (53), valley at 37 (36) csf peak at 27, setting threshold to 45 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 54 (53), valley at 32 (31) csf peak at 27, setting threshold to 45 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Done iterating --------------------------------- writing output to T1.mgz 3D bias adjustment took 1 minutes and 9 seconds. @#@FSTIME 2023:06:14:16:13:11 mri_normalize N 7 e 69.28 S 13.71 U 55.48 P 99% M 583612 F 8 R 263263 W 0 c 119 w 597 I 1480 O 0 L 1.00 0.86 0.56 @#@FSLOADPOST 2023:06:14:16:14:20 mri_normalize N 7 1.50 1.03 0.64 #-------------------------------------------- #@# Skull Stripping Wed Jun 14 16:14:20 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri mri_em_register -skull nu.mgz /home/varun/freesurfer/average/RB_all_withskull_2020_01_02.gca transforms/talairach_with_skull.lta aligning to atlas containing skull, setting unknown_nbr_spacing = 5 == Number of threads available to mri_em_register for OpenMP = 1 == reading 1 input volumes... logging results to talairach_with_skull.log reading '/home/varun/freesurfer/average/RB_all_withskull_2020_01_02.gca'... GCAread took 0 minutes and 1 seconds. average std = 23.0 using min determinant for regularization = 52.8 0 singular and 9205 ill-conditioned covariance matrices regularized reading 'nu.mgz'... freeing gibbs priors...done. accounting for voxel sizes in initial transform bounding unknown intensity as < 8.9 or > 556.0 total sample mean = 77.3 (1403 zeros) ************************************************ spacing=8, using 3292 sample points, tol=1.00e-05... ************************************************ register_mri: find_optimal_transform find_optimal_transform: nsamples 3292, passno 0, spacing 8 resetting wm mean[0]: 100 --> 108 resetting gm mean[0]: 61 --> 61 input volume #1 is the most T1-like using real data threshold=4.0 skull bounding box = (47, 54, 33) --> (209, 220, 236) finding center of left hemi white matter using (101, 109, 135) as brain centroid of Right_Cerebral_White_Matter... MRImask(): AllowDiffGeom = 1 mean wm in atlas = 108, using box (81,89,110) --> (120, 129,160) to find MRI wm before smoothing, mri peak at 109 robust fit to distribution - 109 +- 3.9 after smoothing, mri peak at 109, scaling input intensities by 0.991 scaling channel 0 by 0.990826 initial log_p = -4.720 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.543988 @ (-10.526, -31.579, -31.579) max log p = -4.284003 @ (5.263, 5.263, 5.263) max log p = -4.248317 @ (2.632, 2.632, -2.632) max log p = -4.235850 @ (1.316, 1.316, 3.947) max log p = -4.229270 @ (-0.658, 0.658, -0.658) max log p = -4.211630 @ (-0.329, 0.329, 0.329) max log p = -4.211630 @ (0.000, 0.000, 0.000) max log p = -4.211630 @ (0.000, 0.000, 0.000) Found translation: (-2.3, -21.4, -25.3): log p = -4.212 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.822, old_max_log_p =-4.212 (thresh=-4.2) 1.06375 0.00000 0.00000 -10.42471; 0.00000 1.11081 0.29764 -72.62590; 0.00000 -0.27532 1.02750 15.92924; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 10 seconds. **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.822, old_max_log_p =-3.822 (thresh=-3.8) 1.06375 0.00000 0.00000 -10.42471; 0.00000 1.11081 0.29764 -72.62590; 0.00000 -0.27532 1.02750 15.92924; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 iteration took 1 minutes and 7 seconds. **************************************** Nine parameter search. iteration 2 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.762, old_max_log_p =-3.822 (thresh=-3.8) 1.08237 0.03700 0.00991 -19.82905; -0.03611 1.15185 0.30864 -74.08561; 0.00000 -0.27016 1.00824 19.58995; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 3 seconds. **************************************** Nine parameter search. iteration 3 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.761, old_max_log_p =-3.762 (thresh=-3.8) 1.08237 0.03700 0.00991 -19.82905; -0.03543 1.13026 0.30285 -70.03194; 0.00000 -0.27016 1.00824 19.58995; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 iteration took 1 minutes and 2 seconds. **************************************** Nine parameter search. iteration 4 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.733, old_max_log_p =-3.761 (thresh=-3.8) 1.08138 0.01429 0.02177 -17.73138; -0.01739 1.13757 0.26971 -68.58839; -0.01864 -0.23451 1.02087 13.79792; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 1 seconds. **************************************** Nine parameter search. iteration 5 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.733, old_max_log_p =-3.733 (thresh=-3.7) 1.08138 0.01429 0.02177 -17.73138; -0.01739 1.13757 0.26971 -68.58839; -0.01864 -0.23451 1.02087 13.79792; 0.00000 0.00000 0.00000 1.00000; min search scale 0.025000 reached *********************************************** Computing MAP estimate using 3292 samples... *********************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-05 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.08138 0.01429 0.02177 -17.73138; -0.01739 1.13757 0.26971 -68.58839; -0.01864 -0.23451 1.02087 13.79792; 0.00000 0.00000 0.00000 1.00000; nsamples 3292 Quasinewton: input matrix 1.08138 0.01429 0.02177 -17.73138; -0.01739 1.13757 0.26971 -68.58839; -0.01864 -0.23451 1.02087 13.79792; 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: 008: -log(p) = -0.0 tol 0.000010 Resulting transform: 1.08138 0.01429 0.02177 -17.73138; -0.01739 1.13757 0.26971 -68.58839; -0.01864 -0.23451 1.02087 13.79792; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -3.733 (old=-4.720) transform before final EM align: 1.08138 0.01429 0.02177 -17.73138; -0.01739 1.13757 0.26971 -68.58839; -0.01864 -0.23451 1.02087 13.79792; 0.00000 0.00000 0.00000 1.00000; ************************************************** EM alignment process ... Computing final MAP estimate using 364986 samples. ************************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-07 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.08138 0.01429 0.02177 -17.73138; -0.01739 1.13757 0.26971 -68.58839; -0.01864 -0.23451 1.02087 13.79792; 0.00000 0.00000 0.00000 1.00000; nsamples 364986 Quasinewton: input matrix 1.08138 0.01429 0.02177 -17.73138; -0.01739 1.13757 0.26971 -68.58839; -0.01864 -0.23451 1.02087 13.79792; 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: 010: -log(p) = 4.1 tol 0.000000 final transform: 1.08138 0.01429 0.02177 -17.73138; -0.01739 1.13757 0.26971 -68.58839; -0.01864 -0.23451 1.02087 13.79792; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach_with_skull.lta... #VMPC# mri_em_register VmPeak 794204 FSRUNTIME@ mri_em_register 0.1225 hours 1 threads registration took 7 minutes and 21 seconds. @#@FSTIME 2023:06:14:16:14:21 mri_em_register N 4 e 440.94 S 1.48 U 439.36 P 99% M 628712 F 9 R 166531 W 0 c 3911 w 174 I 151112 O 0 L 1.50 1.03 0.64 @#@FSLOADPOST 2023:06:14:16:21:41 mri_em_register N 4 1.46 1.12 0.84 mri_watershed -T1 -brain_atlas /home/varun/freesurfer/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=128 y=133 z=127 r=72 first estimation of the main basin volume: 1591576 voxels Looking for seedpoints 2 found in the cerebellum 17 found in the rest of the brain global maximum in x=110, y=119, z=93, Imax=255 CSF=11, 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=2165146386 voxels, voxel volume =1.000 = 2165146386 mmm3 = 2165146.368 cm3 done. PostAnalyze...Basin Prior 2 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=149,y=168, z=147, r=105161 iterations ^^^^^^^^ couldn't find WM with original limits - expanding ^^^^^^ GLOBAL CSF_MIN=0, CSF_intensity=3, CSF_MAX=28 , nb = 43452 RIGHT_CER CSF_MIN=0, CSF_intensity=7, CSF_MAX=37 , nb = 3186 LEFT_CER CSF_MIN=0, CSF_intensity=7, CSF_MAX=22 , nb = 3618 RIGHT_BRAIN CSF_MIN=0, CSF_intensity=9, CSF_MAX=24 , nb = 8712 LEFT_BRAIN CSF_MIN=0, CSF_intensity=9, CSF_MAX=26 , nb = 15876 OTHER CSF_MIN=0, CSF_intensity=3, CSF_MAX=5 , nb = 11082 Problem with the least square interpolation in GM_MIN calculation. CSF_MAX TRANSITION GM_MIN GM GLOBAL before analyzing : 28, 24, 20, 56 after analyzing : 18, 24, 25, 32 RIGHT_CER before analyzing : 37, 30, 24, 56 after analyzing : 24, 30, 30, 36 LEFT_CER before analyzing : 22, 28, 42, 72 after analyzing : 22, 37, 42, 45 RIGHT_BRAIN before analyzing : 24, 24, 26, 52 after analyzing : 24, 25, 26, 31 LEFT_BRAIN before analyzing : 26, 26, 27, 54 after analyzing : 20, 26, 27, 33 OTHER before analyzing : 5, 7, 24, 41 after analyzing : 5, 19, 25, 24 mri_strip_skull: done peeling brain highly tesselated surface with 10242 vertices matching...111 iterations *********************VALIDATION********************* curvature mean = -0.004, std = 0.031 curvature mean = 65.842, std = 13.482 No Rigid alignment: -atlas Mode Off (basic atlas / no registration) before rotation: sse = 29.67, sigma = 78.11 after rotation: sse = 29.67, sigma = 78.11 Localization of inacurate regions: Erosion-Dilation steps the sse mean is 36.56, its var is 56.28 before Erosion-Dilatation 42.05% 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...102 iterations mri_strip_skull: done peeling brain Brain Size = 2554813 voxels, voxel volume = 1.000 mm3 = 2554813 mmm3 = 2554.813 cm3 ****************************** Saving brainmask.auto.mgz done mri_watershed utimesec 20.062517 mri_watershed stimesec 0.234649 mri_watershed ru_maxrss 852916 mri_watershed ru_ixrss 0 mri_watershed ru_idrss 0 mri_watershed ru_isrss 0 mri_watershed ru_minflt 215947 mri_watershed ru_majflt 13 mri_watershed ru_nswap 0 mri_watershed ru_inblock 9808 mri_watershed ru_oublock 0 mri_watershed ru_msgsnd 0 mri_watershed ru_msgrcv 0 mri_watershed ru_nsignals 0 mri_watershed ru_nvcsw 741 mri_watershed ru_nivcsw 417 mri_watershed done @#@FSTIME 2023:06:14:16:21:42 mri_watershed N 6 e 20.55 S 0.28 U 20.06 P 98% M 852916 F 13 R 215951 W 0 c 419 w 742 I 9808 O 0 L 1.46 1.12 0.84 @#@FSLOADPOST 2023:06:14:16:22:02 mri_watershed N 6 1.39 1.13 0.85 cp brainmask.auto.mgz brainmask.mgz #------------------------------------- #@# EM Registration Wed Jun 14 16:22:03 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri mri_em_register -uns 3 -mask brainmask.mgz nu.mgz /home/varun/freesurfer/average/RB_all_2020-01-02.gca transforms/talairach.lta setting unknown_nbr_spacing = 3 using MR volume brainmask.mgz to mask input volume... == Number of threads available to mri_em_register for OpenMP = 1 == reading 1 input volumes... logging results to talairach.log reading '/home/varun/freesurfer/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=22.9 skull bounding box = (64, 80, 48) --> (190, 216, 233) finding center of left hemi white matter using (106, 125, 141) as brain centroid of Right_Cerebral_White_Matter... MRImask(): AllowDiffGeom = 1 mean wm in atlas = 107, using box (91,108,118) --> (121, 141,163) to find MRI wm before smoothing, mri peak at 111 robust fit to distribution - 110 +- 3.5 after smoothing, mri peak at 110, scaling input intensities by 0.973 scaling channel 0 by 0.972727 initial log_p = -4.419 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.291135 @ (-10.526, -10.526, -31.579) max log p = -4.008760 @ (5.263, -15.789, 5.263) max log p = -3.928399 @ (2.632, 7.895, 2.632) max log p = -3.928399 @ (0.000, 0.000, 0.000) max log p = -3.914087 @ (0.658, 3.289, 0.658) max log p = -3.913273 @ (-0.987, -2.303, -0.329) max log p = -3.913273 @ (0.000, 0.000, 0.000) max log p = -3.913273 @ (0.000, 0.000, 0.000) Found translation: (-3.0, -17.4, -23.4): log p = -3.913 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.460, old_max_log_p =-3.913 (thresh=-3.9) 1.06375 0.00000 0.00000 -11.23290; 0.00000 1.19413 0.31997 -85.17984; 0.00000 -0.27823 1.03837 17.24447; 0.00000 0.00000 0.00000 1.00000; iteration took 1 minutes and 8 seconds. **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.460, old_max_log_p =-3.460 (thresh=-3.5) 1.06375 0.00000 0.00000 -11.23290; 0.00000 1.19413 0.31997 -85.17984; 0.00000 -0.27823 1.03837 17.24447; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 iteration took 1 minutes and 0 seconds. **************************************** Nine parameter search. iteration 2 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.394, old_max_log_p =-3.460 (thresh=-3.5) 1.08295 0.00000 0.00000 -13.72401; 0.00000 1.15055 0.37966 -86.42780; 0.00000 -0.35573 1.01522 30.80625; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 58 seconds. **************************************** Nine parameter search. iteration 3 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.390, old_max_log_p =-3.394 (thresh=-3.4) 1.10325 0.00000 0.00000 -16.35888; 0.00000 1.17135 0.31245 -79.23232; 0.00000 -0.27448 1.01842 17.28471; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 53 seconds. **************************************** Nine parameter search. iteration 4 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.386, old_max_log_p =-3.390 (thresh=-3.4) 1.10325 0.00000 0.00000 -16.35888; 0.00000 1.20066 0.28522 -79.87715; 0.00000 -0.23457 1.02848 10.59284; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 55 seconds. **************************************** Nine parameter search. iteration 5 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.385, old_max_log_p =-3.386 (thresh=-3.4) 1.10325 0.00000 0.00000 -16.35888; 0.00000 1.17815 0.27987 -76.14276; 0.00000 -0.23897 1.04776 8.81482; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 iteration took 0 minutes and 57 seconds. **************************************** Nine parameter search. iteration 6 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.345, old_max_log_p =-3.385 (thresh=-3.4) 1.10030 0.00330 0.02799 -19.40283; -0.00900 1.17685 0.29746 -76.96554; -0.02705 -0.25813 1.04273 16.03279; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 52 seconds. **************************************** Nine parameter search. iteration 7 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.340, old_max_log_p =-3.345 (thresh=-3.3) 1.10030 0.00330 0.02799 -19.40283; -0.00901 1.17822 0.29780 -77.19498; -0.02696 -0.25723 1.03907 16.34681; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 50 seconds. **************************************** Nine parameter search. iteration 8 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.337, old_max_log_p =-3.340 (thresh=-3.3) 1.10030 0.00330 0.02799 -19.40283; -0.00902 1.17960 0.29815 -77.42523; -0.02693 -0.25693 1.03785 16.45120; 0.00000 0.00000 0.00000 1.00000; min search scale 0.025000 reached *********************************************** Computing MAP estimate using 2841 samples... *********************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-05 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.10030 0.00330 0.02799 -19.40283; -0.00902 1.17960 0.29815 -77.42523; -0.02693 -0.25693 1.03785 16.45120; 0.00000 0.00000 0.00000 1.00000; nsamples 2841 Quasinewton: input matrix 1.10030 0.00330 0.02799 -19.40283; -0.00902 1.17960 0.29815 -77.42523; -0.02693 -0.25693 1.03785 16.45120; 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: 1.10030 0.00330 0.02799 -19.40283; -0.00902 1.17960 0.29815 -77.42523; -0.02693 -0.25693 1.03785 16.45120; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -3.337 (old=-4.419) transform before final EM align: 1.10030 0.00330 0.02799 -19.40283; -0.00902 1.17960 0.29815 -77.42523; -0.02693 -0.25693 1.03785 16.45120; 0.00000 0.00000 0.00000 1.00000; ************************************************** EM alignment process ... Computing final MAP estimate using 315638 samples. ************************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-07 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.10030 0.00330 0.02799 -19.40283; -0.00902 1.17960 0.29815 -77.42523; -0.02693 -0.25693 1.03785 16.45120; 0.00000 0.00000 0.00000 1.00000; nsamples 315638 Quasinewton: input matrix 1.10030 0.00330 0.02799 -19.40283; -0.00902 1.17960 0.29815 -77.42523; -0.02693 -0.25693 1.03785 16.45120; 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: 1.10030 0.00330 0.02799 -19.40283; -0.00902 1.17960 0.29815 -77.42523; -0.02693 -0.25693 1.03785 16.45120; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach.lta... #VMPC# mri_em_register VmPeak 781656 FSRUNTIME@ mri_em_register 0.1519 hours 1 threads registration took 9 minutes and 7 seconds. @#@FSTIME 2023:06:14:16:22:03 mri_em_register N 7 e 546.84 S 1.57 U 545.19 P 99% M 616264 F 0 R 166678 W 0 c 3716 w 378 I 139952 O 0 L 1.39 1.13 0.85 @#@FSLOADPOST 2023:06:14:16:31:10 mri_em_register N 7 1.25 1.11 0.98 #-------------------------------------- #@# CA Normalize Wed Jun 14 16:31:10 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri mri_ca_normalize -c ctrl_pts.mgz -mask brainmask.mgz nu.mgz /home/varun/freesurfer/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 '/home/varun/freesurfer/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=23.9 skull bounding box = (64, 80, 48) --> (190, 216, 233) finding center of left hemi white matter using (106, 125, 141) as brain centroid of Right_Cerebral_White_Matter... mean wm in atlas = 107, using box (91,108,118) --> (121, 141,163) to find MRI wm before smoothing, mri peak at 111 robust fit to distribution - 110 +- 3.5 after smoothing, mri peak at 110, scaling input intensities by 0.973 scaling channel 0 by 0.972727 using 246437 sample points... INFO: compute sample coordinates transform 1.10030 0.00330 0.02799 -19.40283; -0.00902 1.17960 0.29815 -77.42523; -0.02693 -0.25693 1.03785 16.45120; 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 (129, 81, 52) --> (189, 180, 206) Left_Cerebral_White_Matter: limiting intensities to 95.0 --> 132.0 3 of 4714 (0.1%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (70, 81, 51) --> (130, 175, 206) Right_Cerebral_White_Matter: limiting intensities to 96.0 --> 132.0 0 of 5894 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (131, 155, 79) --> (174, 194, 129) Left_Cerebellum_White_Matter: limiting intensities to 97.0 --> 132.0 0 of 319 (0.0%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (89, 155, 76) --> (129, 193, 129) Right_Cerebellum_White_Matter: limiting intensities to 100.0 --> 132.0 0 of 253 (0.0%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (114, 146, 112) --> (145, 203, 139) Brain_Stem: limiting intensities to 90.0 --> 132.0 0 of 323 (0.0%) samples deleted using 11503 total control points for intensity normalization... bias field = 0.985 +- 0.063 69 of 11500 control points discarded finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (129, 81, 52) --> (189, 180, 206) Left_Cerebral_White_Matter: limiting intensities to 92.0 --> 132.0 3 of 4850 (0.1%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (70, 81, 51) --> (130, 175, 206) Right_Cerebral_White_Matter: limiting intensities to 92.0 --> 132.0 1 of 6095 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (131, 155, 79) --> (174, 194, 129) Left_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 7 of 386 (1.8%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (89, 155, 76) --> (129, 193, 129) Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 2 of 304 (0.7%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (114, 146, 112) --> (145, 203, 139) Brain_Stem: limiting intensities to 88.0 --> 132.0 22 of 422 (5.2%) samples deleted using 12057 total control points for intensity normalization... bias field = 1.017 +- 0.047 63 of 11918 control points discarded finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (129, 81, 52) --> (189, 180, 206) Left_Cerebral_White_Matter: limiting intensities to 92.0 --> 132.0 9 of 4839 (0.2%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (70, 81, 51) --> (130, 175, 206) Right_Cerebral_White_Matter: limiting intensities to 93.0 --> 132.0 3 of 6053 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (131, 155, 79) --> (174, 194, 129) Left_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 76 of 409 (18.6%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (89, 155, 76) --> (129, 193, 129) Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 75 of 319 (23.5%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (114, 146, 112) --> (145, 203, 139) Brain_Stem: limiting intensities to 88.0 --> 132.0 182 of 505 (36.0%) samples deleted using 12125 total control points for intensity normalization... bias field = 1.014 +- 0.035 32 of 11630 control points discarded writing normalized volume to norm.mgz... writing control points to ctrl_pts.mgz freeing GCA...done. normalization took 0 minutes and 52 seconds. @#@FSTIME 2023:06:14:16:31:10 mri_ca_normalize N 8 e 51.69 S 0.59 U 50.82 P 99% M 708280 F 9 R 408124 W 0 c 1014 w 805 I 1352 O 0 L 1.25 1.11 0.98 @#@FSLOADPOST 2023:06:14:16:32:02 mri_ca_normalize N 8 1.11 1.09 0.98 #-------------------------------------- #@# CA Reg Wed Jun 14 16:32:02 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri mri_ca_register -nobigventricles -T transforms/talairach.lta -align-after -mask brainmask.mgz norm.mgz /home/varun/freesurfer/average/RB_all_2020-01-02.gca transforms/talairach.m3z not handling expanded ventricles... using previously computed transform transforms/talairach.lta renormalizing sequences with structure alignment, equivalent to: -renormalize -regularize_mean 0.500 -regularize 0.500 using MR volume brainmask.mgz to mask input volume... == Number of threads available to mri_ca_register for OpenMP = 1 == reading 1 input volumes... logging results to talairach.log reading input volume 'norm.mgz'... reading GCA '/home/varun/freesurfer/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 0.90931 #FOTS# QuadFit found better minimum quadopt=(dt=183.93,rms=0.846076) vs oldopt=(dt=92.48,rms=0.862066) #GCMRL# 0 dt 183.929619 rms 0.846 6.954% neg 0 invalid 762 tFOTS 12.5090 tGradient 5.3500 tsec 18.7650 #FOTS# QuadFit found better minimum quadopt=(dt=206.863,rms=0.828614) vs oldopt=(dt=92.48,rms=0.834123) #GCMRL# 1 dt 206.862559 rms 0.829 2.064% neg 0 invalid 762 tFOTS 11.7290 tGradient 5.0790 tsec 17.6750 #FOTS# QuadFit found better minimum quadopt=(dt=173.222,rms=0.818259) vs oldopt=(dt=92.48,rms=0.821003) #GCMRL# 2 dt 173.221557 rms 0.818 1.250% neg 0 invalid 762 tFOTS 11.7660 tGradient 5.0260 tsec 17.6240 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.811659) vs oldopt=(dt=369.92,rms=0.813545) #GCMRL# 3 dt 221.952000 rms 0.812 0.807% neg 0 invalid 762 tFOTS 10.9530 tGradient 4.8660 tsec 16.6090 #FOTS# QuadFit found better minimum quadopt=(dt=160,rms=0.805975) vs oldopt=(dt=92.48,rms=0.807416) #GCMRL# 4 dt 160.000000 rms 0.806 0.700% neg 0 invalid 762 tFOTS 11.7840 tGradient 4.8980 tsec 17.5820 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.801273) vs oldopt=(dt=369.92,rms=0.801298) #GCMRL# 5 dt 295.936000 rms 0.801 0.583% neg 0 invalid 762 tFOTS 11.8190 tGradient 4.8140 tsec 17.4620 #FOTS# QuadFit found better minimum quadopt=(dt=137.897,rms=0.797328) vs oldopt=(dt=92.48,rms=0.797991) #GCMRL# 6 dt 137.896907 rms 0.797 0.492% neg 0 invalid 762 tFOTS 11.4470 tGradient 4.7190 tsec 17.0030 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.79256) vs oldopt=(dt=369.92,rms=0.79291) #GCMRL# 7 dt 517.888000 rms 0.793 0.598% neg 0 invalid 762 tFOTS 11.4770 tGradient 4.8550 tsec 17.2490 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.788186) vs oldopt=(dt=92.48,rms=0.788556) #GCMRL# 8 dt 129.472000 rms 0.788 0.552% neg 0 invalid 762 tFOTS 11.4130 tGradient 4.7900 tsec 17.1000 #GCMRL# 9 dt 369.920000 rms 0.785 0.357% neg 0 invalid 762 tFOTS 11.7710 tGradient 4.8270 tsec 17.5380 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.782987) vs oldopt=(dt=92.48,rms=0.78325) #GCMRL# 10 dt 129.472000 rms 0.783 0.303% neg 0 invalid 762 tFOTS 12.8540 tGradient 4.6970 tsec 18.4230 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.780013) vs oldopt=(dt=369.92,rms=0.780257) #GCMRL# 11 dt 517.888000 rms 0.780 0.380% neg 0 invalid 762 tFOTS 12.6240 tGradient 4.8620 tsec 18.3420 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.777625) vs oldopt=(dt=92.48,rms=0.777722) #GCMRL# 12 dt 110.976000 rms 0.778 0.306% neg 0 invalid 762 tFOTS 11.8120 tGradient 4.8010 tsec 17.3630 #GCMRL# 13 dt 1479.680000 rms 0.771 0.847% neg 0 invalid 762 tFOTS 11.1160 tGradient 4.6590 tsec 16.5350 #GCMRL# 14 dt 92.480000 rms 0.767 0.498% neg 0 invalid 762 tFOTS 11.2610 tGradient 4.6090 tsec 16.7670 #FOTS# QuadFit found better minimum quadopt=(dt=1183.74,rms=0.765199) vs oldopt=(dt=1479.68,rms=0.765522) #GCMRL# 15 dt 1183.744000 rms 0.765 0.261% neg 0 invalid 762 tFOTS 11.8780 tGradient 4.5910 tsec 17.4080 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.761799) vs oldopt=(dt=92.48,rms=0.762124) #GCMRL# 16 dt 129.472000 rms 0.762 0.444% neg 0 invalid 762 tFOTS 11.4360 tGradient 4.7310 tsec 16.9740 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.761461) vs oldopt=(dt=92.48,rms=0.7615) #GCMRL# 17 dt 129.472000 rms 0.761 0.000% neg 0 invalid 762 tFOTS 11.1730 tGradient 4.6910 tsec 16.6470 #GCMRL# 18 dt 129.472000 rms 0.761 0.071% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5730 tsec 5.4660 #GCMRL# 19 dt 129.472000 rms 0.760 0.114% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7820 tsec 5.5950 #GCMRL# 20 dt 129.472000 rms 0.759 0.148% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6720 tsec 5.4940 #GCMRL# 21 dt 129.472000 rms 0.758 0.168% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5470 tsec 5.3530 #GCMRL# 22 dt 129.472000 rms 0.756 0.195% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5850 tsec 5.3890 #GCMRL# 23 dt 129.472000 rms 0.754 0.230% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7100 tsec 5.5620 #GCMRL# 24 dt 129.472000 rms 0.753 0.232% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5790 tsec 5.3240 #GCMRL# 25 dt 129.472000 rms 0.751 0.233% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8740 tsec 5.6360 #GCMRL# 26 dt 129.472000 rms 0.749 0.217% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5710 tsec 5.5670 #GCMRL# 27 dt 129.472000 rms 0.748 0.220% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5320 tsec 5.4140 #GCMRL# 28 dt 129.472000 rms 0.746 0.226% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5470 tsec 5.3330 #GCMRL# 29 dt 129.472000 rms 0.744 0.218% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5320 tsec 5.3120 #GCMRL# 30 dt 129.472000 rms 0.743 0.201% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6380 tsec 5.4000 #GCMRL# 31 dt 129.472000 rms 0.742 0.175% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8220 tsec 5.6250 #GCMRL# 32 dt 129.472000 rms 0.740 0.167% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7810 tsec 5.5810 #GCMRL# 33 dt 129.472000 rms 0.739 0.157% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6620 tsec 5.5300 #GCMRL# 34 dt 129.472000 rms 0.738 0.163% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7500 tsec 5.5250 #GCMRL# 35 dt 129.472000 rms 0.737 0.152% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8750 tsec 5.7030 #GCMRL# 36 dt 129.472000 rms 0.736 0.128% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7950 tsec 5.5790 #GCMRL# 37 dt 129.472000 rms 0.735 0.105% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8220 tsec 5.7720 #GCMRL# 38 dt 129.472000 rms 0.734 0.107% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6870 tsec 5.5300 #GCMRL# 39 dt 129.472000 rms 0.733 0.113% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7170 tsec 5.5100 #GCMRL# 40 dt 129.472000 rms 0.733 0.104% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6520 tsec 5.5230 #GCMRL# 41 dt 0.850000 rms 0.733 0.000% neg 0 invalid 762 tFOTS 11.8480 tGradient 4.8400 tsec 17.4500 #GCAMreg# pass 0 level1 5 level2 1 tsec 466.917 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 0.733314 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.732282) vs oldopt=(dt=92.48,rms=0.732323) #GCMRL# 43 dt 129.472000 rms 0.732 0.141% neg 0 invalid 762 tFOTS 11.3260 tGradient 4.7810 tsec 16.8620 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.732164) vs oldopt=(dt=92.48,rms=0.732178) #GCMRL# 44 dt 129.472000 rms 0.732 0.000% neg 0 invalid 762 tFOTS 12.5010 tGradient 4.8400 tsec 18.2200 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 0.74699 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.74521) vs oldopt=(dt=25.92,rms=0.745366) #GCMRL# 46 dt 36.288000 rms 0.745 0.238% neg 0 invalid 762 tFOTS 11.1110 tGradient 3.5960 tsec 15.5740 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.744515) vs oldopt=(dt=25.92,rms=0.744644) #GCMRL# 47 dt 36.288000 rms 0.745 0.000% neg 0 invalid 762 tFOTS 10.5020 tGradient 3.4840 tsec 14.7910 #GCMRL# 48 dt 36.288000 rms 0.744 0.111% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6630 tsec 4.4220 #GCMRL# 49 dt 36.288000 rms 0.743 0.136% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3680 tsec 4.1480 #GCMRL# 50 dt 36.288000 rms 0.742 0.121% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3470 tsec 4.1440 #GCMRL# 51 dt 36.288000 rms 0.740 0.187% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5460 tsec 4.3480 #GCMRL# 52 dt 36.288000 rms 0.738 0.374% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4300 tsec 4.2800 #GCMRL# 53 dt 36.288000 rms 0.733 0.612% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4480 tsec 4.3130 #GCMRL# 54 dt 36.288000 rms 0.727 0.786% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6350 tsec 4.5030 #GCMRL# 55 dt 36.288000 rms 0.721 0.834% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5070 tsec 4.2990 #GCMRL# 56 dt 36.288000 rms 0.716 0.754% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6270 tsec 4.4680 #GCMRL# 57 dt 36.288000 rms 0.712 0.596% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7840 tsec 4.6340 #GCMRL# 58 dt 36.288000 rms 0.709 0.396% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5660 tsec 4.3580 #GCMRL# 59 dt 36.288000 rms 0.707 0.193% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4620 tsec 4.2490 #GCMRL# 60 dt 36.288000 rms 0.707 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4620 tsec 4.2890 #GCMRL# 61 dt 36.288000 rms 0.707 -0.093% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5430 tsec 5.1570 #GCAMreg# pass 0 level1 4 level2 1 tsec 111.411 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 0.707887 #GCMRL# 63 dt 0.000000 rms 0.707 0.094% neg 0 invalid 762 tFOTS 11.2140 tGradient 3.4150 tsec 15.4050 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 0.744858 #GCMRL# 65 dt 0.000000 rms 0.744 0.085% neg 0 invalid 762 tFOTS 10.2600 tGradient 2.9050 tsec 13.9770 #GCAMreg# pass 0 level1 3 level2 1 tsec 31.596 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 0.744858 #GCMRL# 67 dt 0.000000 rms 0.744 0.085% neg 0 invalid 762 tFOTS 9.7300 tGradient 2.8010 tsec 13.3010 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 0.85403 #FOTS# QuadFit found better minimum quadopt=(dt=2.84744,rms=0.819524) vs oldopt=(dt=2.88,rms=0.81953) #GCMRL# 69 dt 2.847442 rms 0.820 4.040% neg 0 invalid 762 tFOTS 10.5600 tGradient 2.8780 tsec 14.3320 #FOTS# QuadFit found better minimum quadopt=(dt=1.68293,rms=0.815926) vs oldopt=(dt=0.72,rms=0.81713) #GCMRL# 70 dt 1.682927 rms 0.816 0.439% neg 0 invalid 762 tFOTS 11.2540 tGradient 3.0480 tsec 15.1340 #FOTS# QuadFit found better minimum quadopt=(dt=1.07895,rms=0.815193) vs oldopt=(dt=0.72,rms=0.815291) #GCMRL# 71 dt 1.078947 rms 0.815 0.000% neg 0 invalid 762 tFOTS 10.1970 tGradient 2.9410 tsec 13.9490 #GCMRL# 72 dt 1.078947 rms 0.815 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4900 tsec 3.3580 #GCAMreg# pass 0 level1 2 level2 1 tsec 54.307 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 0.815783 #GCMRL# 74 dt 0.000000 rms 0.815 0.074% neg 0 invalid 762 tFOTS 10.0610 tGradient 2.7100 tsec 13.5230 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 0.886413 #GCMRL# 76 dt 0.320000 rms 0.885 0.154% neg 0 invalid 762 tFOTS 10.3230 tGradient 2.4590 tsec 13.5520 #GCMRL# 77 dt 1.280000 rms 0.878 0.744% neg 0 invalid 762 tFOTS 10.1890 tGradient 2.3860 tsec 13.3470 #FOTS# QuadFit found better minimum quadopt=(dt=0.256,rms=0.876754) vs oldopt=(dt=0.32,rms=0.876914) #GCMRL# 78 dt 0.256000 rms 0.877 0.000% neg 0 invalid 762 tFOTS 10.1510 tGradient 2.4210 tsec 13.3390 #GCMRL# 79 dt 0.256000 rms 0.875 0.197% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7230 tsec 3.5090 #GCMRL# 80 dt 0.256000 rms 0.873 0.278% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6200 tsec 3.4090 #GCMRL# 81 dt 0.256000 rms 0.870 0.333% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5100 tsec 3.2920 #GCMRL# 82 dt 0.256000 rms 0.868 0.247% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5840 tsec 3.4030 #GCMRL# 83 dt 0.256000 rms 0.866 0.121% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4630 tsec 3.3100 #GCMRL# 84 dt 0.256000 rms 0.866 -0.019% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4600 tsec 4.0620 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.865979) vs oldopt=(dt=0.32,rms=0.866026) #GCMRL# 85 dt 0.448000 rms 0.866 0.057% neg 0 invalid 762 tFOTS 10.5090 tGradient 2.5610 tsec 13.9720 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.864716) vs oldopt=(dt=0.32,rms=0.86494) #GCMRL# 86 dt 0.448000 rms 0.865 0.146% neg 0 invalid 762 tFOTS 11.1620 tGradient 2.6320 tsec 14.6090 #FOTS# QuadFit found better minimum quadopt=(dt=0.192,rms=0.86447) vs oldopt=(dt=0.32,rms=0.864534) #GCAMreg# pass 0 level1 1 level2 1 tsec 107.321 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 0.865105 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.863567) vs oldopt=(dt=0.32,rms=0.863744) #GCMRL# 88 dt 0.448000 rms 0.864 0.178% neg 0 invalid 762 tFOTS 10.4780 tGradient 2.4680 tsec 13.7370 #GCMRL# 89 dt 0.080000 rms 0.864 0.000% neg 0 invalid 762 tFOTS 10.8120 tGradient 2.5940 tsec 14.2410 #GCMRL# 90 dt 0.080000 rms 0.864 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5120 tsec 3.2810 #GCMRL# 91 dt 0.080000 rms 0.863 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5550 tsec 3.3590 #GCMRL# 92 dt 0.080000 rms 0.863 0.011% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5430 tsec 3.4010 #GCMRL# 93 dt 0.080000 rms 0.863 0.017% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4980 tsec 3.3380 #GCMRL# 94 dt 0.080000 rms 0.863 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6450 tsec 3.5440 #GCMRL# 95 dt 0.080000 rms 0.863 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5990 tsec 3.4280 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 0.831515 #GCMRL# 97 dt 0.320000 rms 0.822 1.089% neg 0 invalid 762 tFOTS 10.6640 tGradient 2.1200 tsec 13.5480 #FOTS# QuadFit found better minimum quadopt=(dt=0.028,rms=0.821853) vs oldopt=(dt=0.02,rms=0.821947) #GCMRL# 98 dt 0.028000 rms 0.822 0.000% neg 0 invalid 762 tFOTS 11.1300 tGradient 2.6780 tsec 14.6950 #GCAMreg# pass 0 level1 0 level2 1 tsec 35.694 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 0.822526 #GCMRL# 100 dt 0.020000 rms 0.822 0.099% neg 0 invalid 762 tFOTS 10.2920 tGradient 2.1710 tsec 13.2490 #FOTS# QuadFit found better minimum quadopt=(dt=0.00175,rms=0.821708) vs oldopt=(dt=0.00125,rms=0.821708) #GCMRL# 101 dt 0.001750 rms 0.822 0.000% neg 0 invalid 762 tFOTS 10.5480 tGradient 2.0320 tsec 13.4770 GCAMregister done in 17.229 min Starting GCAmapRenormalizeWithAlignment() without scales renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.10253 (16) mri peak = 0.20795 ( 7) Left_Lateral_Ventricle (4): linear fit = 0.31 x + 0.0 (1230 voxels, overlap=0.006) Left_Lateral_Ventricle (4): linear fit = 0.40 x + 0.0 (1230 voxels, peak = 5), gca=6.4 gca peak = 0.17690 (16) mri peak = 0.17154 ( 6) Right_Lateral_Ventricle (43): linear fit = 0.31 x + 0.0 (911 voxels, overlap=0.037) Right_Lateral_Ventricle (43): linear fit = 0.40 x + 0.0 (911 voxels, peak = 5), gca=6.4 gca peak = 0.28275 (96) mri peak = 0.07628 (95) Right_Pallidum (52): linear fit = 1.00 x + 0.0 (746 voxels, overlap=0.999) Right_Pallidum (52): linear fit = 1.00 x + 0.0 (746 voxels, peak = 96), gca=95.5 gca peak = 0.18948 (93) mri peak = 0.06310 (99) Left_Pallidum (13): linear fit = 1.03 x + 0.0 (589 voxels, overlap=0.828) Left_Pallidum (13): linear fit = 1.03 x + 0.0 (589 voxels, peak = 96), gca=96.3 gca peak = 0.20755 (55) mri peak = 0.09635 (56) Right_Hippocampus (53): linear fit = 0.92 x + 0.0 (521 voxels, overlap=0.995) Right_Hippocampus (53): linear fit = 0.92 x + 0.0 (521 voxels, peak = 50), gca=50.3 gca peak = 0.31831 (58) mri peak = 0.11338 (55) Left_Hippocampus (17): linear fit = 0.94 x + 0.0 (692 voxels, overlap=0.990) Left_Hippocampus (17): linear fit = 0.94 x + 0.0 (692 voxels, peak = 55), gca=54.8 gca peak = 0.11957 (102) mri peak = 0.17173 (106) Right_Cerebral_White_Matter (41): linear fit = 1.03 x + 0.0 (40746 voxels, overlap=0.505) Right_Cerebral_White_Matter (41): linear fit = 1.03 x + 0.0 (40746 voxels, peak = 106), gca=105.6 gca peak = 0.11429 (102) mri peak = 0.13670 (108) Left_Cerebral_White_Matter (2): linear fit = 1.05 x + 0.0 (43995 voxels, overlap=0.521) Left_Cerebral_White_Matter (2): linear fit = 1.05 x + 0.0 (43995 voxels, peak = 108), gca=107.6 gca peak = 0.14521 (59) mri peak = 0.04327 (52) Left_Cerebral_Cortex (3): linear fit = 0.89 x + 0.0 (14734 voxels, overlap=0.833) Left_Cerebral_Cortex (3): linear fit = 0.89 x + 0.0 (14734 voxels, peak = 53), gca=52.8 gca peak = 0.14336 (58) mri peak = 0.04257 (56) Right_Cerebral_Cortex (42): linear fit = 0.94 x + 0.0 (13374 voxels, overlap=0.972) Right_Cerebral_Cortex (42): linear fit = 0.94 x + 0.0 (13374 voxels, peak = 55), gca=54.8 gca peak = 0.13305 (70) mri peak = 0.16062 (61) Right_Caudate (50): linear fit = 0.88 x + 0.0 (454 voxels, overlap=0.350) Right_Caudate (50): linear fit = 0.88 x + 0.0 (454 voxels, peak = 62), gca=62.0 gca peak = 0.15761 (71) mri peak = 0.11809 (64) Left_Caudate (11): linear fit = 0.88 x + 0.0 (703 voxels, overlap=0.201) Left_Caudate (11): linear fit = 0.88 x + 0.0 (703 voxels, peak = 63), gca=62.8 gca peak = 0.13537 (57) mri peak = 0.03946 (55) Left_Cerebellum_Cortex (8): linear fit = 0.96 x + 0.0 (19714 voxels, overlap=0.873) Left_Cerebellum_Cortex (8): linear fit = 0.96 x + 0.0 (19714 voxels, peak = 55), gca=55.0 gca peak = 0.13487 (56) mri peak = 0.04108 ( 5) Right_Cerebellum_Cortex (47): linear fit = 1.04 x + 0.0 (21884 voxels, overlap=0.947) Right_Cerebellum_Cortex (47): linear fit = 1.04 x + 0.0 (21884 voxels, peak = 59), gca=58.5 gca peak = 0.19040 (84) mri peak = 0.08934 (90) Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (7369 voxels, overlap=0.447) Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (7369 voxels, peak = 89), gca=89.5 gca peak = 0.18871 (83) mri peak = 0.11055 (88) Right_Cerebellum_White_Matter (46): linear fit = 1.07 x + 0.0 (5493 voxels, overlap=0.496) Right_Cerebellum_White_Matter (46): linear fit = 1.07 x + 0.0 (5493 voxels, peak = 88), gca=88.4 gca peak = 0.24248 (57) mri peak = 0.17162 (56) Left_Amygdala (18): linear fit = 0.92 x + 0.0 (328 voxels, overlap=0.962) Left_Amygdala (18): linear fit = 0.92 x + 0.0 (328 voxels, peak = 52), gca=52.2 gca peak = 0.35833 (56) mri peak = 0.13151 (55) Right_Amygdala (54): linear fit = 0.96 x + 0.0 (427 voxels, overlap=1.000) Right_Amygdala (54): linear fit = 0.96 x + 0.0 (427 voxels, peak = 54), gca=54.0 gca peak = 0.12897 (85) mri peak = 0.04938 (89) Left_Thalamus (10): linear fit = 1.05 x + 0.0 (4323 voxels, overlap=0.959) Left_Thalamus (10): linear fit = 1.05 x + 0.0 (4323 voxels, peak = 90), gca=89.7 gca peak = 0.13127 (83) mri peak = 0.07931 (85) Right_Thalamus (49): linear fit = 1.01 x + 0.0 (2794 voxels, overlap=0.905) Right_Thalamus (49): linear fit = 1.01 x + 0.0 (2794 voxels, peak = 84), gca=84.2 gca peak = 0.12974 (78) mri peak = 0.05998 (76) Left_Putamen (12): linear fit = 0.96 x + 0.0 (1921 voxels, overlap=0.942) Left_Putamen (12): linear fit = 0.96 x + 0.0 (1921 voxels, peak = 75), gca=75.3 gca peak = 0.17796 (79) mri peak = 0.08296 (69) Right_Putamen (51): linear fit = 0.93 x + 0.0 (1957 voxels, overlap=0.880) Right_Putamen (51): linear fit = 0.93 x + 0.0 (1957 voxels, peak = 73), gca=73.1 gca peak = 0.10999 (80) mri peak = 0.08461 (88) Brain_Stem (16): linear fit = 1.12 x + 0.0 (9525 voxels, overlap=0.416) Brain_Stem (16): linear fit = 1.12 x + 0.0 (9525 voxels, peak = 90), gca=90.0 gca peak = 0.13215 (88) mri peak = 0.08951 (96) Right_VentralDC (60): linear fit = 1.09 x + 0.0 (857 voxels, overlap=0.347) Right_VentralDC (60): linear fit = 1.09 x + 0.0 (857 voxels, peak = 95), gca=95.5 gca peak = 0.11941 (89) mri peak = 0.09236 (96) Left_VentralDC (28): linear fit = 1.09 x + 0.0 (985 voxels, overlap=0.449) Left_VentralDC (28): linear fit = 1.09 x + 0.0 (985 voxels, peak = 97), gca=96.6 gca peak = 0.20775 (25) mri peak = 0.30044 ( 9) gca peak = 0.13297 (21) mri peak = 0.30519 ( 5) Fourth_Ventricle (15): linear fit = 0.23 x + 0.0 (309 voxels, overlap=0.016) Fourth_Ventricle (15): linear fit = 0.23 x + 0.0 (309 voxels, peak = 5), gca=4.9 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.19087 (28) gca peak Third_Ventricle = 0.20775 (25) gca peak Fourth_Ventricle = 0.13297 (21) gca peak CSF = 0.16821 (33) gca peak Left_Accumbens_area = 0.32850 (63) gca peak Left_undetermined = 0.98480 (28) gca peak Left_vessel = 0.40887 (53) gca peak Left_choroid_plexus = 0.10898 (46) gca peak Right_Inf_Lat_Vent = 0.17798 (26) gca peak Right_Accumbens_area = 0.30137 (64) gca peak Right_vessel = 0.47828 (52) gca peak Right_choroid_plexus = 0.11612 (45) gca peak Fifth_Ventricle = 0.59466 (35) gca peak WM_hypointensities = 0.10053 (78) gca peak non_WM_hypointensities = 0.07253 (60) gca peak Optic_Chiasm = 0.25330 (73) not using caudate to estimate GM means estimating mean gm scale to be 0.93 x + 0.0 estimating mean wm scale to be 1.04 x + 0.0 estimating mean csf scale to be 0.40 x + 0.0 saving intensity scales to talairach.label_intensities.txt GCAmapRenormalizeWithAlignment() took 3.17958 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 0.848281 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.824063) vs oldopt=(dt=369.92,rms=0.831673) #GCMRL# 103 dt 221.952000 rms 0.824 2.855% neg 0 invalid 762 tFOTS 11.9800 tGradient 5.1210 tsec 17.9340 #FOTS# QuadFit found better minimum quadopt=(dt=181.234,rms=0.81675) vs oldopt=(dt=92.48,rms=0.818963) #GCMRL# 104 dt 181.233645 rms 0.817 0.887% neg 0 invalid 762 tFOTS 11.8770 tGradient 4.8720 tsec 17.5200 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.808216) vs oldopt=(dt=369.92,rms=0.80898) #GCMRL# 105 dt 295.936000 rms 0.808 1.045% neg 0 invalid 762 tFOTS 10.8180 tGradient 4.7700 tsec 16.3760 #FOTS# QuadFit found better minimum quadopt=(dt=92.6792,rms=0.804542) vs oldopt=(dt=92.48,rms=0.804543) #GCMRL# 106 dt 92.679245 rms 0.805 0.455% neg 0 invalid 762 tFOTS 12.3850 tGradient 5.0860 tsec 18.3560 #GCMRL# 107 dt 1479.680000 rms 0.789 1.927% neg 0 invalid 762 tFOTS 10.6870 tGradient 5.0180 tsec 16.5070 #FOTS# QuadFit found better minimum quadopt=(dt=136.727,rms=0.783783) vs oldopt=(dt=92.48,rms=0.78459) #GCMRL# 108 dt 136.727273 rms 0.784 0.666% neg 0 invalid 762 tFOTS 11.9890 tGradient 5.0050 tsec 17.8080 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.783044) vs oldopt=(dt=92.48,rms=0.783131) #GCMRL# 109 dt 129.472000 rms 0.783 0.094% neg 0 invalid 762 tFOTS 12.1040 tGradient 4.9450 tsec 17.8990 #FOTS# QuadFit found better minimum quadopt=(dt=1183.74,rms=0.77598) vs oldopt=(dt=1479.68,rms=0.776822) #GCMRL# 110 dt 1183.744000 rms 0.776 0.902% neg 0 invalid 762 tFOTS 13.1700 tGradient 5.0290 tsec 19.0570 #FOTS# QuadFit found better minimum quadopt=(dt=73.984,rms=0.77257) vs oldopt=(dt=92.48,rms=0.772701) #GCMRL# 111 dt 73.984000 rms 0.773 0.439% neg 0 invalid 762 tFOTS 11.8390 tGradient 4.7830 tsec 17.4390 #GCMRL# 112 dt 92.480000 rms 0.772 0.000% neg 0 invalid 762 tFOTS 10.5280 tGradient 5.1690 tsec 16.5130 #GCMRL# 113 dt 92.480000 rms 0.772 0.082% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8610 tsec 5.6480 #GCMRL# 114 dt 92.480000 rms 0.770 0.140% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7490 tsec 5.5350 #GCMRL# 115 dt 92.480000 rms 0.769 0.189% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8570 tsec 5.7860 #GCMRL# 116 dt 92.480000 rms 0.767 0.225% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7610 tsec 5.5800 #GCMRL# 117 dt 92.480000 rms 0.765 0.247% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7730 tsec 5.5600 #GCMRL# 118 dt 92.480000 rms 0.763 0.271% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8590 tsec 5.6540 #GCMRL# 119 dt 92.480000 rms 0.761 0.280% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.0230 tsec 5.9200 #GCMRL# 120 dt 92.480000 rms 0.759 0.288% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.9360 tsec 5.7880 #GCMRL# 121 dt 92.480000 rms 0.757 0.289% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5550 tsec 6.3440 #GCMRL# 122 dt 92.480000 rms 0.755 0.286% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7170 tsec 5.5170 #GCMRL# 123 dt 92.480000 rms 0.753 0.274% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6650 tsec 5.4950 #GCMRL# 124 dt 92.480000 rms 0.751 0.259% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7510 tsec 5.5290 #GCMRL# 125 dt 92.480000 rms 0.749 0.244% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6610 tsec 5.4280 #GCMRL# 126 dt 92.480000 rms 0.747 0.226% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7200 tsec 5.5040 #GCMRL# 127 dt 92.480000 rms 0.745 0.215% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8620 tsec 5.8180 #GCMRL# 128 dt 92.480000 rms 0.744 0.205% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.1510 tsec 5.9940 #GCMRL# 129 dt 92.480000 rms 0.743 0.193% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.9750 tsec 5.8630 #GCMRL# 130 dt 92.480000 rms 0.741 0.185% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.1430 tsec 6.0870 #GCMRL# 131 dt 92.480000 rms 0.740 0.179% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.3360 tsec 6.2160 #GCMRL# 132 dt 92.480000 rms 0.739 0.172% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.0850 tsec 5.9170 #GCMRL# 133 dt 92.480000 rms 0.737 0.169% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.1770 tsec 6.1510 #GCMRL# 134 dt 92.480000 rms 0.736 0.162% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.2810 tsec 6.2890 #GCMRL# 135 dt 92.480000 rms 0.735 0.156% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6620 tsec 6.5640 #GCMRL# 136 dt 92.480000 rms 0.734 0.149% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.1090 tsec 5.9570 #GCMRL# 137 dt 92.480000 rms 0.733 0.139% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.1030 tsec 6.0050 #GCMRL# 138 dt 92.480000 rms 0.732 0.130% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.2030 tsec 6.1370 #GCMRL# 139 dt 92.480000 rms 0.731 0.122% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6430 tsec 6.6770 #GCMRL# 140 dt 92.480000 rms 0.730 0.116% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6190 tsec 6.5880 #GCMRL# 141 dt 92.480000 rms 0.729 0.115% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.4910 tsec 6.3910 #GCMRL# 142 dt 92.480000 rms 0.729 0.109% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.5020 tsec 6.4260 #GCMRL# 143 dt 92.480000 rms 0.728 0.103% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.4590 tsec 6.3940 #GCMRL# 144 dt 92.480000 rms 0.727 0.099% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6220 tsec 6.6810 #GCMRL# 145 dt 92.480000 rms 0.726 0.094% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.4660 tsec 6.3930 #GCMRL# 146 dt 92.480000 rms 0.726 0.087% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6450 tsec 6.6030 #GCMRL# 147 dt 92.480000 rms 0.725 0.083% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.2090 tsec 6.0580 #GCMRL# 148 dt 92.480000 rms 0.725 0.076% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.3440 tsec 6.3920 #GCMRL# 149 dt 92.480000 rms 0.724 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.2660 tsec 6.1350 #GCMRL# 150 dt 92.480000 rms 0.723 0.073% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.0090 tsec 5.9290 #GCMRL# 151 dt 92.480000 rms 0.723 0.070% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.2270 tsec 6.1640 #GCMRL# 152 dt 92.480000 rms 0.723 0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.1940 tsec 6.0960 #GCMRL# 153 dt 92.480000 rms 0.722 0.064% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6970 tsec 5.5230 #GCMRL# 154 dt 92.480000 rms 0.722 0.060% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.9360 tsec 5.8800 #GCMRL# 155 dt 92.480000 rms 0.721 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8260 tsec 5.6280 #GCMRL# 156 dt 92.480000 rms 0.721 0.057% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.9610 tsec 5.8100 #GCMRL# 157 dt 92.480000 rms 0.720 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7610 tsec 5.5860 #GCMRL# 158 dt 92.480000 rms 0.720 0.052% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8190 tsec 5.5850 #GCMRL# 159 dt 92.480000 rms 0.720 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7410 tsec 5.5110 #GCMRL# 160 dt 92.480000 rms 0.719 0.047% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7520 tsec 5.6140 #GCMRL# 161 dt 92.480000 rms 0.719 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.1150 tsec 5.9140 #GCMRL# 162 dt 92.480000 rms 0.719 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7700 tsec 5.6170 #GCMRL# 163 dt 92.480000 rms 0.718 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7430 tsec 5.5370 #GCMRL# 164 dt 92.480000 rms 0.718 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.9180 tsec 5.7160 #GCMRL# 165 dt 92.480000 rms 0.718 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8660 tsec 5.6930 #GCMRL# 166 dt 92.480000 rms 0.717 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7990 tsec 5.6050 #GCMRL# 167 dt 92.480000 rms 0.717 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8630 tsec 5.6470 #GCMRL# 168 dt 92.480000 rms 0.717 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.0710 tsec 5.9610 #GCMRL# 169 dt 92.480000 rms 0.717 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.3280 tsec 6.3010 #GCMRL# 170 dt 92.480000 rms 0.716 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.6570 tsec 6.6200 #GCMRL# 171 dt 92.480000 rms 0.716 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.4400 tsec 6.4420 #GCMRL# 172 dt 92.480000 rms 0.716 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.3730 tsec 6.2630 #GCMRL# 173 dt 92.480000 rms 0.716 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.9760 tsec 5.8100 #GCMRL# 174 dt 92.480000 rms 0.715 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.3630 tsec 6.3420 #GCMRL# 175 dt 92.480000 rms 0.715 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.7100 tsec 6.5810 #GCMRL# 176 dt 92.480000 rms 0.715 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.1100 tsec 6.0360 #GCMRL# 177 dt 92.480000 rms 0.715 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.9400 tsec 5.7720 #GCMRL# 178 dt 92.480000 rms 0.714 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.0850 tsec 5.9520 #GCMRL# 179 dt 92.480000 rms 0.714 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.0260 tsec 5.9550 #GCMRL# 180 dt 92.480000 rms 0.714 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.1210 tsec 5.9630 #GCMRL# 181 dt 92.480000 rms 0.713 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8730 tsec 5.6670 #GCMRL# 182 dt 92.480000 rms 0.713 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6430 tsec 5.4470 #GCMRL# 183 dt 92.480000 rms 0.713 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7670 tsec 5.5720 #GCMRL# 184 dt 92.480000 rms 0.713 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6680 tsec 5.5090 #GCMRL# 185 dt 92.480000 rms 0.713 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8000 tsec 5.6620 #GCMRL# 186 dt 92.480000 rms 0.712 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.9400 tsec 5.7970 #GCMRL# 187 dt 92.480000 rms 0.712 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7740 tsec 5.6100 #GCMRL# 188 dt 92.480000 rms 0.712 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7570 tsec 5.5590 #GCMRL# 189 dt 92.480000 rms 0.712 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6870 tsec 5.4900 #GCMRL# 190 dt 92.480000 rms 0.711 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6650 tsec 5.4590 #GCMRL# 191 dt 92.480000 rms 0.711 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.0160 tsec 5.8350 #GCMRL# 192 dt 92.480000 rms 0.711 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8860 tsec 5.7850 #GCMRL# 193 dt 92.480000 rms 0.711 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.9440 tsec 5.8240 #GCMRL# 194 dt 92.480000 rms 0.710 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8770 tsec 5.6640 #GCMRL# 195 dt 92.480000 rms 0.710 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7580 tsec 5.6270 #GCMRL# 196 dt 92.480000 rms 0.710 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.9590 tsec 5.8640 #GCMRL# 197 dt 92.480000 rms 0.710 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.2320 tsec 6.0740 #GCMRL# 198 dt 92.480000 rms 0.710 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.1220 tsec 5.9510 #GCMRL# 199 dt 92.480000 rms 0.709 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5710 tsec 5.3310 #GCMRL# 200 dt 92.480000 rms 0.709 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5530 tsec 5.3600 #GCMRL# 201 dt 92.480000 rms 0.709 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6530 tsec 5.4480 #GCMRL# 202 dt 92.480000 rms 0.709 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7640 tsec 5.5370 #GCMRL# 203 dt 92.480000 rms 0.709 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7470 tsec 5.5140 #GCMRL# 204 dt 92.480000 rms 0.708 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5810 tsec 5.3400 #GCMRL# 205 dt 92.480000 rms 0.708 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5650 tsec 5.3290 #GCMRL# 206 dt 92.480000 rms 0.708 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7360 tsec 5.5020 #GCMRL# 207 dt 92.480000 rms 0.708 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5990 tsec 5.3980 #GCMRL# 208 dt 92.480000 rms 0.708 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5530 tsec 5.3210 #GCMRL# 209 dt 92.480000 rms 0.708 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5660 tsec 5.3930 #FOTS# QuadFit found better minimum quadopt=(dt=8286.21,rms=0.706574) vs oldopt=(dt=5918.72,rms=0.706756) #GCMRL# 210 dt 8286.208000 rms 0.707 0.138% neg 0 invalid 762 tFOTS 11.2640 tGradient 4.5560 tsec 16.5770 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.706125) vs oldopt=(dt=92.48,rms=0.706169) #GCMRL# 211 dt 129.472000 rms 0.706 0.064% neg 0 invalid 762 tFOTS 10.6220 tGradient 4.5730 tsec 15.9520 #GCMRL# 212 dt 92.480000 rms 0.706 0.000% neg 0 invalid 762 tFOTS 11.3000 tGradient 4.5490 tsec 16.6280 #GCMRL# 213 dt 92.480000 rms 0.706 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5700 tsec 5.3330 #GCMRL# 214 dt 92.480000 rms 0.706 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6450 tsec 5.4040 #GCMRL# 215 dt 92.480000 rms 0.706 0.006% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5940 tsec 5.3520 #GCMRL# 216 dt 92.480000 rms 0.706 0.008% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5830 tsec 5.3470 #GCMRL# 217 dt 92.480000 rms 0.706 0.009% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6000 tsec 5.3690 #GCMRL# 218 dt 92.480000 rms 0.706 0.010% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6190 tsec 5.3800 #GCMRL# 219 dt 92.480000 rms 0.706 0.010% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5580 tsec 5.3660 #GCMRL# 220 dt 92.480000 rms 0.706 0.012% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6140 tsec 5.3750 #GCMRL# 221 dt 92.480000 rms 0.706 0.012% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6950 tsec 5.4660 #GCMRL# 222 dt 92.480000 rms 0.706 0.012% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6110 tsec 5.3660 #GCMRL# 223 dt 92.480000 rms 0.705 0.013% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6750 tsec 5.5000 #GCMRL# 224 dt 92.480000 rms 0.705 0.013% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5610 tsec 5.3210 #GCMRL# 225 dt 92.480000 rms 0.705 0.017% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6380 tsec 5.3970 #GCMRL# 226 dt 92.480000 rms 0.705 0.018% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6280 tsec 5.4030 #GCMRL# 227 dt 92.480000 rms 0.705 0.016% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5810 tsec 5.3640 #FOTS# QuadFit found better minimum quadopt=(dt=3551.23,rms=0.704601) vs oldopt=(dt=5918.72,rms=0.704676) #GCMRL# 228 dt 3551.232000 rms 0.705 0.050% neg 0 invalid 762 tFOTS 11.2070 tGradient 4.5780 tsec 16.5430 #GCMRL# 229 dt 92.480000 rms 0.704 0.000% neg 0 invalid 762 tFOTS 10.6020 tGradient 4.5420 tsec 15.9150 #GCMRL# 230 dt 92.480000 rms 0.704 0.010% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5870 tsec 5.3440 #GCMRL# 231 dt 92.480000 rms 0.704 0.010% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6050 tsec 5.3700 #GCMRL# 232 dt 92.480000 rms 0.704 0.007% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6030 tsec 5.3760 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.704294) vs oldopt=(dt=92.48,rms=0.704297) #GCAMreg# pass 0 level1 5 level2 1 tsec 941.726 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 0.705153 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.703815) vs oldopt=(dt=92.48,rms=0.703891) #GCMRL# 234 dt 129.472000 rms 0.704 0.190% neg 0 invalid 762 tFOTS 11.5180 tGradient 4.6070 tsec 17.0100 #FOTS# QuadFit found better minimum quadopt=(dt=443.904,rms=0.703213) vs oldopt=(dt=369.92,rms=0.703237) #GCMRL# 235 dt 443.904000 rms 0.703 0.085% neg 0 invalid 762 tFOTS 11.3300 tGradient 4.6420 tsec 16.7340 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.70304) vs oldopt=(dt=92.48,rms=0.703053) #GCMRL# 236 dt 129.472000 rms 0.703 0.000% neg 0 invalid 762 tFOTS 10.6060 tGradient 4.5960 tsec 15.9990 #GCMRL# 237 dt 129.472000 rms 0.703 0.020% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5720 tsec 5.3270 #GCMRL# 238 dt 129.472000 rms 0.703 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6460 tsec 5.4060 #GCMRL# 239 dt 129.472000 rms 0.703 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5710 tsec 5.3250 #GCMRL# 240 dt 129.472000 rms 0.702 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5830 tsec 5.3420 #GCMRL# 241 dt 129.472000 rms 0.702 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5740 tsec 5.3410 #GCMRL# 242 dt 129.472000 rms 0.702 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5870 tsec 5.3530 #GCMRL# 243 dt 129.472000 rms 0.702 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6390 tsec 5.4220 #GCMRL# 244 dt 129.472000 rms 0.702 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6670 tsec 5.4250 #GCMRL# 245 dt 129.472000 rms 0.701 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5470 tsec 5.3650 #GCMRL# 246 dt 129.472000 rms 0.701 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5610 tsec 5.3180 #GCMRL# 247 dt 129.472000 rms 0.701 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5880 tsec 5.3670 #GCMRL# 248 dt 129.472000 rms 0.701 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5640 tsec 5.3260 #GCMRL# 249 dt 129.472000 rms 0.701 0.019% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.6370 tsec 5.3950 #GCMRL# 250 dt 129.472000 rms 0.700 0.019% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5420 tsec 5.3170 #FOTS# QuadFit found better minimum quadopt=(dt=443.904,rms=0.700412) vs oldopt=(dt=369.92,rms=0.700415) #GCMRL# 251 dt 443.904000 rms 0.700 0.000% neg 0 invalid 762 tFOTS 11.2840 tGradient 4.5800 tsec 16.6420 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 0.703934 #FOTS# QuadFit found better minimum quadopt=(dt=213.126,rms=0.694385) vs oldopt=(dt=103.68,rms=0.696437) #GCMRL# 253 dt 213.126214 rms 0.694 1.357% neg 0 invalid 762 tFOTS 10.6040 tGradient 3.4620 tsec 14.8280 #FOTS# QuadFit found better minimum quadopt=(dt=134.084,rms=0.685721) vs oldopt=(dt=103.68,rms=0.686147) #GCMRL# 254 dt 134.083650 rms 0.686 1.248% neg 0 invalid 762 tFOTS 11.2230 tGradient 3.4080 tsec 15.4040 #FOTS# QuadFit found better minimum quadopt=(dt=75.5663,rms=0.681376) vs oldopt=(dt=103.68,rms=0.681927) #GCMRL# 255 dt 75.566265 rms 0.681 0.634% neg 0 invalid 762 tFOTS 10.6320 tGradient 3.4680 tsec 14.8590 #FOTS# QuadFit found better minimum quadopt=(dt=189.217,rms=0.676844) vs oldopt=(dt=103.68,rms=0.677619) #GCMRL# 256 dt 189.217391 rms 0.677 0.665% neg 0 invalid 762 tFOTS 10.6320 tGradient 3.3870 tsec 14.7820 #FOTS# QuadFit found better minimum quadopt=(dt=68.0635,rms=0.672769) vs oldopt=(dt=25.92,rms=0.673987) #GCMRL# 257 dt 68.063492 rms 0.673 0.602% neg 0 invalid 762 tFOTS 10.0030 tGradient 3.4890 tsec 14.2490 #FOTS# QuadFit found better minimum quadopt=(dt=101.783,rms=0.670274) vs oldopt=(dt=103.68,rms=0.670276) #GCMRL# 258 dt 101.783133 rms 0.670 0.371% neg 0 invalid 762 tFOTS 10.6180 tGradient 3.4540 tsec 14.8280 #FOTS# QuadFit found better minimum quadopt=(dt=72.0437,rms=0.667911) vs oldopt=(dt=103.68,rms=0.668261) #GCMRL# 259 dt 72.043716 rms 0.668 0.352% neg 0 invalid 762 tFOTS 10.6460 tGradient 3.4590 tsec 14.8870 #FOTS# QuadFit found better minimum quadopt=(dt=101.628,rms=0.665865) vs oldopt=(dt=103.68,rms=0.665868) #GCMRL# 260 dt 101.628141 rms 0.666 0.306% neg 0 invalid 762 tFOTS 10.6270 tGradient 3.4520 tsec 14.8370 #FOTS# QuadFit found better minimum quadopt=(dt=65.8935,rms=0.663867) vs oldopt=(dt=103.68,rms=0.664318) #GCMRL# 261 dt 65.893491 rms 0.664 0.300% neg 0 invalid 762 tFOTS 10.6590 tGradient 3.4190 tsec 14.8390 #GCMRL# 262 dt 103.680000 rms 0.662 0.276% neg 0 invalid 762 tFOTS 11.2830 tGradient 3.4490 tsec 15.5080 #FOTS# QuadFit found better minimum quadopt=(dt=67.1549,rms=0.660334) vs oldopt=(dt=103.68,rms=0.660686) #GCMRL# 263 dt 67.154930 rms 0.660 0.257% neg 0 invalid 762 tFOTS 10.6470 tGradient 3.3560 tsec 14.7660 #GCMRL# 264 dt 103.680000 rms 0.659 0.245% neg 0 invalid 762 tFOTS 10.8670 tGradient 3.4350 tsec 15.0570 #FOTS# QuadFit found better minimum quadopt=(dt=64.2362,rms=0.657218) vs oldopt=(dt=103.68,rms=0.657617) #GCMRL# 265 dt 64.236162 rms 0.657 0.228% neg 0 invalid 762 tFOTS 10.7310 tGradient 3.4570 tsec 14.9920 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.655743) vs oldopt=(dt=103.68,rms=0.655749) #GCMRL# 266 dt 124.416000 rms 0.656 0.224% neg 0 invalid 762 tFOTS 9.9980 tGradient 3.4520 tsec 14.2260 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.654378) vs oldopt=(dt=25.92,rms=0.654617) #GCMRL# 267 dt 36.288000 rms 0.654 0.208% neg 0 invalid 762 tFOTS 10.0980 tGradient 3.4130 tsec 14.2740 #FOTS# QuadFit found better minimum quadopt=(dt=1327.1,rms=0.643921) vs oldopt=(dt=1658.88,rms=0.644626) #GCMRL# 268 dt 1327.104000 rms 0.644 1.598% neg 0 invalid 762 tFOTS 10.6750 tGradient 3.4570 tsec 14.8900 #FOTS# QuadFit found better minimum quadopt=(dt=55.2925,rms=0.637806) vs oldopt=(dt=25.92,rms=0.639239) #GCMRL# 269 dt 55.292517 rms 0.638 0.950% neg 0 invalid 762 tFOTS 10.6670 tGradient 3.3780 tsec 14.8020 #FOTS# QuadFit found better minimum quadopt=(dt=62.208,rms=0.637026) vs oldopt=(dt=103.68,rms=0.63716) #GCMRL# 270 dt 62.208000 rms 0.637 0.122% neg 0 invalid 762 tFOTS 9.9690 tGradient 3.4210 tsec 14.1460 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.636353) vs oldopt=(dt=103.68,rms=0.636391) #GCMRL# 271 dt 145.152000 rms 0.636 0.106% neg 0 invalid 762 tFOTS 10.6050 tGradient 3.4210 tsec 14.8060 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.635758) vs oldopt=(dt=25.92,rms=0.635857) #GCMRL# 272 dt 36.288000 rms 0.636 0.093% neg 0 invalid 762 tFOTS 9.9530 tGradient 3.3690 tsec 14.0810 #FOTS# QuadFit found better minimum quadopt=(dt=995.328,rms=0.632324) vs oldopt=(dt=1658.88,rms=0.633313) #GCMRL# 273 dt 995.328000 rms 0.632 0.540% neg 0 invalid 762 tFOTS 10.6540 tGradient 3.4110 tsec 14.8240 #FOTS# QuadFit found better minimum quadopt=(dt=56,rms=0.629992) vs oldopt=(dt=25.92,rms=0.63037) #GCMRL# 274 dt 56.000000 rms 0.630 0.369% neg 0 invalid 762 tFOTS 10.5760 tGradient 3.5200 tsec 14.8520 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.629552) vs oldopt=(dt=25.92,rms=0.629623) #GCMRL# 275 dt 36.288000 rms 0.630 0.070% neg 0 invalid 762 tFOTS 10.6660 tGradient 3.4280 tsec 14.9020 #FOTS# QuadFit found better minimum quadopt=(dt=995.328,rms=0.626565) vs oldopt=(dt=1658.88,rms=0.62739) #GCMRL# 276 dt 995.328000 rms 0.627 0.475% neg 0 invalid 762 tFOTS 10.6140 tGradient 3.4180 tsec 14.7880 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.624487) vs oldopt=(dt=25.92,rms=0.624854) #GCMRL# 277 dt 36.288000 rms 0.624 0.332% neg 0 invalid 762 tFOTS 11.1090 tGradient 3.4110 tsec 15.2860 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.623691) vs oldopt=(dt=103.68,rms=0.623727) #GCMRL# 278 dt 124.416000 rms 0.624 0.128% neg 0 invalid 762 tFOTS 10.6340 tGradient 3.4110 tsec 14.8140 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.623476) vs oldopt=(dt=25.92,rms=0.623521) #GCMRL# 279 dt 36.288000 rms 0.623 0.000% neg 0 invalid 762 tFOTS 11.3140 tGradient 3.4240 tsec 15.5330 #GCMRL# 280 dt 36.288000 rms 0.623 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4330 tsec 4.1940 #GCMRL# 281 dt 36.288000 rms 0.623 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4500 tsec 4.2060 #GCMRL# 282 dt 36.288000 rms 0.623 0.059% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4880 tsec 4.3010 #GCMRL# 283 dt 36.288000 rms 0.622 0.075% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3830 tsec 4.1390 #GCMRL# 284 dt 36.288000 rms 0.622 0.091% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3690 tsec 4.1320 #GCMRL# 285 dt 36.288000 rms 0.621 0.104% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4240 tsec 4.2150 #GCMRL# 286 dt 36.288000 rms 0.620 0.118% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4410 tsec 4.2030 #GCMRL# 287 dt 36.288000 rms 0.619 0.127% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4000 tsec 4.2120 #GCMRL# 288 dt 36.288000 rms 0.619 0.134% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3940 tsec 4.1570 #GCMRL# 289 dt 36.288000 rms 0.618 0.140% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4370 tsec 4.1980 #GCMRL# 290 dt 36.288000 rms 0.617 0.143% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3710 tsec 4.1270 #GCMRL# 291 dt 36.288000 rms 0.616 0.144% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3980 tsec 4.1590 #GCMRL# 292 dt 36.288000 rms 0.615 0.146% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3740 tsec 4.1940 #GCMRL# 293 dt 36.288000 rms 0.614 0.145% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3740 tsec 4.1370 #GCMRL# 294 dt 36.288000 rms 0.613 0.144% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3970 tsec 4.1550 #GCMRL# 295 dt 36.288000 rms 0.612 0.142% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4240 tsec 4.1870 #GCMRL# 296 dt 36.288000 rms 0.612 0.137% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4150 tsec 4.1730 #GCMRL# 297 dt 36.288000 rms 0.611 0.133% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5020 tsec 4.3130 #GCMRL# 298 dt 36.288000 rms 0.610 0.130% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4090 tsec 4.1700 #GCMRL# 299 dt 36.288000 rms 0.609 0.130% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5020 tsec 4.2590 #GCMRL# 300 dt 36.288000 rms 0.608 0.125% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4560 tsec 4.2220 #GCMRL# 301 dt 36.288000 rms 0.608 0.118% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4130 tsec 4.1820 #GCMRL# 302 dt 36.288000 rms 0.607 0.117% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4020 tsec 4.1830 #GCMRL# 303 dt 36.288000 rms 0.606 0.115% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4210 tsec 4.1800 #GCMRL# 304 dt 36.288000 rms 0.606 0.113% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4140 tsec 4.1700 #GCMRL# 305 dt 36.288000 rms 0.605 0.111% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4150 tsec 4.1790 #GCMRL# 306 dt 36.288000 rms 0.604 0.103% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3850 tsec 4.1540 #GCMRL# 307 dt 36.288000 rms 0.604 0.102% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3990 tsec 4.1730 #GCMRL# 308 dt 36.288000 rms 0.603 0.096% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4160 tsec 4.2620 #GCMRL# 309 dt 36.288000 rms 0.603 0.094% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7220 tsec 4.5160 #GCMRL# 310 dt 36.288000 rms 0.602 0.090% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4540 tsec 4.2160 #GCMRL# 311 dt 36.288000 rms 0.602 0.086% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4270 tsec 4.2230 #GCMRL# 312 dt 36.288000 rms 0.601 0.087% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4120 tsec 4.1660 #GCMRL# 313 dt 36.288000 rms 0.600 0.084% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3800 tsec 4.1430 #GCMRL# 314 dt 36.288000 rms 0.600 0.082% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4630 tsec 4.2250 #GCMRL# 315 dt 36.288000 rms 0.600 0.080% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4550 tsec 4.2230 #GCMRL# 316 dt 36.288000 rms 0.599 0.081% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4530 tsec 4.2330 #GCMRL# 317 dt 36.288000 rms 0.599 0.079% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5000 tsec 4.2600 #GCMRL# 318 dt 36.288000 rms 0.598 0.079% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3950 tsec 4.1540 #GCMRL# 319 dt 36.288000 rms 0.598 0.009% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4230 tsec 4.4910 #GCMRL# 320 dt 36.288000 rms 0.598 0.013% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4230 tsec 4.2290 #GCMRL# 321 dt 36.288000 rms 0.598 0.021% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3680 tsec 4.1270 #GCMRL# 322 dt 36.288000 rms 0.598 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4930 tsec 4.2520 #GCMRL# 323 dt 36.288000 rms 0.598 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4400 tsec 4.2250 #GCMRL# 324 dt 36.288000 rms 0.597 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4570 tsec 4.2320 #GCMRL# 325 dt 36.288000 rms 0.597 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4540 tsec 4.2360 #GCMRL# 326 dt 36.288000 rms 0.597 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4230 tsec 4.1840 #GCMRL# 327 dt 36.288000 rms 0.597 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4240 tsec 4.1850 #GCMRL# 328 dt 36.288000 rms 0.596 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4620 tsec 4.2580 #GCMRL# 329 dt 36.288000 rms 0.596 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4790 tsec 4.2650 #GCMRL# 330 dt 36.288000 rms 0.596 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4080 tsec 4.1650 #GCMRL# 331 dt 36.288000 rms 0.596 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4000 tsec 4.1580 #GCMRL# 332 dt 36.288000 rms 0.595 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4070 tsec 4.1640 #GCMRL# 333 dt 36.288000 rms 0.595 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4420 tsec 4.1990 #GCMRL# 334 dt 36.288000 rms 0.595 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4020 tsec 4.1980 #GCMRL# 335 dt 36.288000 rms 0.595 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3820 tsec 4.1420 #GCMRL# 336 dt 36.288000 rms 0.594 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4290 tsec 4.1860 #GCMRL# 337 dt 36.288000 rms 0.594 0.052% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3880 tsec 4.1460 #GCMRL# 338 dt 36.288000 rms 0.594 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3940 tsec 4.1580 #GCMRL# 339 dt 36.288000 rms 0.594 0.006% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3860 tsec 4.4990 #GCMRL# 340 dt 18.144000 rms 0.594 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3980 tsec 4.8250 #FOTS# QuadFit found better minimum quadopt=(dt=9.072,rms=0.593547) vs oldopt=(dt=6.48,rms=0.59355) #GCMRL# 341 dt 9.072000 rms 0.594 0.000% neg 0 invalid 762 tFOTS 9.4840 tGradient 3.4610 tsec 13.7900 #GCMRL# 342 dt 4.536000 rms 0.594 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5400 tsec 5.0740 #GCMRL# 343 dt 1.134000 rms 0.594 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.9780 tsec 5.9300 #GCAMreg# pass 0 level1 4 level2 1 tsec 690.698 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 0.594537 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.591872) vs oldopt=(dt=103.68,rms=0.591887) #GCMRL# 345 dt 124.416000 rms 0.592 0.448% neg 0 invalid 762 tFOTS 11.5740 tGradient 3.7480 tsec 16.1270 #GCMRL# 346 dt 103.680000 rms 0.591 0.162% neg 0 invalid 762 tFOTS 10.7030 tGradient 3.6110 tsec 15.1190 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.590731) vs oldopt=(dt=25.92,rms=0.590761) #GCMRL# 347 dt 36.288000 rms 0.591 0.000% neg 0 invalid 762 tFOTS 12.0210 tGradient 3.5290 tsec 16.3810 #GCMRL# 348 dt 36.288000 rms 0.591 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5050 tsec 4.3330 #GCMRL# 349 dt 36.288000 rms 0.590 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4790 tsec 4.2830 #GCMRL# 350 dt 36.288000 rms 0.590 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5710 tsec 4.3700 #GCMRL# 351 dt 36.288000 rms 0.590 0.061% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5440 tsec 4.3360 #GCMRL# 352 dt 36.288000 rms 0.589 0.060% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6040 tsec 4.4550 #GCMRL# 353 dt 36.288000 rms 0.589 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5740 tsec 4.3750 #GCMRL# 354 dt 36.288000 rms 0.589 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6450 tsec 4.4920 #GCMRL# 355 dt 36.288000 rms 0.588 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8490 tsec 4.7670 #GCMRL# 356 dt 36.288000 rms 0.588 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7060 tsec 4.6170 #GCMRL# 357 dt 36.288000 rms 0.588 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7970 tsec 4.6390 #GCMRL# 358 dt 36.288000 rms 0.588 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6880 tsec 4.4990 #GCMRL# 359 dt 36.288000 rms 0.587 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.8510 tsec 4.7380 #GCMRL# 360 dt 36.288000 rms 0.587 0.052% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.7760 tsec 4.5840 #GCMRL# 361 dt 36.288000 rms 0.587 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6490 tsec 4.4520 #GCMRL# 362 dt 36.288000 rms 0.586 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5680 tsec 4.4290 #GCMRL# 363 dt 36.288000 rms 0.586 0.052% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4980 tsec 4.2790 #GCMRL# 364 dt 36.288000 rms 0.586 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4600 tsec 4.2260 #GCMRL# 365 dt 36.288000 rms 0.586 0.057% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4220 tsec 4.1820 #GCMRL# 366 dt 36.288000 rms 0.585 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4170 tsec 4.1780 #GCMRL# 367 dt 36.288000 rms 0.585 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4090 tsec 4.2160 #GCMRL# 368 dt 36.288000 rms 0.585 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3810 tsec 4.1400 #GCMRL# 369 dt 36.288000 rms 0.584 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3910 tsec 4.1480 #GCMRL# 370 dt 36.288000 rms 0.584 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3440 tsec 4.1020 #GCMRL# 371 dt 36.288000 rms 0.584 0.057% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4110 tsec 4.1700 #GCMRL# 372 dt 36.288000 rms 0.583 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3870 tsec 4.1840 #GCMRL# 373 dt 36.288000 rms 0.583 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5480 tsec 4.3080 #GCMRL# 374 dt 36.288000 rms 0.583 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4750 tsec 4.2390 #GCMRL# 375 dt 36.288000 rms 0.582 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4300 tsec 4.1850 #GCMRL# 376 dt 36.288000 rms 0.582 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4460 tsec 4.2130 #GCMRL# 377 dt 36.288000 rms 0.582 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3660 tsec 4.1680 #GCMRL# 378 dt 36.288000 rms 0.582 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3600 tsec 4.1210 #GCMRL# 379 dt 36.288000 rms 0.581 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3580 tsec 4.1160 #GCMRL# 380 dt 36.288000 rms 0.581 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4150 tsec 4.1740 #GCMRL# 381 dt 36.288000 rms 0.581 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4020 tsec 4.1620 #GCMRL# 382 dt 36.288000 rms 0.581 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3860 tsec 4.2010 #GCMRL# 383 dt 36.288000 rms 0.580 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3930 tsec 4.1540 #GCMRL# 384 dt 36.288000 rms 0.580 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3930 tsec 4.1510 #GCMRL# 385 dt 36.288000 rms 0.580 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3960 tsec 4.1520 #GCMRL# 386 dt 36.288000 rms 0.580 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3700 tsec 4.1300 #GCMRL# 387 dt 36.288000 rms 0.580 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4680 tsec 4.2870 #GCMRL# 388 dt 36.288000 rms 0.579 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4040 tsec 4.1590 #GCMRL# 389 dt 36.288000 rms 0.579 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3550 tsec 4.1190 #GCMRL# 390 dt 36.288000 rms 0.579 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3770 tsec 4.1360 #GCMRL# 391 dt 36.288000 rms 0.579 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3790 tsec 4.1360 #GCMRL# 392 dt 36.288000 rms 0.579 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3600 tsec 4.1640 #GCMRL# 393 dt 36.288000 rms 0.578 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3440 tsec 4.1060 #GCMRL# 394 dt 36.288000 rms 0.578 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3870 tsec 4.1450 #GCMRL# 395 dt 36.288000 rms 0.578 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3580 tsec 4.1200 #GCMRL# 396 dt 36.288000 rms 0.578 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3810 tsec 4.1390 #GCMRL# 397 dt 36.288000 rms 0.578 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3480 tsec 4.1450 #GCMRL# 398 dt 36.288000 rms 0.578 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3490 tsec 4.1210 #GCMRL# 399 dt 36.288000 rms 0.577 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3900 tsec 4.1500 #GCMRL# 400 dt 36.288000 rms 0.577 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3630 tsec 4.1240 #GCMRL# 401 dt 36.288000 rms 0.577 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3640 tsec 4.1260 #GCMRL# 402 dt 36.288000 rms 0.577 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3970 tsec 4.1710 #GCMRL# 403 dt 36.288000 rms 0.577 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3440 tsec 4.1330 #GCMRL# 404 dt 36.288000 rms 0.577 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3530 tsec 4.1060 #GCMRL# 405 dt 36.288000 rms 0.576 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3490 tsec 4.1110 #GCMRL# 406 dt 36.288000 rms 0.576 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3490 tsec 4.1000 #GCMRL# 407 dt 36.288000 rms 0.576 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3480 tsec 4.1130 #GCMRL# 408 dt 36.288000 rms 0.576 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3530 tsec 4.1480 #GCMRL# 409 dt 36.288000 rms 0.576 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3500 tsec 4.1150 #GCMRL# 410 dt 36.288000 rms 0.576 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3240 tsec 4.0770 #GCMRL# 411 dt 36.288000 rms 0.576 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3540 tsec 4.4120 #GCMRL# 412 dt 36.288000 rms 0.576 0.003% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3530 tsec 4.1330 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.575457) vs oldopt=(dt=103.68,rms=0.575465) #GCMRL# 413 dt 124.416000 rms 0.575 0.011% neg 0 invalid 762 tFOTS 9.3320 tGradient 3.3350 tsec 13.4240 #GCMRL# 414 dt 103.680000 rms 0.575 0.000% neg 0 invalid 762 tFOTS 9.3510 tGradient 3.4160 tsec 13.5360 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 0.588471 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.585086) vs oldopt=(dt=32,rms=0.585123) #GCMRL# 416 dt 38.400000 rms 0.585 0.575% neg 0 invalid 762 tFOTS 10.5910 tGradient 2.7890 tsec 14.1380 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.580711) vs oldopt=(dt=32,rms=0.581775) #GCMRL# 417 dt 44.800000 rms 0.581 0.748% neg 0 invalid 762 tFOTS 9.9560 tGradient 2.8900 tsec 13.6050 #FOTS# QuadFit found better minimum quadopt=(dt=124.433,rms=0.575312) vs oldopt=(dt=128,rms=0.57533) #GCMRL# 418 dt 124.432787 rms 0.575 0.930% neg 0 invalid 762 tFOTS 10.6970 tGradient 2.7520 tsec 14.2050 #FOTS# QuadFit found better minimum quadopt=(dt=34.8649,rms=0.569872) vs oldopt=(dt=32,rms=0.569888) #GCMRL# 419 dt 34.864947 rms 0.570 0.946% neg 0 invalid 762 tFOTS 9.9920 tGradient 2.9590 tsec 13.7090 #GCMRL# 420 dt 32.000000 rms 0.568 0.353% neg 0 invalid 762 tFOTS 10.5800 tGradient 2.8420 tsec 14.1800 #FOTS# QuadFit found better minimum quadopt=(dt=57.6508,rms=0.565131) vs oldopt=(dt=32,rms=0.565484) #GCMRL# 421 dt 57.650794 rms 0.565 0.480% neg 0 invalid 762 tFOTS 9.9910 tGradient 2.8920 tsec 13.6410 #FOTS# QuadFit found better minimum quadopt=(dt=22.8671,rms=0.563471) vs oldopt=(dt=32,rms=0.563837) #GCMRL# 422 dt 22.867102 rms 0.563 0.294% neg 0 invalid 762 tFOTS 10.5970 tGradient 2.8920 tsec 14.2500 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.561239) vs oldopt=(dt=32,rms=0.561705) #GCMRL# 423 dt 44.800000 rms 0.561 0.396% neg 0 invalid 762 tFOTS 10.0570 tGradient 2.8570 tsec 13.6710 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.560708) vs oldopt=(dt=8,rms=0.560834) #GCMRL# 424 dt 11.200000 rms 0.561 0.095% neg 0 invalid 762 tFOTS 9.3600 tGradient 2.8450 tsec 12.9650 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.560302) vs oldopt=(dt=8,rms=0.56041) #GCMRL# 425 dt 11.200000 rms 0.560 0.072% neg 0 invalid 762 tFOTS 9.3460 tGradient 2.8000 tsec 12.9200 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.559898) vs oldopt=(dt=8,rms=0.560007) #GCMRL# 426 dt 11.200000 rms 0.560 0.072% neg 0 invalid 762 tFOTS 9.3850 tGradient 2.7830 tsec 12.9280 #FOTS# QuadFit found better minimum quadopt=(dt=0.04375,rms=0.559905) vs oldopt=(dt=0.03125,rms=0.559906) #GCMRL# 427 dt 0.043750 rms 0.560 0.000% neg 0 invalid 762 tFOTS 6.8930 tGradient 2.8180 tsec 11.2250 #GCMRL# 428 dt 0.043750 rms 0.560 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7990 tsec 3.5710 #GCMRL# 429 dt 0.043750 rms 0.560 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7880 tsec 3.9040 #GCMRL# 430 dt 0.010937 rms 0.560 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7830 tsec 4.4980 #GCAMreg# pass 0 level1 3 level2 1 tsec 180.78 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 0.56087 #FOTS# QuadFit found better minimum quadopt=(dt=58.5,rms=0.55706) vs oldopt=(dt=32,rms=0.557462) #GCMRL# 432 dt 58.500000 rms 0.557 0.679% neg 0 invalid 762 tFOTS 10.6320 tGradient 2.7900 tsec 14.1900 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.554886) vs oldopt=(dt=32,rms=0.555118) #GCMRL# 433 dt 38.400000 rms 0.555 0.390% neg 0 invalid 762 tFOTS 10.0270 tGradient 2.8280 tsec 13.6190 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.554077) vs oldopt=(dt=32,rms=0.554113) #GCMRL# 434 dt 38.400000 rms 0.554 0.146% neg 0 invalid 762 tFOTS 10.6780 tGradient 2.7960 tsec 14.2380 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.553274) vs oldopt=(dt=8,rms=0.553447) #GCMRL# 435 dt 11.200000 rms 0.553 0.145% neg 0 invalid 762 tFOTS 9.3570 tGradient 2.8330 tsec 13.0030 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.552923) vs oldopt=(dt=8,rms=0.553024) #GCMRL# 436 dt 11.200000 rms 0.553 0.063% neg 0 invalid 762 tFOTS 9.3300 tGradient 2.8070 tsec 12.8950 #FOTS# QuadFit found better minimum quadopt=(dt=2.8,rms=0.552848) vs oldopt=(dt=2,rms=0.552871) #GCMRL# 437 dt 2.800000 rms 0.553 0.000% neg 0 invalid 762 tFOTS 8.7510 tGradient 2.8400 tsec 12.3670 #GCMRL# 438 dt 2.800000 rms 0.553 0.013% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8150 tsec 3.5710 #GCMRL# 439 dt 1.400000 rms 0.553 0.007% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8370 tsec 4.1940 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 0.580325 #GCMRL# 441 dt 0.000000 rms 0.579 0.150% neg 0 invalid 762 tFOTS 9.4210 tGradient 2.5540 tsec 12.7290 #GCMRL# 442 dt 0.150000 rms 0.579 0.000% neg 0 invalid 762 tFOTS 9.3470 tGradient 2.5930 tsec 13.3930 #GCAMreg# pass 0 level1 2 level2 1 tsec 33.41 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 0.580325 #GCMRL# 444 dt 0.000000 rms 0.579 0.150% neg 0 invalid 762 tFOTS 9.3320 tGradient 2.5840 tsec 12.7000 #GCMRL# 445 dt 0.150000 rms 0.579 0.000% neg 0 invalid 762 tFOTS 9.4280 tGradient 2.5620 tsec 13.4810 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 0.633277 #FOTS# QuadFit found better minimum quadopt=(dt=2.03727,rms=0.626514) vs oldopt=(dt=1.28,rms=0.627574) #GCMRL# 447 dt 2.037267 rms 0.627 1.068% neg 0 invalid 762 tFOTS 9.3790 tGradient 2.4220 tsec 12.5560 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.626425) vs oldopt=(dt=0.32,rms=0.626428) #GCMRL# 448 dt 0.384000 rms 0.626 0.000% neg 0 invalid 762 tFOTS 9.3910 tGradient 2.4120 tsec 12.5830 #GCAMreg# pass 0 level1 1 level2 1 tsec 32.307 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 0.62723 #FOTS# QuadFit found better minimum quadopt=(dt=1.024,rms=0.625871) vs oldopt=(dt=1.28,rms=0.625883) #GCMRL# 450 dt 1.024000 rms 0.626 0.217% neg 0 invalid 762 tFOTS 10.0310 tGradient 2.4050 tsec 13.2150 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.625868) vs oldopt=(dt=0.08,rms=0.625868) #GCMRL# 451 dt 0.112000 rms 0.626 0.000% neg 0 invalid 762 tFOTS 10.0470 tGradient 2.3960 tsec 13.2280 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 0.559766 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.540011) vs oldopt=(dt=0.32,rms=0.545219) #GCMRL# 453 dt 0.448000 rms 0.540 3.529% neg 0 invalid 762 tFOTS 9.4440 tGradient 1.9730 tsec 12.1770 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.535066) vs oldopt=(dt=0.32,rms=0.535872) #GCMRL# 454 dt 0.384000 rms 0.535 0.916% neg 0 invalid 762 tFOTS 9.3620 tGradient 1.9650 tsec 12.1010 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.531826) vs oldopt=(dt=0.32,rms=0.532357) #GCMRL# 455 dt 0.384000 rms 0.532 0.605% neg 0 invalid 762 tFOTS 9.4060 tGradient 1.9120 tsec 12.0800 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.529745) vs oldopt=(dt=0.32,rms=0.53008) #GCMRL# 456 dt 0.384000 rms 0.530 0.391% neg 0 invalid 762 tFOTS 9.3540 tGradient 1.9140 tsec 12.0230 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.528167) vs oldopt=(dt=0.32,rms=0.528426) #GCMRL# 457 dt 0.384000 rms 0.528 0.298% neg 0 invalid 762 tFOTS 9.4270 tGradient 1.9380 tsec 12.1270 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.527004) vs oldopt=(dt=0.32,rms=0.527184) #GCMRL# 458 dt 0.384000 rms 0.527 0.220% neg 0 invalid 762 tFOTS 9.3710 tGradient 1.9090 tsec 12.0330 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.526018) vs oldopt=(dt=0.32,rms=0.526177) #GCMRL# 459 dt 0.384000 rms 0.526 0.187% neg 0 invalid 762 tFOTS 9.4070 tGradient 1.9190 tsec 12.0810 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.525295) vs oldopt=(dt=0.32,rms=0.525405) #GCMRL# 460 dt 0.384000 rms 0.525 0.137% neg 0 invalid 762 tFOTS 9.3980 tGradient 1.9070 tsec 12.0660 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.524581) vs oldopt=(dt=0.32,rms=0.52469) #GCMRL# 461 dt 0.384000 rms 0.525 0.136% neg 0 invalid 762 tFOTS 9.4400 tGradient 1.9330 tsec 12.1380 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.524091) vs oldopt=(dt=0.32,rms=0.524161) #GCMRL# 462 dt 0.384000 rms 0.524 0.094% neg 0 invalid 762 tFOTS 9.4930 tGradient 1.8870 tsec 12.1400 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.52355) vs oldopt=(dt=0.32,rms=0.523629) #GCMRL# 463 dt 0.384000 rms 0.524 0.103% neg 0 invalid 762 tFOTS 9.3970 tGradient 1.9540 tsec 12.1120 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.523193) vs oldopt=(dt=0.32,rms=0.52324) #GCMRL# 464 dt 0.384000 rms 0.523 0.068% neg 0 invalid 762 tFOTS 9.3980 tGradient 1.8950 tsec 12.0520 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.522768) vs oldopt=(dt=0.32,rms=0.522828) #GCMRL# 465 dt 0.384000 rms 0.523 0.081% neg 0 invalid 762 tFOTS 9.4050 tGradient 1.9410 tsec 12.1200 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.522524) vs oldopt=(dt=0.32,rms=0.522554) #GCMRL# 466 dt 0.384000 rms 0.523 0.000% neg 0 invalid 762 tFOTS 9.3420 tGradient 1.8920 tsec 12.0070 #GCMRL# 467 dt 0.384000 rms 0.522 0.067% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9250 tsec 2.6990 #GCMRL# 468 dt 0.384000 rms 0.522 0.099% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9210 tsec 2.6960 #GCMRL# 469 dt 0.384000 rms 0.521 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9450 tsec 3.0610 #GCMRL# 470 dt 0.384000 rms 0.521 0.082% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9180 tsec 2.8210 #GCMRL# 471 dt 0.192000 rms 0.521 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9320 tsec 3.2920 #GCMRL# 472 dt 0.192000 rms 0.521 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9240 tsec 2.6860 #GCMRL# 473 dt 0.192000 rms 0.520 0.047% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9100 tsec 2.6800 #GCMRL# 474 dt 0.192000 rms 0.520 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9030 tsec 2.6920 #GCMRL# 475 dt 0.192000 rms 0.520 0.014% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8910 tsec 2.9790 #GCMRL# 476 dt 0.192000 rms 0.520 0.015% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8890 tsec 2.6520 #GCMRL# 477 dt 0.192000 rms 0.520 0.018% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9940 tsec 2.7580 #GCMRL# 478 dt 0.192000 rms 0.520 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9720 tsec 2.7360 #GCMRL# 479 dt 0.192000 rms 0.520 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8990 tsec 2.6960 #GCMRL# 480 dt 0.192000 rms 0.519 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9010 tsec 2.6580 #GCMRL# 481 dt 0.192000 rms 0.519 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9190 tsec 2.6870 #GCMRL# 482 dt 0.192000 rms 0.519 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9200 tsec 2.6840 #GCMRL# 483 dt 0.192000 rms 0.519 0.010% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9120 tsec 2.9850 #GCMRL# 484 dt 0.192000 rms 0.519 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8870 tsec 2.7060 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.518943) vs oldopt=(dt=0.08,rms=0.518944) #GCMRL# 485 dt 0.112000 rms 0.519 0.000% neg 0 invalid 762 tFOTS 8.8060 tGradient 1.8780 tsec 11.4580 #GCMRL# 486 dt 0.112000 rms 0.519 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9090 tsec 2.7100 #GCMRL# 487 dt 0.112000 rms 0.519 0.003% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8910 tsec 2.7260 #GCMRL# 488 dt 0.112000 rms 0.519 0.008% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9000 tsec 2.6660 #GCMRL# 489 dt 0.112000 rms 0.519 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9820 tsec 2.7400 #GCMRL# 490 dt 0.112000 rms 0.519 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9600 tsec 2.7210 #GCMRL# 491 dt 0.112000 rms 0.519 0.007% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8830 tsec 2.6840 #GCAMreg# pass 0 level1 0 level2 1 tsec 253.072 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 0.51975 #GCMRL# 493 dt 0.320000 rms 0.516 0.764% neg 0 invalid 762 tFOTS 9.3170 tGradient 1.9090 tsec 11.9890 #GCMRL# 494 dt 0.320000 rms 0.515 0.187% neg 0 invalid 762 tFOTS 9.3650 tGradient 1.9470 tsec 12.0730 #GCMRL# 495 dt 0.320000 rms 0.515 0.054% neg 0 invalid 762 tFOTS 9.3690 tGradient 1.8800 tsec 12.0060 #GCMRL# 496 dt 0.320000 rms 0.514 0.000% neg 0 invalid 762 tFOTS 9.4630 tGradient 1.9290 tsec 12.1660 GCAMregister done in 47.6136 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 0.512581 #FOTS# QuadFit found better minimum quadopt=(dt=8.092,rms=0.511584) vs oldopt=(dt=5.78,rms=0.511585) #GCMRL# 498 dt 8.092000 rms 0.512 0.194% neg 0 invalid 762 tFOTS 11.8750 tGradient 4.4770 tsec 17.1130 #GCMRL# 499 dt 5.780000 rms 0.512 0.000% neg 0 invalid 762 tFOTS 11.8480 tGradient 4.5050 tsec 17.1290 #GCMRL# 500 dt 5.780000 rms 0.512 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.5000 tsec 5.2540 #GCAMreg# pass 0 level1 5 level2 1 tsec 48.719 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 0.51257 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.510833) vs oldopt=(dt=369.92,rms=0.510922) #GCMRL# 502 dt 295.936000 rms 0.511 0.339% neg 0 invalid 762 tFOTS 11.2390 tGradient 4.5400 tsec 16.5340 #FOTS# QuadFit found better minimum quadopt=(dt=32.368,rms=0.510705) vs oldopt=(dt=23.12,rms=0.510734) #GCMRL# 503 dt 32.368000 rms 0.511 0.000% neg 0 invalid 762 tFOTS 11.8340 tGradient 4.5230 tsec 17.1560 #GCMRL# 504 dt 32.368000 rms 0.511 0.010% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.4810 tsec 5.2410 #GCMRL# 505 dt 32.368000 rms 0.511 0.006% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.4510 tsec 5.2110 #GCMRL# 506 dt 32.368000 rms 0.511 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.4610 tsec 5.2330 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.510178) vs oldopt=(dt=369.92,rms=0.510221) #GCMRL# 507 dt 295.936000 rms 0.510 0.087% neg 0 invalid 762 tFOTS 11.3170 tGradient 4.4390 tsec 16.5660 #FOTS# QuadFit found better minimum quadopt=(dt=32.368,rms=0.510122) vs oldopt=(dt=23.12,rms=0.51013) 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 0.511155 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.509186) vs oldopt=(dt=25.92,rms=0.509343) iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 509 dt 36.288000 rms 0.509 0.385% neg 0 invalid 762 tFOTS 11.9300 tGradient 3.2810 tsec 16.6180 #FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.508436) vs oldopt=(dt=103.68,rms=0.508531) iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 510 dt 82.944000 rms 0.508 0.000% neg 0 invalid 762 tFOTS 11.3060 tGradient 3.3450 tsec 16.0900 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 511 dt 82.944000 rms 0.507 0.232% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3580 tsec 4.7900 iter 0, gcam->neg = 6 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 512 dt 82.944000 rms 0.506 0.184% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3380 tsec 5.4130 iter 0, gcam->neg = 11 after 5 iterations, nbhd size=0, neg = 0 #GCMRL# 513 dt 82.944000 rms 0.506 0.123% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2860 tsec 6.3590 iter 0, gcam->neg = 14 after 11 iterations, nbhd size=1, neg = 0 #GCMRL# 514 dt 82.944000 rms 0.505 0.067% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2940 tsec 8.2950 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.504261) vs oldopt=(dt=103.68,rms=0.50433) #GCMRL# 515 dt 145.152000 rms 0.504 0.218% neg 0 invalid 762 tFOTS 11.2010 tGradient 3.2970 tsec 15.2520 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.503468) vs oldopt=(dt=25.92,rms=0.50351) iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 516 dt 36.288000 rms 0.503 0.000% neg 0 invalid 762 tFOTS 11.8980 tGradient 3.3330 tsec 16.6470 iter 0, gcam->neg = 1 after 1 iterations, nbhd size=0, neg = 0 #GCMRL# 517 dt 36.288000 rms 0.503 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3300 tsec 5.0970 #GCMRL# 518 dt 36.288000 rms 0.503 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3500 tsec 4.1100 iter 0, gcam->neg = 1 after 4 iterations, nbhd size=0, neg = 0 #GCMRL# 519 dt 36.288000 rms 0.503 0.063% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3150 tsec 6.0310 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 520 dt 36.288000 rms 0.502 0.080% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3040 tsec 4.7050 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 521 dt 36.288000 rms 0.502 0.077% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3120 tsec 4.7100 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCAMreg# pass 0 level1 4 level2 1 tsec 122.138 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 0.502611 #FOTS# QuadFit found better minimum quadopt=(dt=118.545,rms=0.499245) vs oldopt=(dt=103.68,rms=0.499273) #GCMRL# 523 dt 118.545455 rms 0.499 0.670% neg 0 invalid 762 tFOTS 11.8810 tGradient 3.3340 tsec 15.9710 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.4987) vs oldopt=(dt=25.92,rms=0.498753) #GCMRL# 524 dt 36.288000 rms 0.499 0.000% neg 0 invalid 762 tFOTS 11.8370 tGradient 3.3110 tsec 15.9330 #GCMRL# 525 dt 36.288000 rms 0.498 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3310 tsec 4.1730 #GCMRL# 526 dt 36.288000 rms 0.498 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3230 tsec 4.0810 #GCMRL# 527 dt 36.288000 rms 0.498 0.088% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3060 tsec 4.0620 #GCMRL# 528 dt 36.288000 rms 0.497 0.084% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3300 tsec 4.0850 #GCMRL# 529 dt 36.288000 rms 0.497 0.084% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3280 tsec 4.1070 #FOTS# QuadFit found better minimum quadopt=(dt=248.832,rms=0.49663) vs oldopt=(dt=414.72,rms=0.496708) 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 0.498298 #FOTS# QuadFit found better minimum quadopt=(dt=55.1456,rms=0.492989) vs oldopt=(dt=32,rms=0.493579) iter 0, gcam->neg = 34 after 9 iterations, nbhd size=0, neg = 0 #GCMRL# 531 dt 55.145631 rms 0.493 1.060% neg 0 invalid 762 tFOTS 11.8540 tGradient 2.7770 tsec 18.9990 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.489771) vs oldopt=(dt=32,rms=0.489984) iter 0, gcam->neg = 8 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 532 dt 44.800000 rms 0.490 0.656% neg 0 invalid 762 tFOTS 12.1410 tGradient 2.8420 tsec 17.3420 #FOTS# QuadFit found better minimum quadopt=(dt=23.5789,rms=0.487493) vs oldopt=(dt=32,rms=0.487826) iter 0, gcam->neg = 6 after 12 iterations, nbhd size=1, neg = 0 #GCMRL# 533 dt 23.578947 rms 0.488 0.455% neg 0 invalid 762 tFOTS 11.8580 tGradient 2.7280 tsec 19.9530 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.486527) vs oldopt=(dt=32,rms=0.486566) iter 0, gcam->neg = 2 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 534 dt 25.600000 rms 0.487 0.000% neg 0 invalid 762 tFOTS 11.8710 tGradient 2.7660 tsec 17.0580 iter 0, gcam->neg = 3 after 8 iterations, nbhd size=1, neg = 0 #GCMRL# 535 dt 25.600000 rms 0.485 0.212% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7780 tsec 6.8110 iter 0, gcam->neg = 5 after 7 iterations, nbhd size=1, neg = 0 #GCMRL# 536 dt 25.600000 rms 0.484 0.354% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7680 tsec 6.5260 iter 0, gcam->neg = 10 after 13 iterations, nbhd size=1, neg = 0 #GCMRL# 537 dt 25.600000 rms 0.482 0.344% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7760 tsec 8.4740 iter 0, gcam->neg = 9 after 17 iterations, nbhd size=1, neg = 0 #GCMRL# 538 dt 25.600000 rms 0.480 0.476% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7760 tsec 9.7560 iter 0, gcam->neg = 26 after 13 iterations, nbhd size=1, neg = 0 #GCMRL# 539 dt 25.600000 rms 0.478 0.337% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7960 tsec 8.4940 iter 0, gcam->neg = 22 after 9 iterations, nbhd size=0, neg = 0 #GCMRL# 540 dt 25.600000 rms 0.476 0.368% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8030 tsec 7.1750 iter 0, gcam->neg = 34 after 14 iterations, nbhd size=1, neg = 0 #GCMRL# 541 dt 25.600000 rms 0.475 0.272% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7900 tsec 8.7960 iter 0, gcam->neg = 43 after 15 iterations, nbhd size=1, neg = 0 #GCMRL# 542 dt 25.600000 rms 0.474 0.326% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7830 tsec 9.1370 iter 0, gcam->neg = 46 after 15 iterations, nbhd size=1, neg = 0 #GCMRL# 543 dt 25.600000 rms 0.473 0.150% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7540 tsec 9.2560 iter 0, gcam->neg = 45 after 9 iterations, nbhd size=0, neg = 0 #GCMRL# 544 dt 25.600000 rms 0.472 0.208% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8160 tsec 7.1640 iter 0, gcam->neg = 44 after 17 iterations, nbhd size=1, neg = 0 #GCMRL# 545 dt 25.600000 rms 0.471 0.129% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7540 tsec 9.7250 iter 0, gcam->neg = 56 after 21 iterations, nbhd size=1, neg = 0 #GCMRL# 546 dt 25.600000 rms 0.471 0.161% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7610 tsec 11.0440 iter 0, gcam->neg = 12 after 13 iterations, nbhd size=1, neg = 0 #GCMRL# 547 dt 25.600000 rms 0.470 0.105% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7670 tsec 8.4400 iter 0, gcam->neg = 19 after 19 iterations, nbhd size=1, neg = 0 #GCMRL# 548 dt 25.600000 rms 0.469 0.118% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7670 tsec 10.4900 iter 0, gcam->neg = 18 after 12 iterations, nbhd size=0, neg = 0 #GCMRL# 549 dt 25.600000 rms 0.469 0.153% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7630 tsec 8.1070 iter 0, gcam->neg = 30 after 16 iterations, nbhd size=1, neg = 0 #GCMRL# 550 dt 25.600000 rms 0.468 0.087% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7650 tsec 9.5180 iter 0, gcam->neg = 39 after 18 iterations, nbhd size=1, neg = 0 #GCMRL# 551 dt 25.600000 rms 0.468 0.011% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7820 tsec 10.0720 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.467926) vs oldopt=(dt=8,rms=0.467995) iter 0, gcam->neg = 2 after 10 iterations, nbhd size=1, neg = 0 #GCMRL# 552 dt 11.200000 rms 0.468 0.087% neg 0 invalid 762 tFOTS 11.8320 tGradient 2.7410 tsec 19.2450 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.46754) vs oldopt=(dt=32,rms=0.467568) iter 0, gcam->neg = 5 after 9 iterations, nbhd size=1, neg = 0 #GCMRL# 553 dt 44.800000 rms 0.468 0.000% neg 0 invalid 762 tFOTS 11.2110 tGradient 2.7880 tsec 18.4100 iter 0, gcam->neg = 3 after 3 iterations, nbhd size=0, neg = 0 #GCAMreg# pass 0 level1 3 level2 1 tsec 269.075 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 0.468895 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.465187) vs oldopt=(dt=32,rms=0.46524) #GCMRL# 555 dt 38.400000 rms 0.465 0.791% neg 0 invalid 762 tFOTS 11.8180 tGradient 2.7870 tsec 15.3620 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.464514) vs oldopt=(dt=8,rms=0.464657) #GCMRL# 556 dt 11.200000 rms 0.465 0.000% neg 0 invalid 762 tFOTS 11.9010 tGradient 2.8000 tsec 15.4770 #GCMRL# 557 dt 11.200000 rms 0.464 0.090% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7690 tsec 3.5240 #GCMRL# 558 dt 11.200000 rms 0.464 0.079% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7800 tsec 3.5370 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 559 dt 11.200000 rms 0.463 0.119% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7700 tsec 4.1660 iter 0, gcam->neg = 3 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 560 dt 11.200000 rms 0.463 0.111% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8260 tsec 4.3160 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.462482) vs oldopt=(dt=32,rms=0.462484) 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 0.473234 #FOTS# QuadFit found better minimum quadopt=(dt=1.728,rms=0.471925) vs oldopt=(dt=2.88,rms=0.471965) #GCMRL# 562 dt 1.728000 rms 0.472 0.277% neg 0 invalid 762 tFOTS 11.9240 tGradient 2.5150 tsec 15.2000 #GCMRL# 563 dt 0.720000 rms 0.472 0.000% neg 0 invalid 762 tFOTS 11.9090 tGradient 2.5290 tsec 15.2170 #GCMRL# 564 dt 0.720000 rms 0.472 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5610 tsec 3.3400 #GCAMreg# pass 0 level1 2 level2 1 tsec 40.987 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 0.473027 #FOTS# QuadFit found better minimum quadopt=(dt=2.304,rms=0.471775) vs oldopt=(dt=2.88,rms=0.471785) #GCMRL# 566 dt 2.304000 rms 0.472 0.265% neg 0 invalid 762 tFOTS 11.9160 tGradient 2.5480 tsec 15.2990 #FOTS# QuadFit found better minimum quadopt=(dt=1.008,rms=0.471745) vs oldopt=(dt=0.72,rms=0.471749) #GCMRL# 567 dt 1.008000 rms 0.472 0.000% neg 0 invalid 762 tFOTS 11.9060 tGradient 2.5330 tsec 15.2220 #GCMRL# 568 dt 1.008000 rms 0.472 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5410 tsec 3.3040 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 0.487368 #FOTS# QuadFit found better minimum quadopt=(dt=0.768,rms=0.485694) vs oldopt=(dt=1.28,rms=0.48588) #GCMRL# 570 dt 0.768000 rms 0.486 0.343% neg 0 invalid 762 tFOTS 11.9080 tGradient 2.3980 tsec 15.0820 #GCMRL# 571 dt 0.320000 rms 0.486 0.000% neg 0 invalid 762 tFOTS 11.9270 tGradient 2.4360 tsec 15.1790 #GCAMreg# pass 0 level1 1 level2 1 tsec 37.451 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 0.486673 #FOTS# QuadFit found better minimum quadopt=(dt=1.024,rms=0.485148) vs oldopt=(dt=1.28,rms=0.485213) #GCMRL# 573 dt 1.024000 rms 0.485 0.313% neg 0 invalid 762 tFOTS 11.9430 tGradient 2.3960 tsec 15.1150 #GCMRL# 574 dt 0.320000 rms 0.485 0.000% neg 0 invalid 762 tFOTS 11.9580 tGradient 2.3180 tsec 15.0520 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 0.47151 #FOTS# QuadFit found better minimum quadopt=(dt=2.14159,rms=0.432104) vs oldopt=(dt=1.28,rms=0.438562) iter 0, gcam->neg = 1743 after 19 iterations, nbhd size=1, neg = 0 #GCMRL# 576 dt 2.141585 rms 0.435 7.683% neg 0 invalid 762 tFOTS 11.9140 tGradient 1.9220 tsec 21.6090 #FOTS# QuadFit found better minimum quadopt=(dt=0.028,rms=0.435263) vs oldopt=(dt=0.02,rms=0.435266) #GCMRL# 577 dt 0.028000 rms 0.435 0.000% neg 0 invalid 762 tFOTS 11.8760 tGradient 1.9100 tsec 14.5970 #GCAMreg# pass 0 level1 0 level2 1 tsec 42.792 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 0.436515 #FOTS# QuadFit found better minimum quadopt=(dt=0.048,rms=0.435153) vs oldopt=(dt=0.08,rms=0.435175) #GCMRL# 579 dt 0.048000 rms 0.435 0.312% neg 0 invalid 762 tFOTS 11.9440 tGradient 1.9480 tsec 14.6450 #FOTS# QuadFit found better minimum quadopt=(dt=0.007,rms=0.43514) vs oldopt=(dt=0.005,rms=0.43514) #GCMRL# 580 dt 0.007000 rms 0.435 0.000% neg 0 invalid 762 tFOTS 11.9130 tGradient 1.9220 tsec 14.6610 #GCMRL# 581 dt 0.007000 rms 0.435 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9060 tsec 2.6710 label assignment complete, 0 changed (0.00%) GCAMregister done in 14.9166 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 0.419589 #GCAMreg# pass 0 level1 5 level2 1 tsec 19.66 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 0.419589 #FOTS# QuadFit found better minimum quadopt=(dt=73.984,rms=0.419481) vs oldopt=(dt=92.48,rms=0.419492) #GCMRL# 584 dt 73.984000 rms 0.419 0.026% neg 0 invalid 762 tFOTS 10.9270 tGradient 4.0610 tsec 15.7290 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.419422) vs oldopt=(dt=92.48,rms=0.419426) #GCMRL# 585 dt 129.472000 rms 0.419 0.000% neg 0 invalid 762 tFOTS 11.0830 tGradient 4.0670 tsec 15.9140 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 0.41962 #FOTS# QuadFit found better minimum quadopt=(dt=9.072,rms=0.4196) vs oldopt=(dt=6.48,rms=0.419602) #GCMRL# 587 dt 9.072000 rms 0.420 0.005% neg 0 invalid 762 tFOTS 11.5880 tGradient 2.9420 tsec 15.2730 #FOTS# QuadFit found better minimum quadopt=(dt=3.888,rms=0.419597) vs oldopt=(dt=6.48,rms=0.419597) #GCMRL# 588 dt 3.888000 rms 0.420 0.000% neg 0 invalid 762 tFOTS 11.5880 tGradient 2.8850 tsec 15.2340 #GCAMreg# pass 0 level1 4 level2 1 tsec 37.995 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 0.419597 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.419059) vs oldopt=(dt=103.68,rms=0.419077) #GCMRL# 590 dt 124.416000 rms 0.419 0.128% neg 0 invalid 762 tFOTS 10.9710 tGradient 2.8860 tsec 14.6050 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.418876) vs oldopt=(dt=25.92,rms=0.418912) #GCMRL# 591 dt 36.288000 rms 0.419 0.000% neg 0 invalid 762 tFOTS 11.0310 tGradient 2.8900 tsec 14.6890 #GCMRL# 592 dt 36.288000 rms 0.419 0.021% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8810 tsec 3.6380 #GCMRL# 593 dt 36.288000 rms 0.419 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8700 tsec 3.6570 #GCMRL# 594 dt 36.288000 rms 0.418 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8570 tsec 3.6470 #GCMRL# 595 dt 36.288000 rms 0.418 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8740 tsec 3.6510 #GCMRL# 596 dt 36.288000 rms 0.418 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8710 tsec 3.6340 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 0.418831 iter 0, gcam->neg = 5 after 10 iterations, nbhd size=1, neg = 0 #GCMRL# 598 dt 32.000000 rms 0.418 0.225% neg 0 invalid 762 tFOTS 11.6190 tGradient 2.3050 tsec 18.5730 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.416937) vs oldopt=(dt=32,rms=0.4171) iter 0, gcam->neg = 26 after 13 iterations, nbhd size=1, neg = 0 #GCMRL# 599 dt 44.800000 rms 0.417 0.000% neg 0 invalid 762 tFOTS 11.6180 tGradient 2.3460 tsec 19.6570 iter 0, gcam->neg = 20 after 16 iterations, nbhd size=1, neg = 0 #GCMRL# 600 dt 44.800000 rms 0.417 0.060% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3140 tsec 8.9560 iter 0, gcam->neg = 71 after 18 iterations, nbhd size=1, neg = 0 #GCMRL# 601 dt 44.800000 rms 0.417 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3460 tsec 9.6770 iter 0, gcam->neg = 109 after 32 iterations, nbhd size=1, neg = 0 #GCAMreg# pass 0 level1 3 level2 1 tsec 75.243 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 0.41662 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.413873) vs oldopt=(dt=32,rms=0.414194) iter 0, gcam->neg = 17 after 12 iterations, nbhd size=1, neg = 0 #GCMRL# 603 dt 44.800000 rms 0.414 0.638% neg 0 invalid 762 tFOTS 11.6230 tGradient 2.3550 tsec 19.3940 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.412881) vs oldopt=(dt=32,rms=0.412963) iter 0, gcam->neg = 7 after 11 iterations, nbhd size=1, neg = 0 #GCMRL# 604 dt 44.800000 rms 0.413 0.261% neg 0 invalid 762 tFOTS 11.6400 tGradient 2.3130 tsec 18.9220 iter 0, gcam->neg = 3 after 1 iterations, nbhd size=0, neg = 0 #GCMRL# 605 dt 32.000000 rms 0.412 0.000% neg 0 invalid 762 tFOTS 11.5460 tGradient 2.3260 tsec 15.6120 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 606 dt 32.000000 rms 0.412 0.080% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3210 tsec 3.7500 iter 0, gcam->neg = 11 after 14 iterations, nbhd size=1, neg = 0 #GCMRL# 607 dt 32.000000 rms 0.411 0.098% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3070 tsec 8.2990 iter 0, gcam->neg = 18 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 608 dt 32.000000 rms 0.411 0.185% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3310 tsec 4.6760 iter 0, gcam->neg = 15 after 14 iterations, nbhd size=1, neg = 0 #GCMRL# 609 dt 32.000000 rms 0.410 0.136% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3670 tsec 8.4420 iter 0, gcam->neg = 18 after 10 iterations, nbhd size=1, neg = 0 #GCMRL# 610 dt 32.000000 rms 0.409 0.171% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3110 tsec 6.9880 iter 0, gcam->neg = 17 after 16 iterations, nbhd size=1, neg = 0 #GCMRL# 611 dt 32.000000 rms 0.409 0.143% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3090 tsec 8.9190 iter 0, gcam->neg = 35 after 15 iterations, nbhd size=1, neg = 0 #GCMRL# 612 dt 32.000000 rms 0.408 0.167% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3460 tsec 8.6680 iter 0, gcam->neg = 35 after 17 iterations, nbhd size=1, neg = 0 #GCMRL# 613 dt 32.000000 rms 0.408 0.112% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3270 tsec 9.2540 iter 0, gcam->neg = 32 after 16 iterations, nbhd size=1, neg = 0 #GCMRL# 614 dt 32.000000 rms 0.407 0.128% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3200 tsec 8.9340 iter 0, gcam->neg = 44 after 15 iterations, nbhd size=1, neg = 0 #GCMRL# 615 dt 32.000000 rms 0.407 0.094% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3340 tsec 8.6320 iter 0, gcam->neg = 52 after 9 iterations, nbhd size=0, neg = 0 #GCMRL# 616 dt 32.000000 rms 0.406 0.101% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3080 tsec 6.6420 iter 0, gcam->neg = 85 after 15 iterations, nbhd size=1, neg = 0 #GCMRL# 617 dt 32.000000 rms 0.406 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3910 tsec 8.7030 #FOTS# QuadFit found better minimum quadopt=(dt=19.2,rms=0.405969) vs oldopt=(dt=32,rms=0.405988) iter 0, gcam->neg = 11 after 25 iterations, nbhd size=2, neg = 0 setting smoothness cost coefficient to 0.400 #GCAMreg# pass 0 level1 2 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.40 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.411133 iter 0, gcam->neg = 9 after 28 iterations, nbhd size=3, neg = 0 #GCAMreg# pass 0 level1 2 level2 1 tsec 28.161 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 0.411133 #FOTS# QuadFit found better minimum quadopt=(dt=4.032,rms=0.410948) vs oldopt=(dt=2.88,rms=0.410963) iter 0, gcam->neg = 8 after 28 iterations, nbhd size=3, neg = 0 setting smoothness cost coefficient to 1.000 #GCAMreg# pass 0 level1 1 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=1.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.421766 #FOTS# QuadFit found better minimum quadopt=(dt=0.028,rms=0.421765) vs oldopt=(dt=0.02,rms=0.421765) iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 621 dt 0.028000 rms 0.422 0.001% neg 0 invalid 762 tFOTS 11.6820 tGradient 1.9350 tsec 15.0230 #FOTS# QuadFit found better minimum quadopt=(dt=6.25e-05,rms=0.421762) vs oldopt=(dt=7.8125e-05,rms=0.421762) #GCMRL# 622 dt 0.000063 rms 0.422 0.000% neg 0 invalid 762 tFOTS 14.7430 tGradient 1.9500 tsec 17.4490 #GCAMreg# pass 0 level1 1 level2 1 tsec 39.058 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 0.421762 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.421622) vs oldopt=(dt=0.32,rms=0.421632) iter 0, gcam->neg = 2 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 624 dt 0.448000 rms 0.422 0.033% neg 0 invalid 762 tFOTS 11.7950 tGradient 1.9350 tsec 15.1410 #FOTS# QuadFit found better minimum quadopt=(dt=0.096,rms=0.421618) vs oldopt=(dt=0.08,rms=0.421619) #GCMRL# 625 dt 0.096000 rms 0.422 0.000% neg 0 invalid 762 tFOTS 11.6700 tGradient 1.9410 tsec 14.3800 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 0.405772 #FOTS# QuadFit found better minimum quadopt=(dt=0.893322,rms=0.395944) vs oldopt=(dt=1.28,rms=0.397913) iter 0, gcam->neg = 1084 after 29 iterations, nbhd size=1, neg = 0 #GCMRL# 627 dt 0.893322 rms 0.398 1.794% neg 0 invalid 762 tFOTS 11.6380 tGradient 1.5010 tsec 24.1450 #GCMRL# 628 dt 0.005000 rms 0.398 0.000% neg 0 invalid 762 tFOTS 11.6920 tGradient 1.4960 tsec 13.9510 #GCAMreg# pass 0 level1 0 level2 1 tsec 44.254 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 0.398492 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.398102) vs oldopt=(dt=0.08,rms=0.398171) #GCMRL# 630 dt 0.112000 rms 0.398 0.098% neg 0 invalid 762 tFOTS 11.6360 tGradient 1.5220 tsec 13.9150 #FOTS# QuadFit found better minimum quadopt=(dt=0.256,rms=0.397647) vs oldopt=(dt=0.32,rms=0.397673) #GCMRL# 631 dt 0.256000 rms 0.398 0.114% neg 0 invalid 762 tFOTS 11.6210 tGradient 1.5210 tsec 13.9000 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.396663) vs oldopt=(dt=0.32,rms=0.396789) iter 0, gcam->neg = 32 after 3 iterations, nbhd size=0, neg = 0 #GCMRL# 632 dt 0.448000 rms 0.397 0.228% neg 0 invalid 762 tFOTS 11.6080 tGradient 1.5130 tsec 15.5140 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.396205) vs oldopt=(dt=0.32,rms=0.396216) iter 0, gcam->neg = 29 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 633 dt 0.384000 rms 0.396 0.000% neg 0 invalid 762 tFOTS 11.6100 tGradient 1.6230 tsec 15.2770 iter 0, gcam->neg = 18 after 1 iterations, nbhd size=0, neg = 0 #GCMRL# 634 dt 0.384000 rms 0.395 0.188% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.5350 tsec 3.2490 iter 0, gcam->neg = 75 after 13 iterations, nbhd size=1, neg = 0 #GCMRL# 635 dt 0.384000 rms 0.395 0.131% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.5250 tsec 7.1390 iter 0, gcam->neg = 128 after 15 iterations, nbhd size=1, neg = 0 #GCMRL# 636 dt 0.384000 rms 0.395 -0.007% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.5270 tsec 8.5140 #FOTS# QuadFit found better minimum quadopt=(dt=0.256,rms=0.394668) vs oldopt=(dt=0.32,rms=0.394675) iter 0, gcam->neg = 4 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 637 dt 0.256000 rms 0.395 0.072% neg 0 invalid 762 tFOTS 11.5500 tGradient 1.5050 tsec 14.4470 iter 0, gcam->neg = 4 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 638 dt 0.320000 rms 0.394 0.105% neg 0 invalid 762 tFOTS 11.6510 tGradient 1.4920 tsec 14.5310 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.394164) vs oldopt=(dt=0.08,rms=0.394178) GCAMregister done in 11.5842 min writing output transformation to transforms/talairach.m3z... GCAMwrite Calls to gcamLogLikelihoodEnergy 4353 tmin = 10.8555 Calls to gcamLabelEnergy 3718 tmin = 0.956883 Calls to gcamJacobianEnergy 4353 tmin = 9.62028 Calls to gcamSmoothnessEnergy 4353 tmin = 10.4856 Calls to gcamLogLikelihoodTerm 640 tmin = 3.30187 Calls to gcamLabelTerm 583 tmin = 4.77587 Calls to gcamJacobianTerm 640 tmin = 8.70865 Calls to gcamSmoothnessTerm 640 tmin = 2.74077 Calls to gcamComputeGradient 640 tmin = 37.5523 Calls to gcamComputeMetricProperties 6836 tmin = 13.6848 mri_ca_register took 1 hours, 34 minutes and 44 seconds. #VMPC# mri_ca_register VmPeak 2018612 FSRUNTIME@ mri_ca_register 1.5790 hours 1 threads @#@FSTIME 2023:06:14:16:32:02 mri_ca_register N 9 e 5684.35 S 3.17 U 5681.57 P 100% M 1321148 F 19 R 1560907 W 0 c 34368 w 4561 I 2880 O 0 L 1.11 1.09 0.98 @#@FSLOADPOST 2023:06:14:18:06:46 mri_ca_register N 9 1.02 1.04 1.00 #-------------------------------------- #@# SubCort Seg Wed Jun 14 18:06:46 BST 2023 mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /home/varun/freesurfer/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz sysname Linux hostname Ubuntu machine x86_64 setenv SUBJECTS_DIR /media/sf_VBOX/T1_FreeSurfer cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /home/varun/freesurfer/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz relabeling unlikely voxels with window_size = 9 and prior threshold 0.30 using Gibbs prior factor = 0.500 renormalizing sequences with structure alignment, equivalent to: -renormalize -renormalize_mean 0.500 -regularize 0.500 == Number of threads available to for OpenMP = 1 == reading 1 input volumes reading classifier array from /home/varun/freesurfer/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 5.59 Atlas used for the 3D morph was /home/varun/freesurfer/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 = 0.18227 ( 6) Left_Lateral_Ventricle (4): linear fit = 0.29 x + 0.0 (4015 voxels, overlap=0.005) Left_Lateral_Ventricle (4): linear fit = 0.40 x + 0.0 (4015 voxels, peak = 6), gca=8.0 gca peak = 0.20380 (13) mri peak = 0.18832 ( 4) Right_Lateral_Ventricle (43): linear fit = 0.22 x + 0.0 (3987 voxels, overlap=0.008) Right_Lateral_Ventricle (43): linear fit = 0.40 x + 0.0 (3987 voxels, peak = 3), gca=5.2 gca peak = 0.26283 (96) mri peak = 0.09227 (95) Right_Pallidum (52): linear fit = 0.99 x + 0.0 (399 voxels, overlap=1.006) Right_Pallidum (52): linear fit = 0.99 x + 0.0 (399 voxels, peak = 95), gca=94.6 gca peak = 0.15814 (97) mri peak = 0.12469 (99) Left_Pallidum (13): linear fit = 1.03 x + 0.0 (401 voxels, overlap=1.007) Left_Pallidum (13): linear fit = 1.03 x + 0.0 (401 voxels, peak = 100), gca=100.4 gca peak = 0.27624 (56) mri peak = 0.11000 (56) Right_Hippocampus (53): linear fit = 0.92 x + 0.0 (827 voxels, overlap=1.008) Right_Hippocampus (53): linear fit = 0.92 x + 0.0 (827 voxels, peak = 51), gca=51.2 gca peak = 0.28723 (59) mri peak = 0.11692 (55) Left_Hippocampus (17): linear fit = 0.96 x + 0.0 (825 voxels, overlap=1.009) Left_Hippocampus (17): linear fit = 0.96 x + 0.0 (825 voxels, peak = 57), gca=56.9 gca peak = 0.07623 (103) mri peak = 0.19443 (106) Right_Cerebral_White_Matter (41): linear fit = 1.01 x + 0.0 (23751 voxels, overlap=0.516) Right_Cerebral_White_Matter (41): linear fit = 1.01 x + 0.0 (23751 voxels, peak = 105), gca=104.5 gca peak = 0.07837 (105) mri peak = 0.16258 (108) Left_Cerebral_White_Matter (2): linear fit = 1.02 x + 0.0 (22167 voxels, overlap=0.474) Left_Cerebral_White_Matter (2): linear fit = 1.02 x + 0.0 (22167 voxels, peak = 108), gca=107.6 gca peak = 0.10165 (58) mri peak = 0.04183 (56) Left_Cerebral_Cortex (3): linear fit = 0.92 x + 0.0 (18015 voxels, overlap=0.876) Left_Cerebral_Cortex (3): linear fit = 0.92 x + 0.0 (18015 voxels, peak = 53), gca=53.1 gca peak = 0.11113 (58) mri peak = 0.04066 (56) Right_Cerebral_Cortex (42): linear fit = 0.92 x + 0.0 (18009 voxels, overlap=0.816) Right_Cerebral_Cortex (42): linear fit = 0.92 x + 0.0 (18009 voxels, peak = 53), gca=53.1 gca peak = 0.27796 (67) mri peak = 0.10775 (64) Right_Caudate (50): linear fit = 0.94 x + 0.0 (824 voxels, overlap=1.008) Right_Caudate (50): linear fit = 0.94 x + 0.0 (824 voxels, peak = 63), gca=63.3 gca peak = 0.14473 (69) mri peak = 0.14406 (66) Left_Caudate (11): linear fit = 0.88 x + 0.0 (715 voxels, overlap=0.337) Left_Caudate (11): linear fit = 0.88 x + 0.0 (715 voxels, peak = 61), gca=61.1 gca peak = 0.14301 (56) mri peak = 0.05038 (57) Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (19410 voxels, overlap=0.999) Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (19410 voxels, peak = 56), gca=56.0 gca peak = 0.14610 (55) mri peak = 0.05120 (54) Right_Cerebellum_Cortex (47): linear fit = 1.04 x + 0.0 (22420 voxels, overlap=0.997) Right_Cerebellum_Cortex (47): linear fit = 1.04 x + 0.0 (22420 voxels, peak = 57), gca=57.5 gca peak = 0.16309 (85) mri peak = 0.13454 (90) Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (4478 voxels, overlap=0.426) Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (4478 voxels, peak = 91), gca=90.5 gca peak = 0.15172 (84) mri peak = 0.13810 (88) Right_Cerebellum_White_Matter (46): linear fit = 1.07 x + 0.0 (4169 voxels, overlap=0.505) Right_Cerebellum_White_Matter (46): linear fit = 1.07 x + 0.0 (4169 voxels, peak = 89), gca=89.5 gca peak = 0.30461 (58) mri peak = 0.12583 (56) Left_Amygdala (18): linear fit = 0.96 x + 0.0 (309 voxels, overlap=1.024) Left_Amygdala (18): linear fit = 0.96 x + 0.0 (309 voxels, peak = 56), gca=56.0 gca peak = 0.32293 (57) mri peak = 0.16000 (56) Right_Amygdala (54): linear fit = 0.96 x + 0.0 (310 voxels, overlap=1.022) Right_Amygdala (54): linear fit = 0.96 x + 0.0 (310 voxels, peak = 55), gca=55.0 gca peak = 0.11083 (90) mri peak = 0.05765 (91) Left_Thalamus (10): linear fit = 1.02 x + 0.0 (3292 voxels, overlap=0.998) Left_Thalamus (10): linear fit = 1.02 x + 0.0 (3292 voxels, peak = 92), gca=92.2 gca peak = 0.11393 (83) mri peak = 0.07605 (85) Right_Thalamus (49): linear fit = 1.01 x + 0.0 (3046 voxels, overlap=0.979) Right_Thalamus (49): linear fit = 1.01 x + 0.0 (3046 voxels, peak = 84), gca=84.2 gca peak = 0.08575 (81) mri peak = 0.07557 (69) Left_Putamen (12): linear fit = 0.93 x + 0.0 (1710 voxels, overlap=0.852) Left_Putamen (12): linear fit = 0.93 x + 0.0 (1710 voxels, peak = 75), gca=74.9 gca peak = 0.08618 (78) mri peak = 0.08398 (69) Right_Putamen (51): linear fit = 0.90 x + 0.0 (1828 voxels, overlap=0.763) Right_Putamen (51): linear fit = 0.90 x + 0.0 (1828 voxels, peak = 71), gca=70.6 gca peak = 0.08005 (78) mri peak = 0.07934 (88) Brain_Stem (16): linear fit = 1.10 x + 0.0 (9717 voxels, overlap=0.492) Brain_Stem (16): linear fit = 1.10 x + 0.0 (9717 voxels, peak = 85), gca=85.4 gca peak = 0.12854 (88) mri peak = 0.09667 (97) Right_VentralDC (60): linear fit = 1.10 x + 0.0 (1008 voxels, overlap=0.420) Right_VentralDC (60): linear fit = 1.10 x + 0.0 (1008 voxels, peak = 96), gca=96.4 gca peak = 0.15703 (87) mri peak = 0.08550 (97) Left_VentralDC (28): linear fit = 1.10 x + 0.0 (1009 voxels, overlap=0.488) Left_VentralDC (28): linear fit = 1.10 x + 0.0 (1009 voxels, peak = 95), gca=95.3 gca peak = 0.17522 (25) mri peak = 0.20714 ( 6) Third_Ventricle (14): linear fit = 0.19 x + 0.0 (194 voxels, overlap=0.041) Third_Ventricle (14): linear fit = 0.19 x + 0.0 (194 voxels, peak = 5), gca=4.9 gca peak = 0.17113 (14) mri peak = 0.28227 ( 5) Fourth_Ventricle (15): linear fit = 0.22 x + 0.0 (823 voxels, overlap=0.014) Fourth_Ventricle (15): linear fit = 0.22 x + 0.0 (823 voxels, peak = 3), gca=3.0 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.16627 (28) gca peak Third_Ventricle = 0.17522 (25) gca peak Fourth_Ventricle = 0.17113 (14) gca peak CSF = 0.20346 (36) gca peak Left_Accumbens_area = 0.70646 (62) gca peak Left_undetermined = 1.00000 (28) gca peak Left_vessel = 0.89917 (53) gca peak Left_choroid_plexus = 0.11689 (35) gca peak Right_Inf_Lat_Vent = 0.25504 (23) gca peak Right_Accumbens_area = 0.31650 (65) gca peak Right_vessel = 0.77268 (52) gca peak Right_choroid_plexus = 0.13275 (38) gca peak Fifth_Ventricle = 0.60973 (33) gca peak WM_hypointensities = 0.11013 (77) gca peak non_WM_hypointensities = 0.11354 (41) gca peak Optic_Chiasm = 0.51646 (76) not using caudate to estimate GM means estimating mean gm scale to be 0.94 x + 0.0 estimating mean wm scale to be 1.02 x + 0.0 estimating mean csf scale to be 0.40 x + 0.0 saving intensity scales to aseg.auto_noCCseg.label_intensities.txt renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.31706 ( 7) mri peak = 0.18227 ( 6) Left_Lateral_Ventricle (4): linear fit = 0.76 x + 0.0 (4015 voxels, overlap=0.963) Left_Lateral_Ventricle (4): linear fit = 0.76 x + 0.0 (4015 voxels, peak = 5), gca=5.4 gca peak = 0.29334 ( 5) mri peak = 0.18832 ( 4) Right_Lateral_Ventricle (43): linear fit = 0.64 x + 0.0 (3987 voxels, overlap=0.973) Right_Lateral_Ventricle (43): linear fit = 0.64 x + 0.0 (3987 voxels, peak = 3), gca=3.2 gca peak = 0.21893 (92) mri peak = 0.09227 (95) Right_Pallidum (52): linear fit = 1.00 x + 0.0 (399 voxels, overlap=1.002) Right_Pallidum (52): linear fit = 1.00 x + 0.0 (399 voxels, peak = 92), gca=91.5 gca peak = 0.15777 (99) mri peak = 0.12469 (99) Left_Pallidum (13): linear fit = 1.00 x + 0.0 (401 voxels, overlap=1.008) Left_Pallidum (13): linear fit = 1.00 x + 0.0 (401 voxels, peak = 99), gca=99.5 gca peak = 0.29628 (51) mri peak = 0.11000 (56) Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (827 voxels, overlap=1.008) Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (827 voxels, peak = 51), gca=51.0 gca peak = 0.29201 (57) mri peak = 0.11692 (55) Left_Hippocampus (17): linear fit = 1.00 x + 0.0 (825 voxels, overlap=1.006) Left_Hippocampus (17): linear fit = 1.00 x + 0.0 (825 voxels, peak = 57), gca=57.0 gca peak = 0.07891 (105) mri peak = 0.19443 (106) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (23751 voxels, overlap=0.582) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (23751 voxels, peak = 106), gca=105.5 gca peak = 0.07733 (107) mri peak = 0.16258 (108) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (22167 voxels, overlap=0.594) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (22167 voxels, peak = 107), gca=107.0 gca peak = 0.10949 (53) mri peak = 0.04183 (56) Left_Cerebral_Cortex (3): linear fit = 0.99 x + 0.0 (18015 voxels, overlap=1.000) Left_Cerebral_Cortex (3): linear fit = 0.99 x + 0.0 (18015 voxels, peak = 52), gca=52.2 gca peak = 0.12299 (53) mri peak = 0.04066 (56) Right_Cerebral_Cortex (42): linear fit = 1.04 x + 0.0 (18009 voxels, overlap=0.983) Right_Cerebral_Cortex (42): linear fit = 1.04 x + 0.0 (18009 voxels, peak = 55), gca=55.4 gca peak = 0.23988 (63) mri peak = 0.10775 (64) Right_Caudate (50): linear fit = 1.00 x + 0.0 (824 voxels, overlap=1.005) Right_Caudate (50): linear fit = 1.00 x + 0.0 (824 voxels, peak = 63), gca=63.0 gca peak = 0.14721 (61) mri peak = 0.14406 (66) Left_Caudate (11): linear fit = 0.99 x + 0.0 (715 voxels, overlap=0.975) Left_Caudate (11): linear fit = 0.99 x + 0.0 (715 voxels, peak = 60), gca=60.1 gca peak = 0.14391 (56) mri peak = 0.05038 (57) Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (19410 voxels, overlap=0.999) Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (19410 voxels, peak = 56), gca=56.0 gca peak = 0.15451 (58) mri peak = 0.05120 (54) Right_Cerebellum_Cortex (47): linear fit = 0.96 x + 0.0 (22420 voxels, overlap=0.986) Right_Cerebellum_Cortex (47): linear fit = 0.96 x + 0.0 (22420 voxels, peak = 56), gca=56.0 gca peak = 0.15248 (90) mri peak = 0.13454 (90) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (4478 voxels, overlap=0.880) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (4478 voxels, peak = 90), gca=89.6 gca peak = 0.16759 (90) mri peak = 0.13810 (88) Right_Cerebellum_White_Matter (46): linear fit = 0.99 x + 0.0 (4169 voxels, overlap=0.876) Right_Cerebellum_White_Matter (46): linear fit = 0.99 x + 0.0 (4169 voxels, peak = 89), gca=88.7 gca peak = 0.32318 (56) mri peak = 0.12583 (56) Left_Amygdala (18): linear fit = 1.04 x + 0.0 (309 voxels, overlap=1.024) Left_Amygdala (18): linear fit = 1.04 x + 0.0 (309 voxels, peak = 59), gca=58.5 gca peak = 0.35902 (55) mri peak = 0.16000 (56) Right_Amygdala (54): linear fit = 1.00 x + 0.0 (310 voxels, overlap=1.025) Right_Amygdala (54): linear fit = 1.00 x + 0.0 (310 voxels, peak = 55), gca=55.0 gca peak = 0.09597 (92) mri peak = 0.05765 (91) Left_Thalamus (10): linear fit = 1.00 x + 0.0 (3292 voxels, overlap=0.996) Left_Thalamus (10): linear fit = 1.00 x + 0.0 (3292 voxels, peak = 92), gca=91.5 gca peak = 0.10262 (82) mri peak = 0.07605 (85) Right_Thalamus (49): linear fit = 1.00 x + 0.0 (3046 voxels, overlap=0.993) Right_Thalamus (49): linear fit = 1.00 x + 0.0 (3046 voxels, peak = 82), gca=82.0 gca peak = 0.08522 (75) mri peak = 0.07557 (69) Left_Putamen (12): linear fit = 1.03 x + 0.0 (1710 voxels, overlap=0.906) Left_Putamen (12): linear fit = 1.03 x + 0.0 (1710 voxels, peak = 78), gca=77.6 gca peak = 0.10408 (72) mri peak = 0.08398 (69) Right_Putamen (51): linear fit = 0.99 x + 0.0 (1828 voxels, overlap=0.999) Right_Putamen (51): linear fit = 0.99 x + 0.0 (1828 voxels, peak = 71), gca=70.9 gca peak = 0.08435 (86) mri peak = 0.07934 (88) Brain_Stem (16): linear fit = 1.01 x + 0.0 (9717 voxels, overlap=0.811) Brain_Stem (16): linear fit = 1.01 x + 0.0 (9717 voxels, peak = 87), gca=87.3 gca peak = 0.10936 (94) mri peak = 0.09667 (97) Right_VentralDC (60): linear fit = 1.00 x + 0.0 (1008 voxels, overlap=0.808) Right_VentralDC (60): linear fit = 1.00 x + 0.0 (1008 voxels, peak = 94), gca=94.5 gca peak = 0.12760 (94) mri peak = 0.08550 (97) Left_VentralDC (28): linear fit = 1.00 x + 0.0 (1009 voxels, overlap=0.903) Left_VentralDC (28): linear fit = 1.00 x + 0.0 (1009 voxels, peak = 94), gca=94.5 gca peak = 0.32031 (10) mri peak = 0.20714 ( 6) Third_Ventricle (14): linear fit = 0.52 x + 0.0 (194 voxels, overlap=0.691) Third_Ventricle (14): linear fit = 0.52 x + 0.0 (194 voxels, peak = 5), gca=5.2 gca peak = 0.46125 ( 6) mri peak = 0.28227 ( 5) Fourth_Ventricle (15): linear fit = 0.55 x + 0.0 (823 voxels, overlap=0.759) Fourth_Ventricle (15): linear fit = 0.55 x + 0.0 (823 voxels, peak = 3), gca=3.3 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.16627 (27) gca peak Third_Ventricle = 0.32031 (10) gca peak Fourth_Ventricle = 0.46125 ( 6) gca peak CSF = 0.27810 (15) gca peak Left_Accumbens_area = 0.79855 (55) gca peak Left_undetermined = 1.00000 (28) gca peak Left_vessel = 0.88792 (53) gca peak Left_choroid_plexus = 0.11689 (35) gca peak Right_Inf_Lat_Vent = 0.25741 (21) gca peak Right_Accumbens_area = 0.41820 (61) gca peak Right_vessel = 0.78856 (52) gca peak Right_choroid_plexus = 0.13275 (38) gca peak Fifth_Ventricle = 0.92085 (13) gca peak WM_hypointensities = 0.09888 (78) gca peak non_WM_hypointensities = 0.08220 (42) gca peak Optic_Chiasm = 0.52440 (76) not using caudate to estimate GM means estimating mean gm scale to be 1.01 x + 0.0 estimating mean wm scale to be 1.00 x + 0.0 estimating mean csf scale to be 0.70 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 42168 voxels changed in iteration 0 of unlikely voxel relabeling 120 voxels changed in iteration 1 of unlikely voxel relabeling 0 voxels changed in iteration 2 of unlikely voxel relabeling 20988 gm and wm labels changed (%26 to gray, %74 to white out of all changed labels) 323 hippocampal voxels changed. 0 amygdala voxels changed. Reclassifying using Gibbs Priors pass 1: 56372 changed. image ll: -2.404, PF=0.500 pass 2: 15812 changed. image ll: -2.404, PF=0.500 pass 3: 5577 changed. pass 4: 2166 changed. 30378 voxels changed in iteration 0 of unlikely voxel relabeling 157 voxels changed in iteration 1 of unlikely voxel relabeling 22 voxels changed in iteration 2 of unlikely voxel relabeling 7 voxels changed in iteration 3 of unlikely voxel relabeling 0 voxels changed in iteration 4 of unlikely voxel relabeling 6336 voxels changed in iteration 0 of unlikely voxel relabeling 49 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 5820 voxels changed in iteration 0 of unlikely voxel relabeling 51 voxels changed in iteration 1 of unlikely voxel relabeling 0 voxels changed in iteration 2 of unlikely voxel relabeling 5313 voxels changed in iteration 0 of unlikely voxel relabeling 14 voxels changed in iteration 1 of unlikely voxel relabeling 3 voxels changed in iteration 2 of unlikely voxel relabeling 0 voxels changed in iteration 3 of unlikely voxel relabeling !!!!!!!!! ventricle segment 0 with volume 12252 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 1 with volume 144 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 3 with volume 127 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 3 with volume 12117 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 0 with volume 116 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 2 with volume 157 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 5 with volume 149 above threshold 100 - not erasing !!!!!!!!!! writing labeled volume to aseg.auto_noCCseg.mgz mri_ca_label utimesec 1414.775211 mri_ca_label stimesec 1.600999 mri_ca_label ru_maxrss 2109524 mri_ca_label ru_ixrss 0 mri_ca_label ru_idrss 0 mri_ca_label ru_isrss 0 mri_ca_label ru_minflt 1506982 mri_ca_label ru_majflt 25 mri_ca_label ru_nswap 0 mri_ca_label ru_inblock 3136 mri_ca_label ru_oublock 0 mri_ca_label ru_msgsnd 0 mri_ca_label ru_msgrcv 0 mri_ca_label ru_nsignals 0 mri_ca_label ru_nvcsw 4376 mri_ca_label ru_nivcsw 7553 auto-labeling took 23 minutes and 37 seconds. @#@FSTIME 2023:06:14:18:06:47 mri_ca_label N 10 e 1416.64 S 1.68 U 1414.77 P 99% M 2109524 F 25 R 1506986 W 0 c 7555 w 4377 I 3136 O 0 L 1.02 1.04 1.00 @#@FSLOADPOST 2023:06:14:18:30:23 mri_ca_label N 10 1.10 1.07 1.01 #-------------------------------------- #@# CC Seg Wed Jun 14 18:30:23 BST 2023 mri_cc -aseg aseg.auto_noCCseg.mgz -o aseg.auto.mgz -lta /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/transforms/cc_up.lta T1_260423_2 will read input aseg from aseg.auto_noCCseg.mgz writing aseg with cc labels to aseg.auto.mgz will write lta as /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/transforms/cc_up.lta reading aseg from /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/aseg.auto_noCCseg.mgz reading norm from /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/norm.mgz MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab 6427 voxels in left wm, 15777 in right wm, xrange [126, 131] searching rotation angles z=[-9 5], y=[-7 7] searching scale 1 Z rot -8.6 searching scale 1 Z rot -8.3 searching scale 1 Z rot -8.1 searching scale 1 Z rot -7.8 searching scale 1 Z rot -7.6 searching scale 1 Z rot -7.3 searching scale 1 Z rot -7.1 searching scale 1 Z rot -6.8 searching scale 1 Z rot -6.6 searching scale 1 Z rot -6.3 searching scale 1 Z rot -6.1 searching scale 1 Z rot -5.8 searching scale 1 Z rot -5.6 searching scale 1 Z rot -5.3 searching scale 1 Z rot -5.1 searching scale 1 Z rot -4.8 searching scale 1 Z rot -4.6 searching scale 1 Z rot -4.3 searching scale 1 Z rot -4.1 searching scale 1 Z rot -3.8 searching scale 1 Z rot -3.6 searching scale 1 Z rot -3.3 searching scale 1 Z rot -3.1 searching scale 1 Z rot -2.8 searching scale 1 Z rot -2.6 searching scale 1 Z rot -2.3 searching scale 1 Z rot -2.1 searching scale 1 Z rot -1.8 searching scale 1 Z rot -1.6 searching scale 1 Z rot -1.3 searching scale 1 Z rot -1.1 searching scale 1 Z rot -0.8 searching scale 1 Z rot -0.6 searching scale 1 Z rot -0.3 searching scale 1 Z rot -0.1 searching scale 1 Z rot 0.2 searching scale 1 Z rot 0.4 searching scale 1 Z rot 0.7 searching scale 1 Z rot 0.9 searching scale 1 Z rot 1.2 searching scale 1 Z rot 1.4 searching scale 1 Z rot 1.7 searching scale 1 Z rot 1.9 searching scale 1 Z rot 2.2 searching scale 1 Z rot 2.4 searching scale 1 Z rot 2.7 searching scale 1 Z rot 2.9 searching scale 1 Z rot 3.2 searching scale 1 Z rot 3.4 searching scale 1 Z rot 3.7 searching scale 1 Z rot 3.9 searching scale 1 Z rot 4.2 searching scale 1 Z rot 4.4 searching scale 1 Z rot 4.7 searching scale 1 Z rot 4.9 searching scale 1 Z rot 5.2 searching scale 1 Z rot 5.4 searching scale 1 Z rot 5.7 global minimum found at slice 129.0, rotations (0.01, -1.09) final transformation (x=129.0, yr=0.007, zr=-1.089): 0.99982 0.01900 0.00011 -3.46039; -0.01900 0.99982 -0.00000 0.47533; -0.00011 0.00000 1.00000 14.01480; 0.00000 0.00000 0.00000 1.00000; updating x range to be [125, 129] in xformed coordinates best xformed slice 127 min_x_fornix = 139 min_x_fornix = 143 min_x_fornix = 142 min_x_fornix = 139 min_x_fornix = 137 cc center is found at 127 130 114 eigenvectors: -0.00062 0.00202 1.00000; -0.11481 -0.99339 0.00193; 0.99339 -0.11480 0.00085; MRIcopyHeader(): source has ctab MRIcopyHeader(): source has ctab error in mid anterior detected - correcting... error in mid anterior detected - correcting... writing aseg with callosum to /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/aseg.auto.mgz... corpus callosum segmentation took 0.3 minutes #VMPC# mri_cc VmPeak 443932 mri_cc done @#@FSTIME 2023:06:14:18:30:23 mri_cc N 7 e 20.47 S 0.27 U 20.10 P 99% M 344048 F 8 R 277291 W 0 c 563 w 333 I 1600 O 0 L 1.10 1.07 1.01 @#@FSLOADPOST 2023:06:14:18:30:44 mri_cc N 7 1.07 1.06 1.01 #-------------------------------------- #@# Merge ASeg Wed Jun 14 18:30:44 BST 2023 cp aseg.auto.mgz aseg.presurf.mgz #-------------------------------------------- #@# Intensity Normalization2 Wed Jun 14 18:30:44 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 979 control points removed MRIcopyHeader(): source has ctab Building bias image building Voronoi diagram... performing soap bubble smoothing, sigma = 0... Smoothing with sigma 8 Applying bias correction building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Iterating 2 times --------------------------------- 3d normalization pass 1 of 2 white matter peak found at 110 white matter peak found at 110 gm peak at 57 (57), valley at 41 (41) csf peak at 29, setting threshold to 47 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 56 (56), valley at 41 (41) csf peak at 28, setting threshold to 46 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Done iterating --------------------------------- writing output to brain.mgz 3D bias adjustment took 1 minutes and 46 seconds. @#@FSTIME 2023:06:14:18:30:44 mri_normalize N 9 e 107.25 S 1.26 U 105.88 P 99% M 1209924 F 1 R 635263 W 0 c 715 w 646 I 48 O 0 L 1.07 1.06 1.01 @#@FSLOADPOST 2023:06:14:18:32:31 mri_normalize N 9 1.18 1.12 1.03 #-------------------------------------------- #@# Mask BFS Wed Jun 14 18:32:31 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri mri_mask -T 5 brain.mgz brainmask.mgz brain.finalsurfs.mgz threshold mask volume at 5 DoAbs = 0 Found 1760788 voxels in mask (pct= 10.50) Writing masked volume to brain.finalsurfs.mgz...done. @#@FSTIME 2023:06:14:18:32:31 mri_mask N 5 e 1.09 S 0.02 U 0.94 P 88% M 74140 F 7 R 17257 W 0 c 54 w 434 I 984 O 0 L 1.18 1.12 1.03 @#@FSLOADPOST 2023:06:14:18:32:32 mri_mask N 5 1.18 1.12 1.03 #-------------------------------------------- #@# WM Segmentation Wed Jun 14 18:32:32 BST 2023 AntsDenoiseImageFs -i brain.mgz -o antsdn.brain.mgz @#@FSTIME 2023:06:14:18:32:32 AntsDenoiseImageFs N 4 e 29.36 S 0.13 U 29.14 P 99% M 350772 F 26 R 86484 W 0 c 207 w 289 I 4240 O 0 L 1.18 1.12 1.03 @#@FSLOADPOST 2023:06:14:18:33:02 AntsDenoiseImageFs N 4 1.11 1.11 1.03 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 (104.0): 104.0 +- 4.7 [79.0 --> 125.0] CCS GM (70.0) : 68.4 +- 10.6 [30.0 --> 95.0] white_mean 104.028 white_sigma 4.70385 gray_mean 68.3697 gray_sigma 10.5713 setting bottom of white matter range wm_low to 78.9 setting top of gray matter range gray_hi to 89.5 wm_low 78.941 wm_hi 125 gray_low 30 gray_hi 89.5123 Redoing initial intensity segmentation... Recomputing local statistics to label ambiguous voxels... wm_low 78.941 wm_hi 125 gray_low 30 gray_hi 89.5123 using local geometry to label remaining ambiguous voxels... polvwsize = 5, polvlen = 3, gray_hi = 89.5123, wm_low = 78.941 MRIcpolvMedianCurveSegment(): wsize=5, len=3, gmhi=89.5123, wmlow=78.941 102456 voxels processed (0.61%) 46745 voxels white (0.28%) 55711 voxels non-white (0.33%) Reclassifying voxels using Gaussian border classifier niter=1 MRIreclassify(): wm_low=73.941, gray_hi=89.5123, wsize=13 165028 voxels tested (0.98%) 38547 voxels changed (0.23%) 36053 multi-scale searches (0.21%) Recovering bright white MRIrecoverBrightWhite() wm_low 78.941 wm_hi 125 slack 4.70385 pct_thresh 0.33 intensity_thresh 129.704 nvox_thresh 8.58 85 voxels tested (0.00%) 79 voxels changed (0.00%) removing voxels with positive offset direction... MRIremoveWrongDirection() wsize=3, lowthr=73.941, hithr=89.5123 smoothing input volume with sigma = 0.250 71201 voxels tested (0.42%) 13768 voxels changed (0.08%) thicken = 1 removing 1-dimensional structures... MRIremove1dStructures(): max_iter=10000, thresh=2, WM_MIN_VAL=5 1431 sparsely connected voxels removed in 1 iterations thickening thin strands.... thickness 4 nsegments 20 wm_hi 125 816 diagonally connected voxels added... MRIthickenThinWMStrands(): thickness=4, nsegments=20 20 segments, 2996 filled MRIfindBrightNonWM(): 0 bright non-wm voxels segmented. MRIfilterMorphology() WM_MIN_VAL=5, DIAGONAL_FILL=230 white matter segmentation took 0.8 minutes writing output to wm.seg.mgz... @#@FSTIME 2023:06:14:18:33:02 mri_segment N 5 e 47.73 S 0.30 U 47.36 P 99% M 143392 F 4 R 287257 W 0 c 487 w 242 I 768 O 0 L 1.11 1.11 1.03 @#@FSLOADPOST 2023:06:14:18:33:50 mri_segment N 5 1.10 1.11 1.03 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 0.36 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 3685 additional wm voxels added 0 additional wm voxels added SEG EDIT: 64748 voxels turned on, 45411 voxels turned off. propagating editing to output volume from wm.seg.mgz writing edited volume to wm.asegedit.mgz.... @#@FSTIME 2023:06:14:18:33:50 mri_edit_wm_with_aseg N 5 e 21.34 S 0.24 U 20.93 P 99% M 463616 F 8 R 315026 W 0 c 373 w 448 I 992 O 0 L 1.10 1.11 1.03 @#@FSLOADPOST 2023:06:14:18:34:11 mri_edit_wm_with_aseg N 5 1.07 1.10 1.03 mri_pretess wm.asegedit.mgz wm norm.mgz wm.mgz Iteration Number : 1 pass 1 (xy+): 10 found - 10 modified | TOTAL: 10 pass 2 (xy+): 0 found - 10 modified | TOTAL: 10 pass 1 (xy-): 11 found - 11 modified | TOTAL: 21 pass 2 (xy-): 0 found - 11 modified | TOTAL: 21 pass 1 (yz+): 18 found - 18 modified | TOTAL: 39 pass 2 (yz+): 0 found - 18 modified | TOTAL: 39 pass 1 (yz-): 12 found - 12 modified | TOTAL: 51 pass 2 (yz-): 0 found - 12 modified | TOTAL: 51 pass 1 (xz+): 13 found - 13 modified | TOTAL: 64 pass 2 (xz+): 0 found - 13 modified | TOTAL: 64 pass 1 (xz-): 14 found - 14 modified | TOTAL: 78 pass 2 (xz-): 0 found - 14 modified | TOTAL: 78 Iteration Number : 1 pass 1 (+++): 4 found - 4 modified | TOTAL: 4 pass 2 (+++): 0 found - 4 modified | TOTAL: 4 pass 1 (+++): 2 found - 2 modified | TOTAL: 6 pass 2 (+++): 0 found - 2 modified | TOTAL: 6 pass 1 (+++): 2 found - 2 modified | TOTAL: 8 pass 2 (+++): 0 found - 2 modified | TOTAL: 8 pass 1 (+++): 1 found - 1 modified | TOTAL: 9 pass 2 (+++): 0 found - 1 modified | TOTAL: 9 Iteration Number : 1 pass 1 (++): 28 found - 28 modified | TOTAL: 28 pass 2 (++): 0 found - 28 modified | TOTAL: 28 pass 1 (+-): 61 found - 61 modified | TOTAL: 89 pass 2 (+-): 0 found - 61 modified | TOTAL: 89 pass 1 (--): 31 found - 31 modified | TOTAL: 120 pass 2 (--): 0 found - 31 modified | TOTAL: 120 pass 1 (-+): 46 found - 46 modified | TOTAL: 166 pass 2 (-+): 0 found - 46 modified | TOTAL: 166 Iteration Number : 2 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 2 found - 2 modified | TOTAL: 2 pass 2 (xy-): 0 found - 2 modified | TOTAL: 2 pass 1 (yz+): 1 found - 1 modified | TOTAL: 3 pass 2 (yz+): 0 found - 1 modified | TOTAL: 3 pass 1 (yz-): 1 found - 1 modified | TOTAL: 4 pass 2 (yz-): 0 found - 1 modified | TOTAL: 4 pass 1 (xz+): 1 found - 1 modified | TOTAL: 5 pass 2 (xz+): 0 found - 1 modified | TOTAL: 5 pass 1 (xz-): 1 found - 1 modified | TOTAL: 6 pass 2 (xz-): 0 found - 1 modified | TOTAL: 6 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 (+-): 1 found - 1 modified | TOTAL: 1 pass 2 (+-): 0 found - 1 modified | TOTAL: 1 pass 1 (--): 0 found - 0 modified | TOTAL: 1 pass 1 (-+): 0 found - 0 modified | TOTAL: 1 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-): 1 found - 1 modified | TOTAL: 1 pass 2 (yz-): 0 found - 1 modified | TOTAL: 1 pass 1 (xz+): 0 found - 0 modified | TOTAL: 1 pass 1 (xz-): 0 found - 0 modified | TOTAL: 1 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 = 261 (out of 429968: 0.060702) binarizing input wm segmentation... Ambiguous edge configurations... mri_pretess done @#@FSTIME 2023:06:14:18:34:11 mri_pretess N 4 e 2.30 S 0.05 U 2.17 P 96% M 56672 F 22 R 12939 W 0 c 15 w 346 I 3568 O 0 L 1.07 1.10 1.03 @#@FSLOADPOST 2023:06:14:18:34:13 mri_pretess N 4 1.07 1.10 1.03 #-------------------------------------------- #@# Fill Wed Jun 14 18:34:13 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri mri_fill -a ../scripts/ponscc.cut.log -xform transforms/talairach.lta -segmentation aseg.presurf.mgz -ctab /home/varun/freesurfer/SubCorticalMassLUT.txt wm.mgz filled.mgz logging cutting plane coordinates to ../scripts/ponscc.cut.log... INFO: Using transforms/talairach.lta and its offset for Talairach volume ... using segmentation aseg.presurf.mgz... reading input volume...done. searching for cutting planes...voxel to talairach voxel transform 1.10030 0.00330 0.02799 -19.40283; -0.00902 1.17960 0.29815 -77.42525; -0.02693 -0.25693 1.03785 16.45119; 0.00000 0.00000 0.00000 1.00000; voxel to talairach voxel transform 1.10030 0.00330 0.02799 -19.40283; -0.00902 1.17960 0.29815 -77.42525; -0.02693 -0.25693 1.03785 16.45119; 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] = 1190 (min = 350, max = 1400), aspect = 0.42 (min = 0.10, max = 0.75) no need to search using seed (125, 109, 89), TAL = (3.0, -39.0, 19.0) talairach voxel to voxel transform 0.90824 -0.00741 -0.02237 17.41634; 0.00093 0.79781 -0.22922 65.56004; 0.02380 0.19731 0.90621 0.83041; 0.00000 0.00000 0.00000 1.00000; segmentation indicates cc at (125, 109, 89) --> (3.0, -39.0, 19.0) done. filling took 1.0 minutes talairach cc position changed to (3.00, -39.00, 19.00) Erasing brainstem...done. seed_search_size = 9, min_neighbors = 5 search rh wm seed point around talairach space:(21.00, -39.00, 19.00) SRC: (111.80, 132.22, 105.54) search lh wm seed point around talairach space (-15.00, -39.00, 19.00), SRC: (144.50, 132.25, 106.39) 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 2023:06:14:18:34:13 mri_fill N 10 e 58.60 S 1.29 U 57.21 P 99% M 977284 F 7 R 514871 W 0 c 603 w 166 I 1280 O 0 L 1.07 1.10 1.03 @#@FSLOADPOST 2023:06:14:18:35:12 mri_fill N 10 1.02 1.08 1.02 cp filled.mgz filled.auto.mgz #-------------------------------------------- #@# Tessellate lh Wed Jun 14 18:35:12 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mri_pretess ../mri/filled.mgz 255 ../mri/norm.mgz ../mri/filled-pretess255.mgz Iteration Number : 1 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+): 4 found - 4 modified | TOTAL: 5 pass 2 (yz+): 0 found - 4 modified | TOTAL: 5 pass 1 (yz-): 5 found - 5 modified | TOTAL: 10 pass 2 (yz-): 0 found - 5 modified | TOTAL: 10 pass 1 (xz+): 2 found - 2 modified | TOTAL: 12 pass 2 (xz+): 0 found - 2 modified | TOTAL: 12 pass 1 (xz-): 0 found - 0 modified | TOTAL: 12 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 (+-): 1 found - 1 modified | TOTAL: 1 pass 2 (+-): 0 found - 1 modified | TOTAL: 1 pass 1 (--): 0 found - 0 modified | TOTAL: 1 pass 1 (-+): 0 found - 0 modified | TOTAL: 1 Iteration Number : 2 pass 1 (xy+): 1 found - 1 modified | TOTAL: 1 pass 2 (xy+): 0 found - 1 modified | TOTAL: 1 pass 1 (xy-): 0 found - 0 modified | TOTAL: 1 pass 1 (yz+): 0 found - 0 modified | TOTAL: 1 pass 1 (yz-): 0 found - 0 modified | TOTAL: 1 pass 1 (xz+): 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 = 14 (out of 204346: 0.006851) Ambiguous edge configurations... mri_pretess done @#@FSTIME 2023:06:14:18:35:12 mri_pretess N 4 e 1.46 S 0.02 U 1.36 P 95% M 40300 F 0 R 8815 W 0 c 177 w 283 I 0 O 0 L 1.02 1.08 1.02 @#@FSLOADPOST 2023:06:14:18:35:14 mri_pretess N 4 1.02 1.08 1.02 mri_tessellate ../mri/filled-pretess255.mgz 255 ../surf/lh.orig.nofix 7.2.0 7.2.0 slice 60: 2013 vertices, 2175 faces slice 70: 8132 vertices, 8374 faces slice 80: 15992 vertices, 16265 faces slice 90: 24237 vertices, 24533 faces slice 100: 32604 vertices, 32871 faces slice 110: 41212 vertices, 41515 faces slice 120: 50780 vertices, 51124 faces slice 130: 59999 vertices, 60312 faces slice 140: 68500 vertices, 68817 faces slice 150: 77635 vertices, 77923 faces slice 160: 84741 vertices, 84973 faces slice 170: 90602 vertices, 90816 faces slice 180: 95995 vertices, 96167 faces slice 190: 100503 vertices, 100642 faces slice 200: 103194 vertices, 103271 faces slice 210: 103732 vertices, 103744 faces slice 220: 103732 vertices, 103744 faces slice 230: 103732 vertices, 103744 faces slice 240: 103732 vertices, 103744 faces slice 250: 103732 vertices, 103744 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 2023:06:14:18:35:14 mri_tessellate N 3 e 1.10 S 0.13 U 0.86 P 90% M 34668 F 8 R 7667 W 0 c 35 w 358 I 1184 O 0 L 1.02 1.08 1.02 @#@FSLOADPOST 2023:06:14:18:35:15 mri_tessellate N 3 1.02 1.08 1.02 rm -f ../mri/filled-pretess255.mgz mris_extract_main_component ../surf/lh.orig.nofix ../surf/lh.orig.nofix counting number of connected components... 103732 voxel in cpt #1: X=-12 [v=103732,e=311232,f=207488] located at (-26.874514, -6.533104, -3.234412) For the whole surface: X=-12 [v=103732,e=311232,f=207488] One single component has been found nothing to do done @#@FSTIME 2023:06:14:18:35:15 mris_extract_main_component N 2 e 0.75 S 0.09 U 0.50 P 79% M 207412 F 19 R 54530 W 0 c 28 w 757 I 3264 O 0 L 1.02 1.08 1.02 @#@FSLOADPOST 2023:06:14:18:35:16 mris_extract_main_component N 2 1.02 1.08 1.02 #-------------------------------------------- #@# Tessellate rh Wed Jun 14 18:35:16 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mri_pretess ../mri/filled.mgz 127 ../mri/norm.mgz ../mri/filled-pretess127.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-): 0 found - 0 modified | TOTAL: 1 pass 1 (yz+): 4 found - 4 modified | TOTAL: 5 pass 2 (yz+): 0 found - 4 modified | TOTAL: 5 pass 1 (yz-): 6 found - 6 modified | TOTAL: 11 pass 2 (yz-): 0 found - 6 modified | TOTAL: 11 pass 1 (xz+): 0 found - 0 modified | TOTAL: 11 pass 1 (xz-): 0 found - 0 modified | TOTAL: 11 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 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 1 found - 1 modified | TOTAL: 1 pass 2 (--): 0 found - 1 modified | TOTAL: 1 pass 1 (-+): 0 found - 0 modified | TOTAL: 1 Iteration Number : 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 = 12 (out of 208221: 0.005763) Ambiguous edge configurations... mri_pretess done @#@FSTIME 2023:06:14:18:35:16 mri_pretess N 4 e 1.14 S 0.26 U 0.83 P 95% M 40304 F 0 R 8812 W 0 c 18 w 271 I 0 O 0 L 1.02 1.08 1.02 @#@FSLOADPOST 2023:06:14:18:35:17 mri_pretess N 4 1.02 1.08 1.02 mri_tessellate ../mri/filled-pretess127.mgz 127 ../surf/rh.orig.nofix 7.2.0 7.2.0 slice 60: 1123 vertices, 1236 faces slice 70: 6350 vertices, 6600 faces slice 80: 14703 vertices, 14988 faces slice 90: 23687 vertices, 23999 faces slice 100: 33287 vertices, 33600 faces slice 110: 42686 vertices, 43015 faces slice 120: 52673 vertices, 53017 faces slice 130: 61819 vertices, 62147 faces slice 140: 71050 vertices, 71368 faces slice 150: 79966 vertices, 80301 faces slice 160: 87705 vertices, 87960 faces slice 170: 94174 vertices, 94398 faces slice 180: 99811 vertices, 99983 faces slice 190: 103920 vertices, 104044 faces slice 200: 106493 vertices, 106581 faces slice 210: 107132 vertices, 107148 faces slice 220: 107132 vertices, 107148 faces slice 230: 107132 vertices, 107148 faces slice 240: 107132 vertices, 107148 faces slice 250: 107132 vertices, 107148 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 2023:06:14:18:35:17 mri_tessellate N 3 e 1.06 S 0.14 U 0.84 P 92% M 34976 F 0 R 7746 W 0 c 18 w 359 I 0 O 0 L 1.02 1.08 1.02 @#@FSLOADPOST 2023:06:14:18:35:18 mri_tessellate N 3 1.02 1.08 1.02 rm -f ../mri/filled-pretess127.mgz mris_extract_main_component ../surf/rh.orig.nofix ../surf/rh.orig.nofix counting number of connected components... 107132 voxel in cpt #1: X=-16 [v=107132,e=321444,f=214296] located at (25.969776, -6.130624, -3.127301) For the whole surface: X=-16 [v=107132,e=321444,f=214296] One single component has been found nothing to do done @#@FSTIME 2023:06:14:18:35:18 mris_extract_main_component N 2 e 0.79 S 0.32 U 0.37 P 88% M 214056 F 0 R 56344 W 0 c 15 w 739 I 0 O 0 L 1.02 1.08 1.02 @#@FSLOADPOST 2023:06:14:18:35:19 mris_extract_main_component N 2 1.02 1.08 1.02 #-------------------------------------------- #@# Smooth1 lh Wed Jun 14 18:35:19 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_smooth -nw -seed 1234 ../surf/lh.orig.nofix ../surf/lh.smoothwm.nofix setting seed for random number generator to 1234 smoothing surface tessellation for 10 iterations... smoothing complete - recomputing first and second fundamental forms... @#@FSTIME 2023:06:14:18:35:19 mris_smooth N 5 e 1.86 S 0.51 U 1.23 P 94% M 165612 F 5 R 49310 W 0 c 39 w 863 I 672 O 0 L 1.02 1.08 1.02 @#@FSLOADPOST 2023:06:14:18:35:21 mris_smooth N 5 1.01 1.07 1.02 #-------------------------------------------- #@# Smooth1 rh Wed Jun 14 18:35:21 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_smooth -nw -seed 1234 ../surf/rh.orig.nofix ../surf/rh.smoothwm.nofix setting seed for random number generator to 1234 smoothing surface tessellation for 10 iterations... smoothing complete - recomputing first and second fundamental forms... @#@FSTIME 2023:06:14:18:35:21 mris_smooth N 5 e 1.86 S 0.62 U 1.13 P 94% M 170936 F 0 R 50941 W 0 c 13 w 898 I 0 O 0 L 1.01 1.07 1.02 @#@FSLOADPOST 2023:06:14:18:35:23 mris_smooth N 5 1.01 1.07 1.02 #-------------------------------------------- #@# Inflation1 lh Wed Jun 14 18:35:23 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_inflate -no-save-sulc ../surf/lh.smoothwm.nofix ../surf/lh.inflated.nofix Not saving sulc Reading ../surf/lh.smoothwm.nofix avg radius = 44.4 mm, total surface area = 56718 mm^2 step 000: RMS=0.144 (target=0.015) step 005: RMS=0.108 (target=0.015) step 010: RMS=0.078 (target=0.015) step 015: RMS=0.063 (target=0.015) step 020: RMS=0.054 (target=0.015) step 025: RMS=0.047 (target=0.015) step 030: RMS=0.041 (target=0.015) step 035: RMS=0.036 (target=0.015) step 040: RMS=0.033 (target=0.015) step 045: RMS=0.031 (target=0.015) step 050: RMS=0.029 (target=0.015) step 055: RMS=0.028 (target=0.015) step 060: RMS=0.028 (target=0.015) writing inflated surface to ../surf/lh.inflated.nofix inflation took 0.2 minutes inflation complete. Not saving sulc mris_inflate utimesec 11.644006 mris_inflate stimesec 0.522986 mris_inflate ru_maxrss 165976 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 433038 mris_inflate ru_majflt 21 mris_inflate ru_nswap 0 mris_inflate ru_inblock 3736 mris_inflate ru_oublock 0 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 894 mris_inflate ru_nivcsw 155 @#@FSTIME 2023:06:14:18:35:23 mris_inflate N 3 e 12.35 S 0.53 U 11.64 P 98% M 165976 F 21 R 433044 W 0 c 158 w 895 I 3736 O 0 L 1.01 1.07 1.02 @#@FSLOADPOST 2023:06:14:18:35:35 mris_inflate N 3 1.23 1.12 1.04 #-------------------------------------------- #@# Inflation1 rh Wed Jun 14 18:35:35 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_inflate -no-save-sulc ../surf/rh.smoothwm.nofix ../surf/rh.inflated.nofix Not saving sulc Reading ../surf/rh.smoothwm.nofix avg radius = 44.1 mm, total surface area = 58357 mm^2 step 000: RMS=0.145 (target=0.015) step 005: RMS=0.109 (target=0.015) step 010: RMS=0.080 (target=0.015) step 015: RMS=0.066 (target=0.015) step 020: RMS=0.056 (target=0.015) step 025: RMS=0.048 (target=0.015) step 030: RMS=0.041 (target=0.015) step 035: RMS=0.037 (target=0.015) step 040: RMS=0.034 (target=0.015) step 045: RMS=0.033 (target=0.015) step 050: RMS=0.031 (target=0.015) step 055: RMS=0.030 (target=0.015) step 060: RMS=0.030 (target=0.015) writing inflated surface to ../surf/rh.inflated.nofix inflation took 0.2 minutes inflation complete. Not saving sulc mris_inflate utimesec 9.382755 mris_inflate stimesec 2.855621 mris_inflate ru_maxrss 171376 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 453898 mris_inflate ru_majflt 0 mris_inflate ru_nswap 0 mris_inflate ru_inblock 0 mris_inflate ru_oublock 0 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 948 mris_inflate ru_nivcsw 34 @#@FSTIME 2023:06:14:18:35:35 mris_inflate N 3 e 12.36 S 2.86 U 9.38 P 99% M 171376 F 0 R 453903 W 0 c 34 w 949 I 0 O 0 L 1.45 1.17 1.05 @#@FSLOADPOST 2023:06:14:18:35:48 mris_inflate N 3 1.46 1.18 1.06 #-------------------------------------------- #@# QSphere lh Wed Jun 14 18:35:48 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_sphere -q -p 6 -a 128 -seed 1234 ../surf/lh.inflated.nofix ../surf/lh.qsphere.nofix doing quick spherical unfolding. limitting unfolding to 6 passes using n_averages = 128 setting seed for random number genererator to 1234 version: 7.2.0 available threads: 1 scaling brain by 0.352... inflating... projecting onto sphere... surface projected - minimizing metric distortion... vertex spacing 1.13 +- 0.63 (0.00-->6.93) (max @ vno 40838 --> 40839) face area 0.03 +- 0.04 (-0.06-->0.72) Entering MRISinflateToSphere() inflating to sphere (rms error < 2.00) 000: dt: 0.0000, rms radial error=176.245, avgs=0 005/300: dt: 0.9000, rms radial error=175.988, avgs=0 010/300: dt: 0.9000, rms radial error=175.434, avgs=0 015/300: dt: 0.9000, rms radial error=174.708, avgs=0 020/300: dt: 0.9000, rms radial error=173.881, avgs=0 025/300: dt: 0.9000, rms radial error=172.995, avgs=0 030/300: dt: 0.9000, rms radial error=172.076, avgs=0 035/300: dt: 0.9000, rms radial error=171.140, avgs=0 040/300: dt: 0.9000, rms radial error=170.196, avgs=0 045/300: dt: 0.9000, rms radial error=169.249, avgs=0 050/300: dt: 0.9000, rms radial error=168.303, avgs=0 055/300: dt: 0.9000, rms radial error=167.362, avgs=0 060/300: dt: 0.9000, rms radial error=166.425, avgs=0 065/300: dt: 0.9000, rms radial error=165.492, avgs=0 070/300: dt: 0.9000, rms radial error=164.563, avgs=0 075/300: dt: 0.9000, rms radial error=163.639, avgs=0 080/300: dt: 0.9000, rms radial error=162.721, avgs=0 085/300: dt: 0.9000, rms radial error=161.807, avgs=0 090/300: dt: 0.9000, rms radial error=160.897, avgs=0 095/300: dt: 0.9000, rms radial error=159.993, avgs=0 100/300: dt: 0.9000, rms radial error=159.094, avgs=0 105/300: dt: 0.9000, rms radial error=158.199, avgs=0 110/300: dt: 0.9000, rms radial error=157.310, avgs=0 115/300: dt: 0.9000, rms radial error=156.425, avgs=0 120/300: dt: 0.9000, rms radial error=155.546, avgs=0 125/300: dt: 0.9000, rms radial error=154.670, avgs=0 130/300: dt: 0.9000, rms radial error=153.802, avgs=0 135/300: dt: 0.9000, rms radial error=152.940, avgs=0 140/300: dt: 0.9000, rms radial error=152.082, avgs=0 145/300: dt: 0.9000, rms radial error=151.230, avgs=0 150/300: dt: 0.9000, rms radial error=150.382, avgs=0 155/300: dt: 0.9000, rms radial error=149.538, avgs=0 160/300: dt: 0.9000, rms radial error=148.700, avgs=0 165/300: dt: 0.9000, rms radial error=147.866, avgs=0 170/300: dt: 0.9000, rms radial error=147.037, avgs=0 175/300: dt: 0.9000, rms radial error=146.212, avgs=0 180/300: dt: 0.9000, rms radial error=145.393, avgs=0 185/300: dt: 0.9000, rms radial error=144.577, avgs=0 190/300: dt: 0.9000, rms radial error=143.767, avgs=0 195/300: dt: 0.9000, rms radial error=142.960, avgs=0 200/300: dt: 0.9000, rms radial error=142.158, avgs=0 205/300: dt: 0.9000, rms radial error=141.361, avgs=0 210/300: dt: 0.9000, rms radial error=140.567, avgs=0 215/300: dt: 0.9000, rms radial error=139.778, avgs=0 220/300: dt: 0.9000, rms radial error=138.993, avgs=0 225/300: dt: 0.9000, rms radial error=138.213, avgs=0 230/300: dt: 0.9000, rms radial error=137.437, avgs=0 235/300: dt: 0.9000, rms radial error=136.665, avgs=0 240/300: dt: 0.9000, rms radial error=135.897, avgs=0 245/300: dt: 0.9000, rms radial error=135.134, avgs=0 250/300: dt: 0.9000, rms radial error=134.375, avgs=0 255/300: dt: 0.9000, rms radial error=133.620, avgs=0 260/300: dt: 0.9000, rms radial error=132.869, avgs=0 265/300: dt: 0.9000, rms radial error=132.122, avgs=0 270/300: dt: 0.9000, rms radial error=131.380, avgs=0 275/300: dt: 0.9000, rms radial error=130.641, avgs=0 280/300: dt: 0.9000, rms radial error=129.907, avgs=0 285/300: dt: 0.9000, rms radial error=129.177, avgs=0 290/300: dt: 0.9000, rms radial error=128.450, avgs=0 295/300: dt: 0.9000, rms radial error=127.728, avgs=0 300/300: dt: 0.9000, rms radial error=127.010, avgs=0 spherical inflation complete. epoch 1 (K=10.0), pass 1, starting sse = 11514.71 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.01/13 = 0.00062 epoch 2 (K=40.0), pass 1, starting sse = 1673.14 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.02/13 = 0.00131 epoch 3 (K=160.0), pass 1, starting sse = 121.67 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.25/22 = 0.01149 epoch 4 (K=640.0), pass 1, starting sse = 3.25 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.19/23 = 0.00841 final distance error %100000.00 writing spherical brain to ../surf/lh.qsphere.nofix spherical transformation took 0.0258 hours FSRUNTIME@ mris_sphere 0.0258 hours 1 threads #VMPC# mris_sphere VmPeak 433848 mris_sphere done @#@FSTIME 2023:06:14:18:35:48 mris_sphere N 9 e 92.89 S 3.76 U 88.96 P 99% M 169612 F 11 R 2911387 W 0 c 1530 w 938 I 1744 O 0 L 1.46 1.18 1.06 @#@FSLOADPOST 2023:06:14:18:37:21 mris_sphere N 9 1.04 1.12 1.05 #-------------------------------------------- #@# QSphere rh Wed Jun 14 18:37:21 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_sphere -q -p 6 -a 128 -seed 1234 ../surf/rh.inflated.nofix ../surf/rh.qsphere.nofix doing quick spherical unfolding. limitting unfolding to 6 passes using n_averages = 128 setting seed for random number genererator to 1234 version: 7.2.0 available threads: 1 scaling brain by 0.355... inflating... projecting onto sphere... surface projected - minimizing metric distortion... vertex spacing 1.13 +- 0.62 (0.00-->6.27) (max @ vno 84690 --> 84691) face area 0.03 +- 0.04 (-0.07-->0.73) Entering MRISinflateToSphere() inflating to sphere (rms error < 2.00) 000: dt: 0.0000, rms radial error=175.849, avgs=0 005/300: dt: 0.9000, rms radial error=175.593, avgs=0 010/300: dt: 0.9000, rms radial error=175.042, avgs=0 015/300: dt: 0.9000, rms radial error=174.319, avgs=0 020/300: dt: 0.9000, rms radial error=173.495, avgs=0 025/300: dt: 0.9000, rms radial error=172.614, avgs=0 030/300: dt: 0.9000, rms radial error=171.703, avgs=0 035/300: dt: 0.9000, rms radial error=170.775, avgs=0 040/300: dt: 0.9000, rms radial error=169.839, avgs=0 045/300: dt: 0.9000, rms radial error=168.900, avgs=0 050/300: dt: 0.9000, rms radial error=167.962, avgs=0 055/300: dt: 0.9000, rms radial error=167.026, avgs=0 060/300: dt: 0.9000, rms radial error=166.094, avgs=0 065/300: dt: 0.9000, rms radial error=165.166, avgs=0 070/300: dt: 0.9000, rms radial error=164.242, avgs=0 075/300: dt: 0.9000, rms radial error=163.323, avgs=0 080/300: dt: 0.9000, rms radial error=162.409, avgs=0 085/300: dt: 0.9000, rms radial error=161.502, avgs=0 090/300: dt: 0.9000, rms radial error=160.600, avgs=0 095/300: dt: 0.9000, rms radial error=159.703, avgs=0 100/300: dt: 0.9000, rms radial error=158.811, avgs=0 105/300: dt: 0.9000, rms radial error=157.924, avgs=0 110/300: dt: 0.9000, rms radial error=157.041, avgs=0 115/300: dt: 0.9000, rms radial error=156.163, avgs=0 120/300: dt: 0.9000, rms radial error=155.289, avgs=0 125/300: dt: 0.9000, rms radial error=154.420, avgs=0 130/300: dt: 0.9000, rms radial error=153.555, avgs=0 135/300: dt: 0.9000, rms radial error=152.695, avgs=0 140/300: dt: 0.9000, rms radial error=151.840, avgs=0 145/300: dt: 0.9000, rms radial error=150.989, avgs=0 150/300: dt: 0.9000, rms radial error=150.142, avgs=0 155/300: dt: 0.9000, rms radial error=149.300, avgs=0 160/300: dt: 0.9000, rms radial error=148.463, avgs=0 165/300: dt: 0.9000, rms radial error=147.630, avgs=0 170/300: dt: 0.9000, rms radial error=146.801, avgs=0 175/300: dt: 0.9000, rms radial error=145.977, avgs=0 180/300: dt: 0.9000, rms radial error=145.157, avgs=0 185/300: dt: 0.9000, rms radial error=144.342, avgs=0 190/300: dt: 0.9000, rms radial error=143.531, avgs=0 195/300: dt: 0.9000, rms radial error=142.725, avgs=0 200/300: dt: 0.9000, rms radial error=141.923, avgs=0 205/300: dt: 0.9000, rms radial error=141.125, avgs=0 210/300: dt: 0.9000, rms radial error=140.332, avgs=0 215/300: dt: 0.9000, rms radial error=139.543, avgs=0 220/300: dt: 0.9000, rms radial error=138.758, avgs=0 225/300: dt: 0.9000, rms radial error=137.978, avgs=0 230/300: dt: 0.9000, rms radial error=137.202, avgs=0 235/300: dt: 0.9000, rms radial error=136.430, avgs=0 240/300: dt: 0.9000, rms radial error=135.663, avgs=0 245/300: dt: 0.9000, rms radial error=134.900, avgs=0 250/300: dt: 0.9000, rms radial error=134.141, avgs=0 255/300: dt: 0.9000, rms radial error=133.386, avgs=0 260/300: dt: 0.9000, rms radial error=132.635, avgs=0 265/300: dt: 0.9000, rms radial error=131.889, avgs=0 270/300: dt: 0.9000, rms radial error=131.147, avgs=0 275/300: dt: 0.9000, rms radial error=130.409, avgs=0 280/300: dt: 0.9000, rms radial error=129.675, avgs=0 285/300: dt: 0.9000, rms radial error=128.945, avgs=0 290/300: dt: 0.9000, rms radial error=128.219, avgs=0 295/300: dt: 0.9000, rms radial error=127.497, avgs=0 300/300: dt: 0.9000, rms radial error=126.779, avgs=0 spherical inflation complete. epoch 1 (K=10.0), pass 1, starting sse = 11831.90 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.01/13 = 0.00052 epoch 2 (K=40.0), pass 1, starting sse = 1703.39 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.02/13 = 0.00126 epoch 3 (K=160.0), pass 1, starting sse = 127.97 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.24/21 = 0.01160 epoch 4 (K=640.0), pass 1, starting sse = 4.54 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.24/33 = 0.00735 final distance error %100000.00 writing spherical brain to ../surf/rh.qsphere.nofix spherical transformation took 0.0288 hours FSRUNTIME@ mris_sphere 0.0288 hours 1 threads #VMPC# mris_sphere VmPeak 439396 mris_sphere done @#@FSTIME 2023:06:14:18:37:21 mris_sphere N 9 e 103.76 S 5.25 U 98.32 P 99% M 175248 F 0 R 3373335 W 0 c 725 w 975 I 0 O 0 L 1.04 1.12 1.05 @#@FSLOADPOST 2023:06:14:18:39:04 mris_sphere N 9 1.50 1.25 1.10 #@# Fix Topology lh Wed Jun 14 18:39:04 BST 2023 mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 T1_260423_2 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=-12 (nv=103732, nf=207488, ne=311232, g=7) 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 11 iterations marking ambiguous vertices... 677 ambiguous faces found in tessellation segmenting defects... 8 defects found, arbitrating ambiguous regions... analyzing neighboring defects... 8 defects to be corrected 0 vertices coincident reading input surface /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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: -9.4381 (-4.7190) -vertex loglikelihood: -6.7222 (-3.3611) -normal dot loglikelihood: -3.6778 (-3.6778) -quad curv loglikelihood: -6.2197 (-3.1098) Total Loglikelihood : -26.0578 CORRECTING DEFECT 0 (vertices=21, convex hull=61, v0=34125) After retessellation of defect 0 (v0=34125), euler #=-5 (103309,309696,206382) : difference with theory (-5) = 0 CORRECTING DEFECT 1 (vertices=175, convex hull=160, v0=72110) After retessellation of defect 1 (v0=72110), euler #=-4 (103389,310010,206617) : difference with theory (-4) = 0 CORRECTING DEFECT 2 (vertices=57, convex hull=24, v0=72850) After retessellation of defect 2 (v0=72850), euler #=-3 (103395,310035,206637) : difference with theory (-3) = 0 CORRECTING DEFECT 3 (vertices=6, convex hull=16, v0=78370) After retessellation of defect 3 (v0=78370), euler #=-2 (103396,310042,206644) : difference with theory (-2) = 0 CORRECTING DEFECT 4 (vertices=94, convex hull=114, v0=85294) After retessellation of defect 4 (v0=85294), euler #=-1 (103436,310215,206778) : difference with theory (-1) = 0 CORRECTING DEFECT 5 (vertices=31, convex hull=76, v0=87044) After retessellation of defect 5 (v0=87044), euler #=0 (103449,310286,206837) : difference with theory (0) = 0 CORRECTING DEFECT 6 (vertices=24, convex hull=74, v0=102585) After retessellation of defect 6 (v0=102585), euler #=1 (103458,310340,206883) : difference with theory (1) = 0 CORRECTING DEFECT 7 (vertices=27, convex hull=69, v0=102831) After retessellation of defect 7 (v0=102831), euler #=2 (103474,310416,206944) : difference with theory (2) = 0 computing original vertex metric properties... storing new metric properties... computing tessellation statistics... vertex spacing 0.89 +- 0.21 (0.11-->4.97) (max @ vno 73985 --> 78038) face area -nan +- -nan (1000.00-->-1.00) performing soap bubble on retessellated vertices for 0 iterations... vertex spacing 0.89 +- 0.21 (0.11-->4.97) (max @ vno 73985 --> 78038) face area -nan +- -nan (1000.00-->-1.00) tessellation finished, orienting corrected surface... 23 mutations (29.9%), 54 crossovers (70.1%), 14 vertices were eliminated building final representation... 258 vertices and 0 faces have been removed from triangulation after topology correction, eno=2 (nv=103474, nf=206944, ne=310416, g=0) writing corrected surface to /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/lh.orig.premesh... 0.000 % of the vertices (0 vertices) exhibit an orientation change removing intersecting faces 000: 42 intersecting terminating search with 0 intersecting topology fixing took 0.4 minutes FSRUNTIME@ mris_fix_topology lh 0.0075 hours 1 threads #VMPC# mris_fix_topology VmPeak 729520 @#@FSTIME 2023:06:14:18:39:04 mris_fix_topology N 14 e 26.89 S 0.30 U 26.24 P 98% M 700140 F 12 R 205379 W 0 c 425 w 1692 I 1536 O 0 L 1.50 1.25 1.10 @#@FSLOADPOST 2023:06:14:18:39:31 mris_fix_topology N 14 1.30 1.22 1.09 #@# Fix Topology rh Wed Jun 14 18:39:31 BST 2023 mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 T1_260423_2 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=-16 (nv=107132, nf=214296, ne=321444, g=9) using quasi-homeomorphic spherical map to tessellate cortical surface... Correction of the Topology Finding true center and radius of Spherical Surface...done Surface centered at (0,0,0) with radius 100.0 in 8 iterations marking ambiguous vertices... 1751 ambiguous faces found in tessellation segmenting defects... 11 defects found, arbitrating ambiguous regions... analyzing neighboring defects... 11 defects to be corrected 0 vertices coincident reading input surface /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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: -9.4675 (-4.7338) -vertex loglikelihood: -6.7088 (-3.3544) -normal dot loglikelihood: -3.6847 (-3.6847) -quad curv loglikelihood: -6.1811 (-3.0905) Total Loglikelihood : -26.0422 CORRECTING DEFECT 0 (vertices=326, convex hull=246, v0=9941) After retessellation of defect 0 (v0=9941), euler #=-7 (106160,318140,211973) : difference with theory (-8) = -1 CORRECTING DEFECT 1 (vertices=81, convex hull=89, v0=30239) After retessellation of defect 1 (v0=30239), euler #=-6 (106174,318228,212048) : difference with theory (-7) = -1 CORRECTING DEFECT 2 (vertices=103, convex hull=124, v0=42519) After retessellation of defect 2 (v0=42519), euler #=-5 (106236,318468,212227) : difference with theory (-6) = -1 CORRECTING DEFECT 3 (vertices=75, convex hull=70, v0=74163) After retessellation of defect 3 (v0=74163), euler #=-4 (106261,318571,212306) : difference with theory (-5) = -1 CORRECTING DEFECT 4 (vertices=80, convex hull=36, v0=77713) After retessellation of defect 4 (v0=77713), euler #=-3 (106272,318615,212340) : difference with theory (-4) = -1 CORRECTING DEFECT 5 (vertices=99, convex hull=131, v0=77935) After retessellation of defect 5 (v0=77935), euler #=-2 (106317,318813,212494) : difference with theory (-3) = -1 CORRECTING DEFECT 6 (vertices=20, convex hull=50, v0=84666) After retessellation of defect 6 (v0=84666), euler #=-2 (106328,318866,212536) : difference with theory (-2) = 0 CORRECTING DEFECT 7 (vertices=33, convex hull=67, v0=93390) After retessellation of defect 7 (v0=93390), euler #=-1 (106346,318946,212599) : difference with theory (-1) = 0 CORRECTING DEFECT 8 (vertices=137, convex hull=69, v0=102109) After retessellation of defect 8 (v0=102109), euler #=0 (106384,319088,212704) : difference with theory (0) = 0 CORRECTING DEFECT 9 (vertices=83, convex hull=70, v0=105811) After retessellation of defect 9 (v0=105811), euler #=1 (106418,319223,212806) : difference with theory (1) = 0 CORRECTING DEFECT 10 (vertices=39, convex hull=58, v0=106146) After retessellation of defect 10 (v0=106146), euler #=2 (106437,319305,212870) : difference with theory (2) = 0 computing original vertex metric properties... storing new metric properties... computing tessellation statistics... vertex spacing 0.89 +- 0.22 (0.04-->8.70) (max @ vno 30869 --> 36635) face area -nan +- -nan (1000.00-->-1.00) performing soap bubble on retessellated vertices for 0 iterations... vertex spacing 0.89 +- 0.22 (0.04-->8.70) (max @ vno 30869 --> 36635) face area -nan +- -nan (1000.00-->-1.00) tessellation finished, orienting corrected surface... 22 mutations (27.5%), 58 crossovers (72.5%), 46 vertices were eliminated building final representation... 695 vertices and 0 faces have been removed from triangulation after topology correction, eno=2 (nv=106437, nf=212870, ne=319305, g=0) writing corrected surface to /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/rh.orig.premesh... 0.000 % of the vertices (0 vertices) exhibit an orientation change removing intersecting faces 000: 66 intersecting 001: 16 intersecting terminating search with 0 intersecting topology fixing took 0.8 minutes FSRUNTIME@ mris_fix_topology rh 0.0140 hours 1 threads #VMPC# mris_fix_topology VmPeak 733284 @#@FSTIME 2023:06:14:18:39:31 mris_fix_topology N 14 e 50.27 S 0.32 U 49.55 P 99% M 703636 F 0 R 206844 W 0 c 831 w 1742 I 0 O 0 L 1.30 1.22 1.09 @#@FSLOADPOST 2023:06:14:18:40:22 mris_fix_topology N 14 1.13 1.19 1.09 mris_euler_number ../surf/lh.orig.premesh euler # = v-e+f = 2g-2: 103474 - 310416 + 206944 = 2 --> 0 holes F =2V-4: 206944 = 206948-4 (0) 2E=3F: 620832 = 620832 (0) total defect index = 0 mris_euler_number ../surf/rh.orig.premesh euler # = v-e+f = 2g-2: 106437 - 319305 + 212870 = 2 --> 0 holes F =2V-4: 212870 = 212874-4 (0) 2E=3F: 638610 = 638610 (0) total defect index = 0 Wed Jun 14 18:40:23 BST 2023 setenv SUBJECTS_DIR /media/sf_VBOX/T1_FreeSurfer cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts /home/varun/freesurfer/bin/defect2seg --s T1_260423_2 freesurfer-linux-ubuntu18_x86_64-7.2.0-20210721-aa8f76b defect2seg 7.2.0 Linux Ubuntu 5.19.0-43-generic #44~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon May 22 13:39:36 UTC 2 x86_64 x86_64 x86_64 GNU/Linux pid 26410 mri_label2vol --defects /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/lh.orig.nofix /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/lh.defect_labels /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/orig.mgz 1000 0 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/surface.defects.mgz mris_defects_pointset -s /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/lh.orig.nofix -d /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/lh.defect_labels -o /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/lh.defects.pointset Reading in surface /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/lh.orig.nofix Reading in defect segmentation /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/lh.defect_labels #VMPC# mris_defects_pointset 135744 mris_defects_pointset done mri_label2vol --defects /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/rh.orig.nofix /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/rh.defect_labels /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/surface.defects.mgz 2000 1 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri/surface.defects.mgz mris_defects_pointset -s /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/rh.orig.nofix -d /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/rh.defect_labels -o /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/rh.defects.pointset Reading in surface /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/rh.orig.nofix Reading in defect segmentation /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/rh.defect_labels #VMPC# mris_defects_pointset 139660 mris_defects_pointset done Started at Wed Jun 14 18:40:23 BST 2023 Ended at Wed Jun 14 18:40:27 BST 2023 Defect2seg-Run-Time-Sec 4 Defect2seg-Run-Time-Min 0.08 Defect2seg-Run-Time-Hours 0.00 tkmeditfv T1_260423_2 brain.finalsurfs.mgz -defect defect2seg Done @#@FSTIME 2023:06:14:18:40:23 defect2seg N 2 e 4.20 S 0.90 U 2.80 P 88% M 207424 F 33 R 185090 W 0 c 110 w 3058 I 5408 O 0 L 1.13 1.19 1.09 @#@FSLOADPOST 2023:06:14:18:40:27 defect2seg N 2 1.12 1.18 1.09 mris_remesh --remesh --iters 3 --input /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/lh.orig.premesh --output /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/lh.orig iters = 3 standard remeshing without target adjusted l: 0.710159 remeshing to edge length 0.710159 with 3 iterations avg qual before : 0.893809 after: 0.970988 Removing intersections Remeshed surface quality stats nv0 = 103474 nv = 108506 1.04863 Area 217008 0.30214 0.03381 0.075030 0.4744 Corner 651024 60.00000 8.83296 18.815332 141.0091 Edge 325512 0.84334 0.08261 0.451451 1.3239 Hinge 325512 9.32829 9.75142 0.000001 133.6701 mris_remesh done @#@FSTIME 2023:06:14:18:40:27 mris_remesh N 7 e 19.93 S 4.35 U 15.46 P 99% M 587588 F 21 R 228152 W 0 c 90 w 939 I 3584 O 0 L 1.12 1.18 1.09 @#@FSLOADPOST 2023:06:14:18:40:47 mris_remesh N 7 1.16 1.19 1.09 mris_remesh --remesh --iters 3 --input /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/rh.orig.premesh --output /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf/rh.orig iters = 3 standard remeshing without target adjusted l: 0.710999 remeshing to edge length 0.710999 with 3 iterations avg qual before : 0.891189 after: 0.971015 Removing intersections Remeshed surface quality stats nv0 = 106437 nv = 111201 1.04476 Area 222398 0.30280 0.03375 0.076787 0.4520 Corner 667194 60.00000 8.82936 14.927054 149.4115 Edge 333597 0.84425 0.08261 0.472875 1.2642 Hinge 333597 9.34767 9.79546 0.000000 143.1805 mris_remesh done @#@FSTIME 2023:06:14:18:40:47 mris_remesh N 7 e 21.96 S 0.34 U 21.39 P 99% M 597644 F 0 R 238639 W 0 c 475 w 967 I 0 O 0 L 1.16 1.19 1.09 @#@FSLOADPOST 2023:06:14:18:41:09 mris_remesh N 7 1.11 1.17 1.09 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_remove_intersection ../surf/lh.orig ../surf/lh.orig intersection removal took 0.00 hours writing corrected surface to ../surf/lh.orig @#@FSTIME 2023:06:14:18:41:09 mris_remove_intersection N 2 e 1.85 S 1.26 U 0.46 P 93% M 280740 F 19 R 73317 W 0 c 22 w 935 I 3312 O 0 L 1.11 1.17 1.09 @#@FSLOADPOST 2023:06:14:18:41:11 mris_remove_intersection N 2 1.10 1.17 1.09 rm -f ../surf/lh.inflated /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_remove_intersection ../surf/rh.orig ../surf/rh.orig intersection removal took 0.00 hours writing corrected surface to ../surf/rh.orig @#@FSTIME 2023:06:14:18:41:11 mris_remove_intersection N 2 e 1.95 S 0.12 U 1.64 P 90% M 282716 F 0 R 73922 W 0 c 32 w 894 I 0 O 0 L 1.10 1.17 1.09 @#@FSLOADPOST 2023:06:14:18:41:13 mris_remove_intersection N 2 1.10 1.17 1.09 rm -f ../surf/rh.inflated #-------------------------------------------- #@# AutoDetGWStats lh Wed Jun 14 18:41:13 BST 2023 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri setenv SUBJECTS_DIR /media/sf_VBOX/T1_FreeSurfer mris_autodet_gwstats --o ../surf/autodet.gw.stats.lh.dat --i brain.finalsurfs.mgz --wm wm.mgz --surf ../surf/lh.orig.premesh border white: 193489 voxels (1.15%) border gray 218132 voxels (1.30%) 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=10878, wmmin=5, clip=110 Binarizing thresholding at 5 computing class statistics... low=30, hi=110.000000 CCS WM (97.0): 97.1 +- 9.3 [70.0 --> 110.0] CCS GM (67.0) : 66.5 +- 12.2 [30.0 --> 110.0] white_mean = 97.1233 +/- 9.29737, gray_mean = 66.4713 +/- 12.2236 using class modes intead of means, discounting robust sigmas.... MRIScomputeClassModes(): min=0 max=145 nbins=146 intensity peaks found at WM=102+-5.2, GM=58+-9.6 white_mode = 102, gray_mode = 58 std_scale = 1 Applying sanity checks, max_scale_down = 0.2 setting MIN_GRAY_AT_WHITE_BORDER to 45.8 (was 70.000000) setting MAX_BORDER_WHITE to 111.3 (was 105.000000) setting MIN_BORDER_WHITE to 58.0 (was 85.000000) setting MAX_CSF to 33.6 (was 40.000000) setting MAX_GRAY to 92.7 (was 95.000000) setting MAX_GRAY_AT_CSF_BORDER to 45.8 (was 75.000000) setting MIN_GRAY_AT_CSF_BORDER to 21.3 (was 40.000000) When placing the white surface white_border_hi = 111.297; white_border_low = 58; white_outside_low = 45.7764; white_inside_hi = 120; white_outside_hi = 111.297; When placing the pial surface pial_border_hi = 45.7764; pial_border_low = 21.3292; pial_outside_low = 10; pial_inside_hi = 92.7026; pial_outside_hi = 39.6646; #VMPC# mris_autodet_gwstats VmPeak 223492 mris_autodet_gwstats done @#@FSTIME 2023:06:14:18:41:13 mris_autodet_gwstats N 8 e 3.29 S 0.06 U 3.08 P 95% M 197856 F 19 R 55581 W 0 c 57 w 757 I 3824 O 0 L 1.10 1.17 1.09 @#@FSLOADPOST 2023:06:14:18:41:16 mris_autodet_gwstats N 8 1.09 1.17 1.09 #-------------------------------------------- #@# AutoDetGWStats rh Wed Jun 14 18:41:16 BST 2023 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri setenv SUBJECTS_DIR /media/sf_VBOX/T1_FreeSurfer mris_autodet_gwstats --o ../surf/autodet.gw.stats.rh.dat --i brain.finalsurfs.mgz --wm wm.mgz --surf ../surf/rh.orig.premesh border white: 193489 voxels (1.15%) border gray 218132 voxels (1.30%) 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=10878, wmmin=5, clip=110 Binarizing thresholding at 5 computing class statistics... low=30, hi=110.000000 CCS WM (97.0): 97.1 +- 9.3 [70.0 --> 110.0] CCS GM (67.0) : 66.5 +- 12.2 [30.0 --> 110.0] white_mean = 97.1233 +/- 9.29737, gray_mean = 66.4713 +/- 12.2236 using class modes intead of means, discounting robust sigmas.... MRIScomputeClassModes(): min=0 max=145 nbins=146 intensity peaks found at WM=102+-4.3, GM=58+-9.6 white_mode = 102, gray_mode = 58 std_scale = 1 Applying sanity checks, max_scale_down = 0.2 setting MIN_GRAY_AT_WHITE_BORDER to 45.8 (was 70.000000) setting MAX_BORDER_WHITE to 111.3 (was 105.000000) setting MIN_BORDER_WHITE to 58.0 (was 85.000000) setting MAX_CSF to 33.6 (was 40.000000) setting MAX_GRAY to 92.7 (was 95.000000) setting MAX_GRAY_AT_CSF_BORDER to 45.8 (was 75.000000) setting MIN_GRAY_AT_CSF_BORDER to 21.3 (was 40.000000) When placing the white surface white_border_hi = 111.297; white_border_low = 58; white_outside_low = 45.7764; white_inside_hi = 120; white_outside_hi = 111.297; When placing the pial surface pial_border_hi = 45.7764; pial_border_low = 21.3292; pial_outside_low = 10; pial_inside_hi = 92.7026; pial_outside_hi = 39.6646; #VMPC# mris_autodet_gwstats VmPeak 227588 mris_autodet_gwstats done @#@FSTIME 2023:06:14:18:41:16 mris_autodet_gwstats N 8 e 3.31 S 0.09 U 3.08 P 95% M 202032 F 0 R 57221 W 0 c 61 w 758 I 0 O 0 L 1.09 1.17 1.09 @#@FSLOADPOST 2023:06:14:18:41:20 mris_autodet_gwstats N 8 1.09 1.17 1.09 #-------------------------------------------- #@# WhitePreAparc lh Wed Jun 14 18:41:20 BST 2023 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri mris_place_surface --adgws-in ../surf/autodet.gw.stats.lh.dat --wm wm.mgz --threads 1 --invol brain.finalsurfs.mgz --lh --i ../surf/lh.orig --o ../surf/lh.white.preaparc --white --seg aseg.presurf.mgz --nsmooth 5 7.2.0 7.2.0 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri setenv SUBJECTS_DIR /media/sf_VBOX/T1_FreeSurfer mris_place_surface --adgws-in ../surf/autodet.gw.stats.lh.dat --wm wm.mgz --threads 1 --invol brain.finalsurfs.mgz --lh --i ../surf/lh.orig --o ../surf/lh.white.preaparc --white --seg aseg.presurf.mgz --nsmooth 5 Reading in input surface ../surf/lh.orig Smoothing surface with 5 iterations Area 217008 0.26856 0.06292 0.002279 0.5852 Corner 651024 60.00000 9.44262 9.152605 142.7046 Edge 325512 0.79132 0.11370 0.060270 1.2836 Hinge 325512 6.22294 6.25126 0.000029 115.5404 Not reading in aparc Reading in input volume brain.finalsurfs.mgz Reading in wm volume wm.mgz MRIclipBrightWM(): nthresh=10878, wmmin=5, clip=110 MRIfindBrightNonWM(): 452 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 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 #FML# MRISripMidline(): nmarked=6188, nmarked2=87, nripped=6188 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 MRISripSegs(): -2 2 0.5 ripped 0 vertex 54253: xyz = (-0.630713,4.69642,-6.37995) oxyz = (-0.630713,4.69642,-6.37995) wxzy = (-0.630713,4.69642,-6.37995) pxyz = (0,0,0) CBVO Creating mask 108506 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=6188 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 #FML# MRISripMidline(): nmarked=6188, nmarked2=87, nripped=6188 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 = 111.2973710; border_low = 58.0000000; outside_low = 45.7763980; outside_hi = 111.2973710; sigma = 2; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=108506 Gdiag_no=-1 vno start=0, stop=108506 Replacing 255s with 0s #SI# sigma=2 had to be increased for 120 vertices, nripped=6188 mean border=72.3, 148 (148) missing vertices, mean dist 0.5 [0.6 (%28.2)->0.9 (%71.8))] %57 local maxima, %37 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.1361 min Finding expansion regions mean absolute distance = 0.81 +- 0.95 3746 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=Ubunt, 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=4313613.5, rms=14.329 001: dt: 0.5000, sse=2503836.5, rms=10.811 (24.549%) 002: dt: 0.5000, sse=1558830.0, rms=8.407 (22.238%) 003: dt: 0.5000, sse=1030682.8, rms=6.694 (20.380%) 004: dt: 0.5000, sse=736866.9, rms=5.504 (17.779%) 005: dt: 0.5000, sse=580383.1, rms=4.742 (13.834%) 006: dt: 0.5000, sse=495454.0, rms=4.279 (9.766%) 007: dt: 0.5000, sse=450851.3, rms=4.022 (6.006%) 008: dt: 0.5000, sse=427339.7, rms=3.873 (3.701%) 009: dt: 0.5000, sse=416107.3, rms=3.800 (1.902%) 010: dt: 0.5000, sse=409281.8, rms=3.748 (1.368%) rms = 3.7299/3.7477, sse=407439.4/409281.8, time step reduction 1 of 3 to 0.250 0 0 1 011: dt: 0.5000, sse=407439.4, rms=3.730 (0.475%) 012: dt: 0.2500, sse=216162.2, rms=2.106 (43.549%) 013: dt: 0.2500, sse=182899.4, rms=1.673 (20.550%) 014: dt: 0.2500, sse=174656.5, rms=1.543 (7.765%) 015: dt: 0.2500, sse=170644.2, rms=1.475 (4.412%) rms = 1.4459/1.4749, sse=169244.9/170644.2, time step reduction 2 of 3 to 0.125 0 0 1 016: dt: 0.2500, sse=169244.9, rms=1.446 (1.965%) 017: dt: 0.1250, sse=165221.7, rms=1.373 (5.009%) rms = 1.3615/1.3735, sse=164732.3/165221.7, time step reduction 3 of 3 to 0.062 0 0 1 018: dt: 0.1250, sse=164732.2, rms=1.362 (0.871%) maximum number of reductions reached, breaking from loop positioning took 1.1 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=6188 removing 2 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6389, nmarked2=104, nripped=6389 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 = 111.2973710; border_low = 58.0000000; outside_low = 45.7763980; outside_hi = 111.2973710; sigma = 1; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=108506 Gdiag_no=-1 vno start=0, stop=108506 Replacing 255s with 0s #SI# sigma=1 had to be increased for 59 vertices, nripped=6389 mean border=76.2, 111 (55) missing vertices, mean dist -0.3 [0.4 (%73.1)->0.3 (%26.9))] %67 local maxima, %27 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0939 min Finding expansion regions mean absolute distance = 0.39 +- 0.66 3148 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=Ubunt, 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=936505.3, rms=6.118 019: dt: 0.5000, sse=515248.1, rms=4.089 (33.162%) 020: dt: 0.5000, sse=507670.5, rms=3.945 (3.522%) 021: dt: 0.5000, sse=456580.1, rms=3.725 (5.586%) rms = 3.8463/3.7246, sse=478869.1/456580.1, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 022: dt: 0.2500, sse=297956.4, rms=2.457 (34.027%) 023: dt: 0.2500, sse=242179.0, rms=1.769 (28.023%) 024: dt: 0.2500, sse=225905.8, rms=1.463 (17.306%) 025: dt: 0.2500, sse=216108.9, rms=1.327 (9.273%) 026: dt: 0.2500, sse=210027.3, rms=1.238 (6.710%) 027: dt: 0.2500, sse=209922.6, rms=1.188 (4.040%) rms = 1.1534/1.1879, sse=225978.0/209922.6, time step reduction 2 of 3 to 0.125 0 1 1 028: dt: 0.2500, sse=225978.0, rms=1.153 (2.907%) 029: dt: 0.1250, sse=203598.9, rms=1.097 (4.877%) rms = 1.0890/1.0971, sse=202588.9/203598.9, time step reduction 3 of 3 to 0.062 0 0 1 030: dt: 0.1250, sse=202588.9, rms=1.089 (0.737%) maximum number of reductions reached, breaking from loop positioning took 0.7 minutes Iteration 2 ========================================= n_averages=1, current_sigma=0.5 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6389 removing 3 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6404, nmarked2=104, nripped=6404 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 = 111.2973710; border_low = 58.0000000; outside_low = 45.7763980; outside_hi = 111.2973710; 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=108506 Gdiag_no=-1 vno start=0, stop=108506 Replacing 255s with 0s #SI# sigma=0.5 had to be increased for 64 vertices, nripped=6404 mean border=78.9, 129 (51) missing vertices, mean dist -0.2 [0.3 (%68.8)->0.2 (%31.2))] %76 local maxima, %18 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0540 min Finding expansion regions mean absolute distance = 0.28 +- 0.48 2744 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=Ubunt, 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=465278.0, rms=3.785 031: dt: 0.5000, sse=350610.8, rms=2.914 (23.000%) rms = 3.2769/2.9142, sse=396059.2/350610.8, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 032: dt: 0.2500, sse=245082.9, rms=1.860 (36.190%) 033: dt: 0.2500, sse=205874.2, rms=1.280 (31.158%) 034: dt: 0.2500, sse=198259.2, rms=1.085 (15.277%) rms = 1.0433/1.0846, sse=195803.5/198259.2, time step reduction 2 of 3 to 0.125 0 0 1 035: dt: 0.2500, sse=195803.5, rms=1.043 (3.803%) 036: dt: 0.1250, sse=189886.0, rms=0.976 (6.490%) rms = 0.9696/0.9756, sse=188907.1/189886.0, time step reduction 3 of 3 to 0.062 0 0 1 037: dt: 0.1250, sse=188907.2, rms=0.970 (0.618%) maximum number of reductions reached, breaking from loop positioning took 0.4 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=6404 removing 3 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6422, nmarked2=104, nripped=6422 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 = 111.2973710; border_low = 58.0000000; outside_low = 45.7763980; outside_hi = 111.2973710; 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=108506 Gdiag_no=-1 vno start=0, stop=108506 Replacing 255s with 0s #SI# sigma=0.25 had to be increased for 64 vertices, nripped=6422 mean border=80.0, 134 (47) missing vertices, mean dist -0.1 [0.3 (%57.1)->0.2 (%42.9))] %80 local maxima, %14 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0340 min Finding expansion regions mean absolute distance = 0.24 +- 0.38 2488 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=Ubunt, 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=239115.6, rms=1.889 rms = 2.1006/1.8890, sse=260747.0/239115.6, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 038: dt: 0.2500, sse=196638.0, rms=1.233 (34.752%) 039: dt: 0.2500, sse=181701.9, rms=0.788 (36.055%) rms = 0.7570/0.7881, sse=178890.5/181701.9, time step reduction 2 of 3 to 0.125 0 0 1 040: dt: 0.2500, sse=178890.5, rms=0.757 (3.955%) rms = 0.7419/0.7570, sse=177890.3/178890.5, time step reduction 3 of 3 to 0.062 0 0 1 041: dt: 0.1250, sse=177890.3, rms=0.742 (1.991%) maximum number of reductions reached, breaking from loop positioning took 0.3 minutes Writing output to ../surf/lh.white.preaparc #ET# mris_place_surface 2.92 minutes #VMPC# mris_place_surfaces VmPeak 1948988 mris_place_surface done @#@FSTIME 2023:06:14:18:41:20 mris_place_surface N 18 e 180.79 S 0.52 U 180.00 P 99% M 1685068 F 12 R 441491 W 0 c 2009 w 1170 I 1928 O 0 L 1.09 1.17 1.09 @#@FSLOADPOST 2023:06:14:18:44:21 mris_place_surface N 18 1.08 1.12 1.09 #-------------------------------------------- #@# WhitePreAparc rh Wed Jun 14 18:44:21 BST 2023 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri mris_place_surface --adgws-in ../surf/autodet.gw.stats.rh.dat --wm wm.mgz --threads 1 --invol brain.finalsurfs.mgz --rh --i ../surf/rh.orig --o ../surf/rh.white.preaparc --white --seg aseg.presurf.mgz --nsmooth 5 7.2.0 7.2.0 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/mri setenv SUBJECTS_DIR /media/sf_VBOX/T1_FreeSurfer mris_place_surface --adgws-in ../surf/autodet.gw.stats.rh.dat --wm wm.mgz --threads 1 --invol brain.finalsurfs.mgz --rh --i ../surf/rh.orig --o ../surf/rh.white.preaparc --white --seg aseg.presurf.mgz --nsmooth 5 Reading in input surface ../surf/rh.orig Smoothing surface with 5 iterations Area 222398 0.26900 0.06333 0.014369 0.6116 Corner 667194 60.00000 9.42340 11.680297 141.0429 Edge 333597 0.79186 0.11435 0.157763 1.2798 Hinge 333597 6.22741 6.32734 0.000006 101.0773 Not reading in aparc Reading in input volume brain.finalsurfs.mgz Reading in wm volume wm.mgz MRIclipBrightWM(): nthresh=10878, wmmin=5, clip=110 MRIfindBrightNonWM(): 452 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 4 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6508, nmarked2=0, nripped=6508 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 MRISripSegs(): -2 2 0.5 ripped 0 vertex 55601: xyz = (54.0246,4.83903,-13.152) oxyz = (54.0246,4.83903,-13.152) wxzy = (54.0246,4.83903,-13.152) pxyz = (0,0,0) CBVO Creating mask 111201 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=6508 removing 3 vertices from ripped group in thread:0 removing 4 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6508, nmarked2=0, nripped=6508 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 = 111.2973710; border_low = 58.0000000; outside_low = 45.7763980; outside_hi = 111.2973710; sigma = 2; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=111201 Gdiag_no=-1 vno start=0, stop=111201 Replacing 255s with 0s #SI# sigma=2 had to be increased for 112 vertices, nripped=6508 mean border=72.6, 61 (61) missing vertices, mean dist 0.5 [0.6 (%28.2)->0.9 (%71.8))] %59 local maxima, %35 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.1417 min Finding expansion regions mean absolute distance = 0.79 +- 0.93 3778 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=Ubunt, 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=4395408.5, rms=14.292 001: dt: 0.5000, sse=2531849.2, rms=10.740 (24.854%) 002: dt: 0.5000, sse=1581306.1, rms=8.364 (22.122%) 003: dt: 0.5000, sse=1046790.6, rms=6.661 (20.359%) 004: dt: 0.5000, sse=749686.5, rms=5.490 (17.582%) 005: dt: 0.5000, sse=589560.1, rms=4.724 (13.955%) 006: dt: 0.5000, sse=505897.3, rms=4.279 (9.428%) 007: dt: 0.5000, sse=462499.0, rms=4.007 (6.352%) 008: dt: 0.5000, sse=438406.0, rms=3.877 (3.247%) 009: dt: 0.5000, sse=424164.8, rms=3.782 (2.436%) rms = 3.7527/3.7822, sse=421895.9/424164.8, time step reduction 1 of 3 to 0.250 0 0 1 010: dt: 0.5000, sse=421895.9, rms=3.753 (0.782%) 011: dt: 0.2500, sse=221517.7, rms=2.114 (43.679%) 012: dt: 0.2500, sse=187455.8, rms=1.679 (20.542%) 013: dt: 0.2500, sse=177485.7, rms=1.536 (8.527%) 014: dt: 0.2500, sse=173202.7, rms=1.461 (4.872%) rms = 1.4194/1.4613, sse=170586.0/173202.7, time step reduction 2 of 3 to 0.125 0 0 1 015: dt: 0.2500, sse=170586.0, rms=1.419 (2.870%) 016: dt: 0.1250, sse=166774.4, rms=1.348 (4.994%) rms = 1.3357/1.3485, sse=166052.6/166774.4, time step reduction 3 of 3 to 0.062 0 0 1 017: dt: 0.1250, sse=166052.6, rms=1.336 (0.948%) maximum number of reductions reached, breaking from loop positioning took 1.0 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=6508 removing 3 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6540, nmarked2=0, nripped=6540 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 = 111.2973710; border_low = 58.0000000; outside_low = 45.7763980; outside_hi = 111.2973710; sigma = 1; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 CBVfindFirstPeakD1=0 CBVfindFirstPeakD2=0 nvertices=111201 Gdiag_no=-1 vno start=0, stop=111201 Replacing 255s with 0s #SI# sigma=1 had to be increased for 43 vertices, nripped=6540 mean border=76.5, 64 (21) missing vertices, mean dist -0.2 [0.4 (%73.2)->0.3 (%26.8))] %67 local maxima, %27 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0969 min Finding expansion regions mean absolute distance = 0.38 +- 0.68 2896 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=Ubunt, 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=944225.8, rms=6.061 018: dt: 0.5000, sse=521190.3, rms=4.056 (33.086%) 019: dt: 0.5000, sse=499224.7, rms=3.943 (2.773%) 020: dt: 0.5000, sse=466469.2, rms=3.740 (5.150%) rms = 3.8523/3.7404, sse=484675.0/466469.2, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 021: dt: 0.2500, sse=299533.1, rms=2.444 (34.664%) 022: dt: 0.2500, sse=238757.6, rms=1.740 (28.804%) 023: dt: 0.2500, sse=217933.0, rms=1.423 (18.193%) 024: dt: 0.2500, sse=210994.3, rms=1.280 (10.040%) 025: dt: 0.2500, sse=205654.2, rms=1.199 (6.394%) rms = 1.1581/1.1986, sse=204214.0/205654.2, time step reduction 2 of 3 to 0.125 0 0 1 026: dt: 0.2500, sse=204214.0, rms=1.158 (3.375%) 027: dt: 0.1250, sse=201243.8, rms=1.097 (5.255%) rms = 1.0867/1.0973, sse=201464.8/201243.8, time step reduction 3 of 3 to 0.062 0 1 1 028: dt: 0.1250, sse=201464.8, rms=1.087 (0.962%) maximum number of reductions reached, breaking from loop positioning took 0.7 minutes Iteration 2 ========================================= n_averages=1, current_sigma=0.5 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6540 removing 2 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6575, nmarked2=0, nripped=6575 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 = 111.2973710; border_low = 58.0000000; outside_low = 45.7763980; outside_hi = 111.2973710; 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=111201 Gdiag_no=-1 vno start=0, stop=111201 Replacing 255s with 0s #SI# sigma=0.5 had to be increased for 55 vertices, nripped=6575 mean border=79.3, 66 (16) missing vertices, mean dist -0.2 [0.3 (%69.2)->0.2 (%30.8))] %76 local maxima, %18 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0541 min Finding expansion regions mean absolute distance = 0.29 +- 0.49 2655 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=Ubunt, 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=499583.2, rms=3.962 029: dt: 0.5000, sse=363777.9, rms=2.998 (24.335%) rms = 3.3642/2.9977, sse=410053.2/363777.9, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 030: dt: 0.2500, sse=256332.2, rms=1.943 (35.187%) 031: dt: 0.2500, sse=212155.5, rms=1.341 (30.964%) 032: dt: 0.2500, sse=199883.8, rms=1.110 (17.213%) 033: dt: 0.2500, sse=198484.0, rms=1.044 (6.023%) rms = 1.0120/1.0435, sse=194597.4/198484.0, time step reduction 2 of 3 to 0.125 0 0 1 034: dt: 0.2500, sse=194597.5, rms=1.012 (3.022%) 035: dt: 0.1250, sse=191133.8, rms=0.952 (5.894%) rms = 0.9499/0.9523, sse=190692.5/191133.8, time step reduction 3 of 3 to 0.062 0 0 1 036: dt: 0.1250, sse=190692.5, rms=0.950 (0.254%) 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=6575 removing 4 vertices from ripped group in thread:0 removing 2 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6586, nmarked2=0, nripped=6586 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 = 111.2973710; border_low = 58.0000000; outside_low = 45.7763980; outside_hi = 111.2973710; 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=111201 Gdiag_no=-1 vno start=0, stop=111201 Replacing 255s with 0s #SI# sigma=0.25 had to be increased for 71 vertices, nripped=6586 mean border=80.5, 92 (9) missing vertices, mean dist -0.1 [0.3 (%57.3)->0.2 (%42.7))] %80 local maxima, %14 large gradients and % 0 min vals, 0 gradients ignored nFirstPeakD1 0 MRIScomputeBorderValues_new() finished in 0.0351 min Finding expansion regions mean absolute distance = 0.24 +- 0.39 2511 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=Ubunt, 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=241636.1, rms=1.872 rms = 2.1465/1.8715, sse=268363.6/241636.1, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 037: dt: 0.2500, sse=197802.6, rms=1.181 (36.881%) 038: dt: 0.2500, sse=181041.5, rms=0.749 (36.635%) rms = 0.7308/0.7485, sse=180434.4/181041.5, time step reduction 2 of 3 to 0.125 0 0 1 039: dt: 0.2500, sse=180434.4, rms=0.731 (2.368%) rms = 0.7204/0.7308, sse=179321.7/180434.4, time step reduction 3 of 3 to 0.062 0 0 1 040: dt: 0.1250, sse=179321.7, rms=0.720 (1.427%) maximum number of reductions reached, breaking from loop positioning took 0.3 minutes Writing output to ../surf/rh.white.preaparc #ET# mris_place_surface 2.93 minutes #VMPC# mris_place_surfaces VmPeak 1956544 mris_place_surface done @#@FSTIME 2023:06:14:18:44:21 mris_place_surface N 18 e 181.03 S 0.60 U 180.23 P 99% M 1692616 F 0 R 447110 W 0 c 1823 w 1266 I 0 O 0 L 1.08 1.12 1.09 @#@FSLOADPOST 2023:06:14:18:47:22 mris_place_surface N 18 1.01 1.07 1.08 #-------------------------------------------- #@# CortexLabel lh Wed Jun 14 18:47:22 BST 2023 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 7633 vertices erasing segment 0 (vno[0] = 21160) erasing segment 2 (vno[0] = 56735) erasing segment 3 (vno[0] = 64250) @#@FSTIME 2023:06:14:18:47:22 mri_label2label N 5 e 9.49 S 0.14 U 9.14 P 97% M 279560 F 7 R 81527 W 0 c 121 w 1083 I 1200 O 0 L 1.01 1.07 1.08 @#@FSLOADPOST 2023:06:14:18:47:31 mri_label2label N 5 1.01 1.07 1.07 #-------------------------------------------- #@# CortexLabel+HipAmyg lh Wed Jun 14 18:47:31 BST 2023 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 7 non-cortical segments detected only using segment with 5641 vertices erasing segment 0 (vno[0] = 21160) erasing segment 2 (vno[0] = 36652) erasing segment 3 (vno[0] = 38285) erasing segment 4 (vno[0] = 41723) erasing segment 5 (vno[0] = 64250) erasing segment 6 (vno[0] = 89788) @#@FSTIME 2023:06:14:18:47:31 mri_label2label N 5 e 9.55 S 0.14 U 9.21 P 97% M 290956 F 0 R 84421 W 0 c 136 w 1130 I 0 O 0 L 1.01 1.07 1.07 @#@FSLOADPOST 2023:06:14:18:47:41 mri_label2label N 5 1.01 1.07 1.07 #-------------------------------------------- #@# CortexLabel rh Wed Jun 14 18:47:41 BST 2023 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 7 non-cortical segments detected only using segment with 7682 vertices erasing segment 1 (vno[0] = 62749) erasing segment 2 (vno[0] = 65586) erasing segment 3 (vno[0] = 67989) erasing segment 4 (vno[0] = 91278) erasing segment 5 (vno[0] = 95258) erasing segment 6 (vno[0] = 110715) @#@FSTIME 2023:06:14:18:47:41 mri_label2label N 5 e 10.01 S 0.14 U 9.66 P 97% M 294852 F 0 R 85657 W 0 c 209 w 1097 I 0 O 0 L 1.01 1.07 1.07 @#@FSLOADPOST 2023:06:14:18:47:51 mri_label2label N 5 1.01 1.06 1.07 #-------------------------------------------- #@# CortexLabel+HipAmyg rh Wed Jun 14 18:47:51 BST 2023 cd /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/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 8 non-cortical segments detected only using segment with 5430 vertices erasing segment 1 (vno[0] = 42085) erasing segment 2 (vno[0] = 44563) erasing segment 3 (vno[0] = 58968) erasing segment 4 (vno[0] = 66357) erasing segment 5 (vno[0] = 67989) erasing segment 6 (vno[0] = 95258) erasing segment 7 (vno[0] = 110715) @#@FSTIME 2023:06:14:18:47:51 mri_label2label N 5 e 10.03 S 0.13 U 9.67 P 97% M 298804 F 0 R 86665 W 0 c 284 w 1142 I 0 O 0 L 1.01 1.06 1.07 @#@FSLOADPOST 2023:06:14:18:48:01 mri_label2label N 5 1.00 1.06 1.07 #-------------------------------------------- #@# Smooth2 lh Wed Jun 14 18:48:01 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_smooth -n 3 -nw -seed 1234 ../surf/lh.white.preaparc ../surf/lh.smoothwm smoothing for 3 iterations setting seed for random number generator to 1234 smoothing surface tessellation for 3 iterations... smoothing complete - recomputing first and second fundamental forms... @#@FSTIME 2023:06:14:18:48:01 mris_smooth N 7 e 2.19 S 0.09 U 1.92 P 92% M 167984 F 0 R 49800 W 0 c 38 w 975 I 0 O 0 L 1.00 1.06 1.07 @#@FSLOADPOST 2023:06:14:18:48:03 mris_smooth N 7 1.00 1.06 1.07 #-------------------------------------------- #@# Smooth2 rh Wed Jun 14 18:48:03 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_smooth -n 3 -nw -seed 1234 ../surf/rh.white.preaparc ../surf/rh.smoothwm smoothing for 3 iterations setting seed for random number generator to 1234 smoothing surface tessellation for 3 iterations... smoothing complete - recomputing first and second fundamental forms... @#@FSTIME 2023:06:14:18:48:04 mris_smooth N 7 e 2.28 S 0.07 U 2.00 P 91% M 172036 F 0 R 51039 W 0 c 34 w 988 I 0 O 0 L 1.00 1.06 1.07 @#@FSLOADPOST 2023:06:14:18:48:06 mris_smooth N 7 1.00 1.06 1.07 #-------------------------------------------- #@# Inflation2 lh Wed Jun 14 18:48:06 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_inflate ../surf/lh.smoothwm ../surf/lh.inflated Reading ../surf/lh.smoothwm avg radius = 44.4 mm, total surface area = 68220 mm^2 step 000: RMS=0.168 (target=0.015) step 005: RMS=0.113 (target=0.015) step 010: RMS=0.085 (target=0.015) step 015: RMS=0.069 (target=0.015) step 020: RMS=0.057 (target=0.015) step 025: RMS=0.048 (target=0.015) step 030: RMS=0.040 (target=0.015) step 035: RMS=0.033 (target=0.015) step 040: RMS=0.028 (target=0.015) step 045: RMS=0.025 (target=0.015) step 050: RMS=0.022 (target=0.015) step 055: RMS=0.020 (target=0.015) step 060: RMS=0.019 (target=0.015) writing inflated surface to ../surf/lh.inflated writing sulcal depths to ../surf/lh.sulc inflation complete. inflation took 0.2 minutes mris_inflate utimesec 13.886043 mris_inflate stimesec 0.073170 mris_inflate ru_maxrss 168096 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 44148 mris_inflate ru_majflt 0 mris_inflate ru_nswap 0 mris_inflate ru_inblock 0 mris_inflate ru_oublock 0 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 966 mris_inflate ru_nivcsw 286 @#@FSTIME 2023:06:14:18:48:06 mris_inflate N 2 e 14.16 S 0.08 U 13.88 P 98% M 168496 F 0 R 44153 W 0 c 289 w 967 I 0 O 0 L 1.00 1.06 1.07 @#@FSLOADPOST 2023:06:14:18:48:20 mris_inflate N 2 1.00 1.06 1.07 #-------------------------------------------- #@# Inflation2 rh Wed Jun 14 18:48:20 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/scripts mris_inflate ../surf/rh.smoothwm ../surf/rh.inflated Reading ../surf/rh.smoothwm avg radius = 44.1 mm, total surface area = 69873 mm^2 step 000: RMS=0.168 (target=0.015) step 005: RMS=0.114 (target=0.015) step 010: RMS=0.085 (target=0.015) step 015: RMS=0.071 (target=0.015) step 020: RMS=0.058 (target=0.015) step 025: RMS=0.047 (target=0.015) step 030: RMS=0.039 (target=0.015) step 035: RMS=0.032 (target=0.015) step 040: RMS=0.028 (target=0.015) step 045: RMS=0.025 (target=0.015) step 050: RMS=0.022 (target=0.015) step 055: RMS=0.020 (target=0.015) step 060: RMS=0.019 (target=0.015) writing inflated surface to ../surf/rh.inflated writing sulcal depths to ../surf/rh.sulc inflation complete. inflation took 0.2 minutes mris_inflate utimesec 14.496791 mris_inflate stimesec 0.089061 mris_inflate ru_maxrss 171988 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 45237 mris_inflate ru_majflt 0 mris_inflate ru_nswap 0 mris_inflate ru_inblock 0 mris_inflate ru_oublock 0 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 1055 mris_inflate ru_nivcsw 201 @#@FSTIME 2023:06:14:18:48:20 mris_inflate N 2 e 14.79 S 0.09 U 14.49 P 98% M 172488 F 0 R 45242 W 0 c 202 w 1056 I 0 O 0 L 1.00 1.06 1.07 @#@FSLOADPOST 2023:06:14:18:48:35 mris_inflate N 2 1.14 1.09 1.08 #-------------------------------------------- #@# Curv .H and .K lh Wed Jun 14 18:48:35 BST 2023 /media/sf_VBOX/T1_FreeSurfer/T1_260423_2/surf mris_curvature -w -seed 1234 lh.white.preaparc setting seed for random number generator to 1234 total integrated curvature = 15.967*4pi (200.653) --> -15 handles ICI = 115.2, FI = 1106.0, variation=17511.531 writing Gaussian curvature to ./lh.white.preaparc.K...done. writing mean curvature to ./lh.white.preaparc.H...done. @#@FSTIME 2023:06:14:18:48:35 mris_curvature N 4 e 1.17 S 0.06 U 1.02 P 92% M 125380 F 5 R 33493 W 0 c 57 w 398 I 616 O 0 L 1.14 1.09 1.08 @#@FSLOADPOST 2023:06:14:18:48:36 mris_curvature N 4 1.13 1.09 1.08 rm -f lh.white.H ln -s lh.white.preaparc.H lh.white.H Linux Ubuntu 5.19.0-43-generic #44~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon May 22 13:39:36 UTC 2 x86_64 x86_64 x86_64 GNU/Linux recon-all -s T1_260423_2 exited with ERRORS at Wed Jun 14 18:48:36 BST 2023 To report a problem, see http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting