Wed Jul 8 13:20:58 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142 /work/bav5809/software/packages/freesurfer/bin/recon-all -sd subjects/freesurfer_output/T1/lh/SEVEREp142 -subjid SEVEREp142 -i subjects/sub-SEVEREp142/ses-T1/anat/sub-SEVEREp142_ses-T1_T1w.nii.gz -qcache -openmp 32 -all -3T -hemi lh subjid SEVEREp142 setenv SUBJECTS_DIR /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142 FREESURFER_HOME /work/bav5809/software/packages/freesurfer Actual FREESURFER_HOME /work/bav5809/software/packages/freesurfer build-stamp.txt: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b Linux node012 4.14.185-1.0.27.el7.rrz.x86_64 #1 SMP Wed Jun 24 17:36:57 CEST 2020 x86_64 x86_64 x86_64 GNU/Linux cputime unlimited filesize unlimited datasize unlimited stacksize unlimited coredumpsize unlimited memoryuse unlimited vmemoryuse unlimited descriptors 100000 memorylocked unlimited maxproc 257198 maxlocks unlimited maxsignal 257198 maxmessage 819200 maxnice 0 maxrtprio 0 maxrttime unlimited total used free shared buff/cache available Mem: 62G 602M 60G 2.0G 2.2G 59G Swap: 1.9G 1.8G 112M ######################################## program versions used 7.1.0 (freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b) 7.1.0 ProgramName: lta_convert ProgramArguments: lta_convert -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:20:59-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_and ProgramArguments: mri_and -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:00-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_annotation2label ProgramArguments: mri_annotation2label -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:01-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_aparc2aseg ProgramArguments: mri_aparc2aseg -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:03-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_surf2volseg ProgramArguments: mri_surf2volseg -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:04-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_binarize ProgramArguments: mri_binarize -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:06-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_ca_label ProgramArguments: mri_ca_label -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:07-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_ca_normalize ProgramArguments: mri_ca_normalize -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:08-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_ca_register ProgramArguments: mri_ca_register -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:10-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_cc ProgramArguments: mri_cc -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:12-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_compute_overlap ProgramArguments: mri_compute_overlap -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:13-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_compute_seg_overlap ProgramArguments: mri_compute_seg_overlap -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:13-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_concat ProgramArguments: mri_concat -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:15-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_concatenate_lta ProgramArguments: mri_concatenate_lta -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:15-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 mri_convert -all-info ProgramName: mri_convert ProgramArguments: mri_convert -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:15-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_diff ProgramArguments: mri_diff -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:17-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_edit_wm_with_aseg ProgramArguments: mri_edit_wm_with_aseg -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:18-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_em_register ProgramArguments: mri_em_register -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:19-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_fill ProgramArguments: mri_fill -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:19-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_fuse_segmentations ProgramArguments: mri_fuse_segmentations -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:21-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_fwhm ProgramArguments: mri_fwhm -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:22-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_gcut ProgramArguments: mri_gcut -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:23-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_info ProgramArguments: mri_info -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:24-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_label2label ProgramArguments: mri_label2label -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:25-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_label2vol ProgramArguments: mri_label2vol -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:26-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_log_likelihood ProgramArguments: mri_log_likelihood -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:27-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_mask ProgramArguments: mri_mask -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:28-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_matrix_multiply ProgramArguments: mri_matrix_multiply -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:29-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_normalize ProgramArguments: mri_normalize -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:29-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_normalize_tp2 ProgramArguments: mri_normalize_tp2 -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:30-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_or ProgramArguments: mri_or -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:31-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_relabel_hypointensities ProgramArguments: mri_relabel_hypointensities -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:32-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_relabel_nonwm_hypos ProgramArguments: mri_relabel_nonwm_hypos -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:33-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_remove_neck ProgramArguments: mri_remove_neck -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:34-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 7.1.0 ProgramName: mri_robust_register ProgramArguments: mri_robust_register -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:35-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 7.1.0 ProgramName: mri_robust_template ProgramArguments: mri_robust_template -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:37-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_anatomical_stats ProgramArguments: mris_anatomical_stats -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:38-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_ca_label ProgramArguments: mris_ca_label -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:39-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_calc ProgramArguments: mris_calc -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:43-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_convert ProgramArguments: mris_convert -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:43-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_curvature ProgramArguments: mris_curvature -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:44-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_curvature_stats ProgramArguments: mris_curvature_stats -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:45-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_diff ProgramArguments: mris_diff -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:45-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_divide_parcellation ProgramArguments: mris_divide_parcellation -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:48-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_segment ProgramArguments: mri_segment -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:49-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_segstats ProgramArguments: mri_segstats -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:49-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_euler_number ProgramArguments: mris_euler_number -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:52-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_fix_topology ProgramArguments: mris_fix_topology -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:53-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_topo_fixer ProgramArguments: mris_topo_fixer -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:54-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_jacobian ProgramArguments: mris_jacobian -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:54-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_label2annot ProgramArguments: mris_label2annot -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:55-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_left_right_register ProgramArguments: mris_left_right_register -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:56-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_place_surface ProgramArguments: mris_place_surface -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:57-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mrisp_paint ProgramArguments: mrisp_paint -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:57-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_register ProgramArguments: mris_register -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:21:58-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_smooth ProgramArguments: mris_smooth -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:00-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_sphere ProgramArguments: mris_sphere -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:01-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_surface_stats ProgramArguments: mris_surface_stats -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:01-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_stats2seg ProgramArguments: mri_stats2seg -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:02-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_thickness ProgramArguments: mris_thickness -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:03-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_thickness_diff ProgramArguments: mris_thickness_diff -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:04-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_topo_fixer ProgramArguments: mris_topo_fixer -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:04-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_surf2surf ProgramArguments: mri_surf2surf -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:05-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_surf2vol ProgramArguments: mri_surf2vol -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:06-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_surfcluster ProgramArguments: mri_surfcluster -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:07-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mris_volmask ProgramArguments: mris_volmask -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:08-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_tessellate ProgramArguments: mri_tessellate -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:09-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_vol2surf ProgramArguments: mri_vol2surf -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:10-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_vol2vol ProgramArguments: mri_vol2vol -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:12-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_voldiff ProgramArguments: mri_voldiff -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:12-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: mri_watershed ProgramArguments: mri_watershed -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:14-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 ProgramName: tkregister2 ProgramArguments: tkregister2_cmdl -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:14-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 mri_motion_correct.fsl 7.1.0 mri_convert -all-info ProgramName: mri_convert ProgramArguments: mri_convert -all-info ProgramVersion: 7.1.0 TimeStamp: 2020/07/08-11:22:16-GMT BuildTime: May 11 2020 13:48:51 BuildStamp: freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b User: bav5809 Machine: node012 Platform: Linux PlatformVersion: 4.14.185-1.0.27.el7.rrz.x86_64 CompilerName: GCC CompilerVersion: 4.9.2 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 /work/bav5809/software/packages/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 /work/bav5809/software/packages/freesurfer/average GCS DKaparc.atlas.acfb40.noaparc.i12.2016-08-02.gcs ####################################### /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142 mri_convert /work/bav5809/subjects/sub-SEVEREp142/ses-T1/anat/sub-SEVEREp142_ses-T1_T1w.nii.gz /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/orig/001.mgz mri_convert /work/bav5809/subjects/sub-SEVEREp142/ses-T1/anat/sub-SEVEREp142_ses-T1_T1w.nii.gz /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/orig/001.mgz reading from /work/bav5809/subjects/sub-SEVEREp142/ses-T1/anat/sub-SEVEREp142_ses-T1_T1w.nii.gz... TR=2500.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (0.996021, -0.0878862, -0.0147755) j_ras = (0.0885761, 0.957965, 0.272868) k_ras = (-0.00982696, -0.273091, 0.961938) writing to /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/orig/001.mgz... @#@FSTIME 2020:07:08:13:22:18 mri_convert N 2 e 11.07 S 0.04 U 7.78 P 70% M 72736 F 143 R 32433 W 0 c 64 w 720 I 31021 O 33063 L 0.61 2.68 8.86 @#@FSLOADPOST 2020:07:08:13:22:30 mri_convert N 2 0.68 2.63 8.78 #-------------------------------------------- #@# MotionCor Wed Jul 8 13:22:36 CEST 2020 Found 1 runs /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/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 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/orig/001.mgz /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/rawavg.mgz /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142 mri_convert /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/rawavg.mgz /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/orig.mgz --conform mri_convert /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/rawavg.mgz /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/orig.mgz --conform reading from /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/rawavg.mgz... TR=2500.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (0.996021, -0.0878862, -0.0147755) j_ras = (0.0885761, 0.957965, 0.272868) k_ras = (-0.00982696, -0.273091, 0.961938) changing data type from float to uchar (noscale = 0)... MRIchangeType: Building histogram 0 789 1000, flo=0, fhi=0.999, dest_type=0 Reslicing using trilinear interpolation writing to /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/orig.mgz... @#@FSTIME 2020:07:08:13:22:41 mri_convert N 3 e 5.49 S 0.01 U 6.52 P 119% M 89720 F 150 R 41956 W 0 c 24 w 848 I 33065 O 11440 L 0.73 2.57 8.69 @#@FSLOADPOST 2020:07:08:13:22:46 mri_convert N 3 0.77 2.52 8.61 mri_add_xform_to_header -c /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/transforms/talairach.xfm /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/orig.mgz /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/orig.mgz INFO: extension is mgz @#@FSTIME 2020:07:08:13:22:47 mri_add_xform_to_header N 4 e 1.24 S 0.00 U 1.63 P 132% M 22212 F 119 R 4617 W 0 c 7 w 460 I 11441 O 11440 L 0.77 2.52 8.61 @#@FSLOADPOST 2020:07:08:13:22:48 mri_add_xform_to_header N 4 0.77 2.52 8.61 #-------------------------------------------- #@# Talairach Wed Jul 8 13:22:48 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri mri_nu_correct.mni --no-rescale --i orig.mgz --o orig_nu.mgz --ants-n4 --n 1 --proto-iters 1000 --distance 50 /bin/bc /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri /work/bav5809/software/packages/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 7.1.0 Linux node012 4.14.185-1.0.27.el7.rrz.x86_64 #1 SMP Wed Jun 24 17:36:57 CEST 2020 x86_64 x86_64 x86_64 GNU/Linux Wed Jul 8 13:22:49 CEST 2020 Found /dev/shm , will use for temp dir tmpdir is /dev/shm/tmp.mri_nu_correct.mni.12466 cd /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri AntsN4BiasFieldCorrectionFs -i orig.mgz -o /dev/shm/tmp.mri_nu_correct.mni.12466/nu0.mgz Using shrink factor: 4 mri_convert /dev/shm/tmp.mri_nu_correct.mni.12466/nu0.mgz orig_nu.mgz --like orig.mgz --conform mri_convert /dev/shm/tmp.mri_nu_correct.mni.12466/nu0.mgz orig_nu.mgz --like orig.mgz --conform reading from /dev/shm/tmp.mri_nu_correct.mni.12466/nu0.mgz... TR=2500.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, -4.65661e-10, -9.31323e-10) j_ras = (3.72529e-09, 2.98023e-08, -1) k_ras = (-1.39698e-08, 1, -2.98023e-08) INFO: transform src into the like-volume: orig.mgz writing to orig_nu.mgz... Wed Jul 8 13:26:18 CEST 2020 mri_nu_correct.mni done @#@FSTIME 2020:07:08:13:22:49 mri_nu_correct.mni N 12 e 209.20 S 0.30 U 206.48 P 98% M 488592 F 390 R 232919 W 0 c 77 w 1501 I 23152 O 10702 L 0.77 2.52 8.61 @#@FSLOADPOST 2020:07:08:13:26:18 mri_nu_correct.mni N 12 1.13 1.81 7.09 talairach_avi --i orig_nu.mgz --xfm transforms/talairach.auto.xfm --atlas 3T18yoSchwartzReactN32_as_orig talairach_avi log file is transforms/talairach_avi.log... Started at Wed Jul 8 13:26:18 CEST 2020 Ended at Wed Jul 8 13:27:17 CEST 2020 talairach_avi done @#@FSTIME 2020:07:08:13:26:18 talairach_avi N 6 e 58.69 S 1.23 U 22.74 P 40% M 255668 F 868 R 396008 W 0 c 81 w 8112 I 2437290 O 295108 L 1.13 1.81 7.09 @#@FSLOADPOST 2020:07:08:13:27:17 talairach_avi N 6 0.69 1.56 6.67 cp transforms/talairach.auto.xfm transforms/talairach.xfm lta_convert --src orig.mgz --trg /work/bav5809/software/packages/freesurfer/average/mni305.cor.mgz --inxfm transforms/talairach.xfm --outlta transforms/talairach.xfm.lta --subject fsaverage --ltavox2vox 7.1.0 --src: orig.mgz src image (geometry). --trg: /work/bav5809/software/packages/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 1.08176 -0.08571 -0.02475 4.75264; 0.14040 0.83312 0.70945 48.88865; -0.11516 -0.78452 0.93808 22.11694; 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 Jul 8 13:27:20 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri talairach_afd -T 0.005 -xfm transforms/talairach.xfm talairach_afd: Talairach Transform: transforms/talairach.xfm OK (p=0.1264, pval=0.0085 >= threshold=0.0050) @#@FSTIME 2020:07:08:13:27:20 talairach_afd N 4 e 1.08 S 0.00 U 0.00 P 0% M 4344 F 98 R 188 W 0 c 1 w 229 I 65 O 0 L 0.69 1.56 6.67 @#@FSLOADPOST 2020:07:08:13:27:21 talairach_afd N 4 0.69 1.56 6.67 awk -f /work/bav5809/software/packages/freesurfer/bin/extract_talairach_avi_QA.awk /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/transforms/talairach_avi.log tal_QC_AZS /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/transforms/talairach_avi.log TalAviQA: 0.95128 z-score: -6 #-------------------------------------------- #@# Nu Intensity Correction Wed Jul 8 13:27:21 CEST 2020 mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --proto-iters 1000 --distance 50 --n 1 --ants-n4 /bin/bc /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri /work/bav5809/software/packages/freesurfer/bin/mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --proto-iters 1000 --distance 50 --n 1 --ants-n4 nIters 1 mri_nu_correct.mni 7.1.0 Linux node012 4.14.185-1.0.27.el7.rrz.x86_64 #1 SMP Wed Jun 24 17:36:57 CEST 2020 x86_64 x86_64 x86_64 GNU/Linux Wed Jul 8 13:27:21 CEST 2020 Found /dev/shm , will use for temp dir tmpdir is /dev/shm/tmp.mri_nu_correct.mni.16245 cd /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri AntsN4BiasFieldCorrectionFs -i orig.mgz -o /dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz Using shrink factor: 4 mri_binarize --i /dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz --min -1 --o /dev/shm/tmp.mri_nu_correct.mni.16245/ones.mgz 7.1.0 cwd /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri cmdline mri_binarize --i /dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz --min -1 --o /dev/shm/tmp.mri_nu_correct.mni.16245/ones.mgz sysname Linux hostname node012 machine x86_64 user bav5809 input /dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz frame 0 nErode3d 0 nErode2d 0 output /dev/shm/tmp.mri_nu_correct.mni.16245/ones.mgz Binarizing based on threshold min -1 max +infinity binval 1 binvalnot 0 fstart = 0, fend = 0, nframes = 1 Found 16777216 values in range Counting number of voxels in first frame Found 16777215 voxels in final mask Count: 16777215 16777215.000000 16777216 99.999994 mri_binarize done mri_segstats --id 1 --seg /dev/shm/tmp.mri_nu_correct.mni.16245/ones.mgz --i orig.mgz --sum /dev/shm/tmp.mri_nu_correct.mni.16245/sum.junk --avgwf /dev/shm/tmp.mri_nu_correct.mni.16245/input.mean.dat 7.1.0 cwd cmdline mri_segstats --id 1 --seg /dev/shm/tmp.mri_nu_correct.mni.16245/ones.mgz --i orig.mgz --sum /dev/shm/tmp.mri_nu_correct.mni.16245/sum.junk --avgwf /dev/shm/tmp.mri_nu_correct.mni.16245/input.mean.dat sysname Linux hostname node012 machine x86_64 user bav5809 whitesurfname white UseRobust 0 Loading /dev/shm/tmp.mri_nu_correct.mni.16245/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 /dev/shm/tmp.mri_nu_correct.mni.16245/input.mean.dat mri_segstats done mri_segstats --id 1 --seg /dev/shm/tmp.mri_nu_correct.mni.16245/ones.mgz --i /dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz --sum /dev/shm/tmp.mri_nu_correct.mni.16245/sum.junk --avgwf /dev/shm/tmp.mri_nu_correct.mni.16245/output.mean.dat 7.1.0 cwd cmdline mri_segstats --id 1 --seg /dev/shm/tmp.mri_nu_correct.mni.16245/ones.mgz --i /dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz --sum /dev/shm/tmp.mri_nu_correct.mni.16245/sum.junk --avgwf /dev/shm/tmp.mri_nu_correct.mni.16245/output.mean.dat sysname Linux hostname node012 machine x86_64 user bav5809 whitesurfname white UseRobust 0 Loading /dev/shm/tmp.mri_nu_correct.mni.16245/ones.mgz Loading /dev/shm/tmp.mri_nu_correct.mni.16245/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 /dev/shm/tmp.mri_nu_correct.mni.16245/output.mean.dat mri_segstats done mris_calc -o /dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz /dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz mul 1.22583842892326182814 Saving result to '/dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz' (type = MGH ) [ ok ] mri_convert /dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz nu.mgz --like orig.mgz mri_convert /dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz nu.mgz --like orig.mgz reading from /dev/shm/tmp.mri_nu_correct.mni.16245/nu0.mgz... TR=2500.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, -4.65661e-10, -9.31323e-10) j_ras = (3.72529e-09, 2.98023e-08, -1) k_ras = (-1.39698e-08, 1, -2.98023e-08) INFO: transform src into the like-volume: orig.mgz writing to nu.mgz... mri_make_uchar nu.mgz transforms/talairach.xfm nu.mgz type change took 0 minutes and 8 seconds. mapping (13, 103) to ( 3, 110) Wed Jul 8 13:31:24 CEST 2020 mri_nu_correct.mni done @#@FSTIME 2020:07:08:13:27:21 mri_nu_correct.mni N 13 e 242.70 S 1.01 U 244.25 P 101% M 612724 F 1144 R 732617 W 0 c 253 w 4270 I 56621 O 29673 L 0.69 1.56 6.67 @#@FSLOADPOST 2020:07:08:13:31:24 mri_nu_correct.mni N 13 1.06 1.32 5.38 mri_add_xform_to_header -c /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/transforms/talairach.xfm nu.mgz nu.mgz INFO: extension is mgz @#@FSTIME 2020:07:08:13:31:24 mri_add_xform_to_header N 4 e 0.86 S 0.03 U 1.16 P 137% M 22060 F 129 R 4615 W 0 c 4 w 425 I 7454 O 7453 L 1.06 1.32 5.38 @#@FSLOADPOST 2020:07:08:13:31:25 mri_add_xform_to_header N 4 1.06 1.32 5.38 #-------------------------------------------- #@# Intensity Normalization Wed Jul 8 13:31:25 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/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... talairach transform 1.08176 -0.08571 -0.02475 4.75264; 0.14040 0.83312 0.70945 48.88865; -0.11516 -0.78452 0.93808 22.11694; 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 = 5 Starting OpenSpline(): npoints = 5 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 90 gm peak at 71 (71), valley at 59 (59) csf peak at 18, setting threshold to 53 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... --------------------------------- 3d normalization pass 2 of 2 white matter peak found at 110 white matter peak found at 89 gm peak at 67 (67), valley at 54 (54) csf peak at 16, setting threshold to 50 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Done iterating --------------------------------- writing output to T1.mgz 3D bias adjustment took 1 minutes and 55 seconds. @#@FSTIME 2020:07:08:13:31:26 mri_normalize N 7 e 117.29 S 0.69 U 190.06 P 162% M 577604 F 180 R 586595 W 0 c 348 w 897 I 7454 O 7737 L 1.06 1.32 5.38 @#@FSLOADPOST 2020:07:08:13:33:23 mri_normalize N 7 2.75 2.03 5.15 #-------------------------------------------- #@# Skull Stripping Wed Jul 8 13:33:23 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri mri_em_register -skull nu.mgz /work/bav5809/software/packages/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 = 32 == reading 1 input volumes... logging results to talairach_with_skull.log reading '/work/bav5809/software/packages/freesurfer/average/RB_all_withskull_2020_01_02.gca'... GCAread took 0 minutes and 3 seconds. average std = 23.0 using min determinant for regularization = 52.8 0 singular and 9205 ill-conditioned covariance matrices regularized reading 'nu.mgz'... freeing gibbs priors...done. accounting for voxel sizes in initial transform bounding unknown intensity as < 8.9 or > 556.0 total sample mean = 77.3 (1403 zeros) ************************************************ spacing=8, using 3292 sample points, tol=1.00e-05... ************************************************ register_mri: find_optimal_transform find_optimal_transform: nsamples 3292, passno 0, spacing 8 resetting wm mean[0]: 100 --> 108 resetting gm mean[0]: 61 --> 61 input volume #1 is the most T1-like using real data threshold=13.0 skull bounding box = (50, 58, 18) --> (215, 255, 185) finding center of left hemi white matter using (105, 124, 102) as brain centroid of Right_Cerebral_White_Matter... MRImask(): AllowDiffGeom = 1 mean wm in atlas = 108, using box (85,100,81) --> (125, 148,122) to find MRI wm before smoothing, mri peak at 86 robust fit to distribution - 85 +- 33.5 distribution too broad for accurate scaling - disabling after smoothing, mri peak at 108, scaling input intensities by 1.000 scaling channel 0 by 1 initial log_p = -4.798 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.685029 @ (-10.526, -10.526, -10.526) max log p = -4.651587 @ (5.263, -15.789, -5.263) max log p = -4.612226 @ (2.632, 7.895, 7.895) max log p = -4.593255 @ (-1.316, -6.579, -1.316) max log p = -4.579464 @ (3.289, 5.921, 1.974) max log p = -4.579464 @ (0.000, 0.000, 0.000) max log p = -4.579464 @ (0.000, 0.000, 0.000) max log p = -4.579464 @ (0.000, 0.000, 0.000) Found translation: (-0.7, -19.1, -7.2): log p = -4.579 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.157, old_max_log_p =-4.579 (thresh=-4.6) 1.14791 -0.05654 0.04012 -26.63396; 0.01068 0.85223 0.89549 -95.22983; -0.06861 -0.83115 0.79182 148.66541; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 17 seconds. **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.157, old_max_log_p =-4.157 (thresh=-4.2) 1.14791 -0.05654 0.04012 -26.63396; 0.01068 0.85223 0.89549 -95.22983; -0.06861 -0.83115 0.79182 148.66541; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 iteration took 0 minutes and 18 seconds. **************************************** Nine parameter search. iteration 2 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.062, old_max_log_p =-4.157 (thresh=-4.2) 1.10345 0.00265 0.09152 -31.94421; -0.05671 0.91296 0.79499 -84.99863; -0.06715 -0.71456 0.84383 126.61530; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 17 seconds. **************************************** Nine parameter search. iteration 3 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.062, old_max_log_p =-4.062 (thresh=-4.1) 1.10345 0.00265 0.09152 -31.94421; -0.05671 0.91296 0.79499 -84.99863; -0.06715 -0.71456 0.84383 126.61530; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 iteration took 0 minutes and 18 seconds. **************************************** Nine parameter search. iteration 4 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.043, old_max_log_p =-4.062 (thresh=-4.1) 1.10312 0.01408 0.07740 -30.99183; -0.05513 0.92913 0.77313 -84.56013; -0.05029 -0.68938 0.86143 119.84557; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 16 seconds. **************************************** Nine parameter search. iteration 5 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.043, old_max_log_p =-4.043 (thresh=-4.0) 1.10345 0.00267 0.09149 -31.45005; -0.05547 0.92237 0.77923 -84.21602; -0.06780 -0.69628 0.85269 124.23692; 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 1.10345 0.00267 0.09149 -31.45005; -0.05547 0.92237 0.77923 -84.21602; -0.06780 -0.69628 0.85269 124.23692; 0.00000 0.00000 0.00000 1.00000; nsamples 3292 Quasinewton: input matrix 1.10345 0.00267 0.09149 -31.45005; -0.05547 0.92237 0.77923 -84.21602; -0.06780 -0.69628 0.85269 124.23692; 0.00000 0.00000 0.00000 1.00000; IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 4 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 008: -log(p) = -0.0 tol 0.000010 Resulting transform: 1.10345 0.00267 0.09149 -31.45005; -0.05547 0.92237 0.77923 -84.21602; -0.06780 -0.69628 0.85269 124.23692; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -4.043 (old=-4.798) transform before final EM align: 1.10345 0.00267 0.09149 -31.45005; -0.05547 0.92237 0.77923 -84.21602; -0.06780 -0.69628 0.85269 124.23692; 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 1.10345 0.00267 0.09149 -31.45005; -0.05547 0.92237 0.77923 -84.21602; -0.06780 -0.69628 0.85269 124.23692; 0.00000 0.00000 0.00000 1.00000; nsamples 364986 Quasinewton: input matrix 1.10345 0.00267 0.09149 -31.45005; -0.05547 0.92237 0.77923 -84.21602; -0.06780 -0.69628 0.85269 124.23692; 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) = 4.4 tol 0.000000 final transform: 1.10345 0.00267 0.09149 -31.45005; -0.05547 0.92237 0.77923 -84.21602; -0.06780 -0.69628 0.85269 124.23692; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach_with_skull.lta... #VMPC# mri_em_register VmPeak 869960 FSRUNTIME@ mri_em_register 0.0466 hours 32 threads registration took 2 minutes and 48 seconds. @#@FSTIME 2020:07:08:13:33:23 mri_em_register N 4 e 169.05 S 2.18 U 3222.95 P 1907% M 627664 F 214 R 1693684 W 0 c 9642 w 108915 I 157125 O 3 L 2.75 2.03 5.15 @#@FSLOADPOST 2020:07:08:13:36:12 mri_em_register N 4 15.94 9.89 7.71 mri_watershed -T1 -brain_atlas /work/bav5809/software/packages/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=140 y=139 z=103 r=80 first estimation of the main basin volume: 2152535 voxels Looking for seedpoints 2 found in the cerebellum 11 found in the rest of the brain global maximum in x=117, y=136, z=71, Imax=255 CSF=20, 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=6122309339 voxels, voxel volume =1.000 = 6122309339 mmm3 = 6122309.120 cm3 done. PostAnalyze...Basin Prior 38 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=131,y=139, z=101, r=9869 iterations ^^^^^^^^ couldn't find WM with original limits - expanding ^^^^^^ GLOBAL CSF_MIN=1, CSF_intensity=2, CSF_MAX=55 , nb = 45108 RIGHT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=76 , nb = 2736 LEFT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=78 , nb = 2376 RIGHT_BRAIN CSF_MIN=0, CSF_intensity=9, CSF_MAX=51 , nb = 20646 LEFT_BRAIN CSF_MIN=1, CSF_intensity=2, CSF_MAX=49 , nb = 19098 OTHER CSF_MIN=0, CSF_intensity=16, CSF_MAX=28 , nb = 252 CSF_MAX TRANSITION GM_MIN GM GLOBAL before analyzing : 55, 38, 24, 68 after analyzing : 29, 38, 38, 45 RIGHT_CER before analyzing : 76, 48, 38, 68 after analyzing : 25, 48, 48, 53 LEFT_CER before analyzing : 78, 51, 38, 76 after analyzing : 27, 51, 51, 57 RIGHT_BRAIN before analyzing : 51, 38, 27, 67 after analyzing : 30, 38, 38, 45 LEFT_BRAIN before analyzing : 49, 36, 21, 79 after analyzing : 27, 36, 36, 46 OTHER before analyzing : 28, 27, 24, 68 after analyzing : 24, 27, 27, 37 mri_strip_skull: done peeling brain highly tesselated surface with 10242 vertices matching...71 iterations *********************VALIDATION********************* curvature mean = -0.013, std = 0.013 curvature mean = 65.311, std = 7.893 No Rigid alignment: -atlas Mode Off (basic atlas / no registration) before rotation: sse = 12.14, sigma = 31.73 after rotation: sse = 12.14, sigma = 31.73 Localization of inacurate regions: Erosion-Dilation steps the sse mean is 19.51, its var is 36.24 before Erosion-Dilatation 19.08% of inacurate vertices after Erosion-Dilatation 0.00% of inacurate vertices Validation of the shape of the surface done. Scaling of atlas fields onto current surface fields ********FINAL ITERATIVE TEMPLATE DEFORMATION******** Compute Local values csf/gray Fine Segmentation...47 iterations mri_strip_skull: done peeling brain Brain Size = 1454286 voxels, voxel volume = 1.000 mm3 = 1454286 mmm3 = 1454.286 cm3 MRImask(): AllowDiffGeom = 1 ****************************** Saving brainmask.auto.mgz done mri_watershed utimesec 53.897456 mri_watershed stimesec 0.898290 mri_watershed ru_maxrss 845808 mri_watershed ru_ixrss 0 mri_watershed ru_idrss 0 mri_watershed ru_isrss 0 mri_watershed ru_minflt 386231 mri_watershed ru_majflt 260 mri_watershed ru_nswap 0 mri_watershed ru_inblock 174491 mri_watershed ru_oublock 2564 mri_watershed ru_msgsnd 0 mri_watershed ru_msgrcv 0 mri_watershed ru_nsignals 0 mri_watershed ru_nvcsw 21193 mri_watershed ru_nivcsw 399 mri_watershed done @#@FSTIME 2020:07:08:13:36:13 mri_watershed N 6 e 21.05 S 0.92 U 53.90 P 260% M 845808 F 260 R 386248 W 0 c 399 w 21195 I 174491 O 2564 L 15.94 9.89 7.71 @#@FSLOADPOST 2020:07:08:13:36:34 mri_watershed N 6 10.91 9.19 7.53 cp brainmask.auto.mgz brainmask.mgz #------------------------------------- #@# EM Registration Wed Jul 8 13:36:35 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri mri_em_register -uns 3 -mask brainmask.mgz nu.mgz /work/bav5809/software/packages/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 = 32 == reading 1 input volumes... logging results to talairach.log reading '/work/bav5809/software/packages/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=15.0 skull bounding box = (64, 81, 32) --> (198, 216, 151) finding center of left hemi white matter using (109, 126, 92) as brain centroid of Right_Cerebral_White_Matter... MRImask(): AllowDiffGeom = 1 mean wm in atlas = 107, using box (93,109,77) --> (125, 142,106) to find MRI wm before smoothing, mri peak at 86 robust fit to distribution - 85 +- 16.8 distribution too broad for accurate scaling - disabling after smoothing, mri peak at 107, scaling input intensities by 1.000 scaling channel 0 by 1 initial log_p = -4.426 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.351010 @ (-10.526, -10.526, -10.526) max log p = -4.196283 @ (5.263, -5.263, 5.263) max log p = -4.181817 @ (2.632, -7.895, -7.895) max log p = -4.130287 @ (-1.316, 1.316, 6.579) max log p = -4.103233 @ (-0.658, 0.658, 1.974) max log p = -4.103233 @ (0.000, 0.000, 0.000) max log p = -4.103233 @ (0.000, 0.000, 0.000) max log p = -4.103233 @ (0.000, 0.000, 0.000) Found translation: (-4.6, -21.7, -4.6): log p = -4.103 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.814, old_max_log_p =-4.103 (thresh=-4.1) 1.05669 0.04234 0.20486 -40.56453; -0.12941 0.92492 0.66393 -59.66766; -0.13053 -0.62036 0.77686 132.06987; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 17 seconds. **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.809, old_max_log_p =-3.814 (thresh=-3.8) 0.99203 0.00109 0.01311 -6.95765; 0.01067 0.93305 0.67152 -80.24950; 0.00852 -0.60953 0.79695 109.99616; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 17 seconds. **************************************** Nine parameter search. iteration 2 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.809, old_max_log_p =-3.809 (thresh=-3.8) 0.99203 0.00109 0.01311 -6.95765; 0.01067 0.93305 0.67152 -80.24950; 0.00852 -0.60953 0.79695 109.99616; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 iteration took 0 minutes and 16 seconds. **************************************** Nine parameter search. iteration 3 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.708, old_max_log_p =-3.809 (thresh=-3.8) 1.04281 0.00768 0.14176 -29.54098; -0.05992 0.97197 0.64921 -76.05385; -0.09128 -0.58689 0.82835 115.04445; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 17 seconds. **************************************** Nine parameter search. iteration 4 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.708, old_max_log_p =-3.708 (thresh=-3.7) 1.04281 0.00768 0.14176 -29.54098; -0.05992 0.97197 0.64921 -76.05385; -0.09128 -0.58689 0.82835 115.04445; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 iteration took 0 minutes and 16 seconds. **************************************** Nine parameter search. iteration 5 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.697, old_max_log_p =-3.708 (thresh=-3.7) 1.03906 0.01069 0.15350 -30.66604; -0.06911 0.96594 0.65398 -74.45574; -0.09930 -0.59485 0.82184 117.91422; 0.00000 0.00000 0.00000 1.00000; iteration took 0 minutes and 17 seconds. **************************************** Nine parameter search. iteration 6 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.696, old_max_log_p =-3.697 (thresh=-3.7) 1.03906 0.01069 0.15350 -30.66604; -0.06919 0.96707 0.65474 -74.68278; -0.09918 -0.59415 0.82087 117.89957; 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 1.03906 0.01069 0.15350 -30.66604; -0.06919 0.96707 0.65474 -74.68278; -0.09918 -0.59415 0.82087 117.89957; 0.00000 0.00000 0.00000 1.00000; nsamples 2841 Quasinewton: input matrix 1.03906 0.01069 0.15350 -30.66604; -0.06919 0.96707 0.65474 -74.68278; -0.09918 -0.59415 0.82087 117.89957; 0.00000 0.00000 0.00000 1.00000; IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 3 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 009: -log(p) = -0.0 tol 0.000010 Resulting transform: 1.03906 0.01069 0.15350 -30.66604; -0.06919 0.96707 0.65474 -74.68278; -0.09918 -0.59415 0.82087 117.89957; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -3.696 (old=-4.426) transform before final EM align: 1.03906 0.01069 0.15350 -30.66604; -0.06919 0.96707 0.65474 -74.68278; -0.09918 -0.59415 0.82087 117.89957; 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 1.03906 0.01069 0.15350 -30.66604; -0.06919 0.96707 0.65474 -74.68278; -0.09918 -0.59415 0.82087 117.89957; 0.00000 0.00000 0.00000 1.00000; nsamples 315638 Quasinewton: input matrix 1.03906 0.01069 0.15350 -30.66604; -0.06919 0.96707 0.65474 -74.68278; -0.09918 -0.59415 0.82087 117.89957; 0.00000 0.00000 0.00000 1.00000; IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 6 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 011: -log(p) = 4.1 tol 0.000000 final transform: 1.03906 0.01069 0.15350 -30.66604; -0.06919 0.96707 0.65474 -74.68278; -0.09918 -0.59415 0.82087 117.89957; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach.lta... #VMPC# mri_em_register VmPeak 857408 FSRUNTIME@ mri_em_register 0.0455 hours 32 threads registration took 2 minutes and 44 seconds. @#@FSTIME 2020:07:08:13:36:35 mri_em_register N 7 e 163.88 S 3.80 U 3605.68 P 2202% M 614932 F 214 R 1954604 W 0 c 10565 w 133608 I 149964 O 3 L 10.91 9.19 7.53 @#@FSLOADPOST 2020:07:08:13:39:19 mri_em_register N 7 19.17 14.31 9.76 #-------------------------------------- #@# CA Normalize Wed Jul 8 13:39:19 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri mri_ca_normalize -c ctrl_pts.mgz -mask brainmask.mgz nu.mgz /work/bav5809/software/packages/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 '/work/bav5809/software/packages/freesurfer/average/RB_all_2020-01-02.gca'... reading transform from 'transforms/talairach.lta'... reading input volume from nu.mgz... MRImask(): AllowDiffGeom = 1 resetting wm mean[0]: 98 --> 107 resetting gm mean[0]: 61 --> 61 input volume #1 is the most T1-like using real data threshold=15.0 skull bounding box = (64, 81, 32) --> (198, 216, 151) finding center of left hemi white matter using (109, 126, 92) as brain centroid of Right_Cerebral_White_Matter... MRImask(): AllowDiffGeom = 1 mean wm in atlas = 107, using box (93,109,77) --> (125, 142,106) to find MRI wm before smoothing, mri peak at 86 robust fit to distribution - 85 +- 16.8 distribution too broad for accurate scaling - disabling after smoothing, mri peak at 107, scaling input intensities by 1.000 scaling channel 0 by 1 using 246437 sample points... INFO: compute sample coordinates transform 1.03906 0.01069 0.15350 -30.66604; -0.06919 0.96707 0.65474 -74.68278; -0.09918 -0.59415 0.82087 117.89957; 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, 76, 37) --> (199, 202, 186) Left_Cerebral_White_Matter: limiting intensities to 95.0 --> 132.0 0 of 29 (0.0%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (72, 77, 37) --> (141, 196, 184) Right_Cerebral_White_Matter: limiting intensities to 100.0 --> 132.0 0 of 51 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (136, 166, 79) --> (181, 209, 126) Left_Cerebellum_White_Matter: limiting intensities to 104.0 --> 132.0 5 of 8 (62.5%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (91, 167, 71) --> (137, 208, 124) Right_Cerebellum_White_Matter: limiting intensities to 98.0 --> 132.0 0 of 6 (0.0%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (115, 150, 99) --> (150, 205, 133) Brain_Stem: limiting intensities to 96.0 --> 132.0 11 of 12 (91.7%) samples deleted using 106 total control points for intensity normalization... bias field = 0.925 +- 0.078 0 of 90 control points discarded finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (129, 76, 37) --> (199, 202, 186) Left_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 13 of 110 (11.8%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (72, 77, 37) --> (141, 196, 184) Right_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 22 of 161 (13.7%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (136, 166, 79) --> (181, 209, 126) Left_Cerebellum_White_Matter: limiting intensities to 96.0 --> 132.0 52 of 65 (80.0%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (91, 167, 71) --> (137, 208, 124) Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 38 of 72 (52.8%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (115, 150, 99) --> (150, 205, 133) Brain_Stem: limiting intensities to 88.0 --> 132.0 99 of 99 (100.0%) samples deleted using 507 total control points for intensity normalization... bias field = 1.005 +- 0.066 0 of 282 control points discarded finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (129, 76, 37) --> (199, 202, 186) Left_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 8 of 189 (4.2%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (72, 77, 37) --> (141, 196, 184) Right_Cerebral_White_Matter: limiting intensities to 90.0 --> 132.0 40 of 227 (17.6%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (136, 166, 79) --> (181, 209, 126) Left_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 60 of 82 (73.2%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (91, 167, 71) --> (137, 208, 124) Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 70 of 94 (74.5%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (115, 150, 99) --> (150, 205, 133) Brain_Stem: limiting intensities to 88.0 --> 132.0 140 of 141 (99.3%) samples deleted using 733 total control points for intensity normalization... bias field = 0.992 +- 0.073 0 of 407 control points discarded writing normalized volume to norm.mgz... writing control points to ctrl_pts.mgz freeing GCA...done. normalization took 1 minutes and 33 seconds. @#@FSTIME 2020:07:08:13:39:19 mri_ca_normalize N 8 e 93.75 S 0.92 U 102.44 P 110% M 670224 F 194 R 747674 W 0 c 314 w 7935 I 149970 O 3394 L 19.17 14.31 9.76 @#@FSLOADPOST 2020:07:08:13:40:52 mri_ca_normalize N 8 5.10 10.86 8.95 #-------------------------------------- #@# CA Reg Wed Jul 8 13:40:52 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri mri_ca_register -nobigventricles -T transforms/talairach.lta -align-after -mask brainmask.mgz norm.mgz /work/bav5809/software/packages/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 = 32 == reading 1 input volumes... logging results to talairach.log reading input volume 'norm.mgz'... MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 reading GCA '/work/bav5809/software/packages/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 0.998308 #FOTS# QuadFit found better minimum quadopt=(dt=283.033,rms=0.881905) vs oldopt=(dt=92.48,rms=0.921429) #GCMRL# 0 dt 283.032735 rms 0.882 11.660% neg 0 invalid 762 tFOTS 6.8040 tGradient 2.8260 tsec 10.0670 #FOTS# QuadFit found better minimum quadopt=(dt=197.72,rms=0.840581) vs oldopt=(dt=92.48,rms=0.851561) #GCMRL# 1 dt 197.720067 rms 0.841 4.686% neg 0 invalid 762 tFOTS 6.1750 tGradient 2.4960 tsec 9.1630 #FOTS# QuadFit found better minimum quadopt=(dt=239.625,rms=0.825543) vs oldopt=(dt=369.92,rms=0.829735) #GCMRL# 2 dt 239.625225 rms 0.826 1.789% neg 0 invalid 762 tFOTS 5.8880 tGradient 2.5490 tsec 8.8650 #FOTS# QuadFit found better minimum quadopt=(dt=172.936,rms=0.816796) vs oldopt=(dt=92.48,rms=0.819137) #GCMRL# 3 dt 172.936170 rms 0.817 1.060% neg 0 invalid 762 tFOTS 6.0640 tGradient 2.4990 tsec 9.0180 #GCMRL# 4 dt 369.920000 rms 0.810 0.853% neg 0 invalid 762 tFOTS 6.1450 tGradient 2.5950 tsec 9.2130 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.805184) vs oldopt=(dt=92.48,rms=0.805754) #GCMRL# 5 dt 129.472000 rms 0.805 0.574% neg 0 invalid 762 tFOTS 5.9680 tGradient 2.4040 tsec 8.8550 #FOTS# QuadFit found better minimum quadopt=(dt=1183.74,rms=0.796349) vs oldopt=(dt=1479.68,rms=0.796744) #GCMRL# 6 dt 1183.744000 rms 0.796 1.097% neg 0 invalid 762 tFOTS 6.0430 tGradient 2.4290 tsec 8.9570 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.792654) vs oldopt=(dt=92.48,rms=0.792721) #GCMRL# 7 dt 110.976000 rms 0.793 0.464% neg 0 invalid 762 tFOTS 6.0080 tGradient 2.2990 tsec 8.9210 #FOTS# QuadFit found better minimum quadopt=(dt=2071.55,rms=0.786898) vs oldopt=(dt=1479.68,rms=0.787686) #GCMRL# 8 dt 2071.552000 rms 0.787 0.726% neg 0 invalid 762 tFOTS 5.9350 tGradient 2.5920 tsec 9.0640 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.78465) vs oldopt=(dt=369.92,rms=0.784797) #GCMRL# 9 dt 295.936000 rms 0.785 0.286% neg 0 invalid 762 tFOTS 6.0180 tGradient 2.5930 tsec 9.0580 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.78396) vs oldopt=(dt=92.48,rms=0.784087) #GCMRL# 10 dt 129.472000 rms 0.784 0.000% neg 0 invalid 762 tFOTS 5.9990 tGradient 2.7790 tsec 9.2480 #GCMRL# 11 dt 129.472000 rms 0.783 0.059% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4740 tsec 2.9550 #GCMRL# 12 dt 129.472000 rms 0.783 0.097% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6840 tsec 3.1510 #GCMRL# 13 dt 129.472000 rms 0.782 0.115% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8430 tsec 3.3090 #GCMRL# 14 dt 129.472000 rms 0.781 0.131% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5100 tsec 3.0020 #GCMRL# 15 dt 129.472000 rms 0.780 0.143% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5830 tsec 3.0930 #GCMRL# 16 dt 129.472000 rms 0.779 0.152% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6200 tsec 3.1120 #GCMRL# 17 dt 129.472000 rms 0.777 0.156% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5370 tsec 2.9970 #GCMRL# 18 dt 129.472000 rms 0.776 0.156% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5960 tsec 3.0980 #GCMRL# 19 dt 129.472000 rms 0.775 0.153% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5190 tsec 3.0170 #GCMRL# 20 dt 129.472000 rms 0.774 0.152% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5820 tsec 3.0580 #GCMRL# 21 dt 129.472000 rms 0.773 0.149% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5630 tsec 3.0340 #GCMRL# 22 dt 129.472000 rms 0.771 0.144% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5270 tsec 3.0260 #GCMRL# 23 dt 129.472000 rms 0.770 0.146% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6440 tsec 3.1460 #GCMRL# 24 dt 129.472000 rms 0.769 0.142% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7470 tsec 3.2540 #GCMRL# 25 dt 129.472000 rms 0.768 0.133% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7410 tsec 3.2470 #GCMRL# 26 dt 129.472000 rms 0.767 0.128% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5330 tsec 3.0270 #GCMRL# 27 dt 129.472000 rms 0.766 0.110% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0930 tsec 3.7170 #GCMRL# 28 dt 129.472000 rms 0.766 0.101% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6720 tsec 3.2510 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.765633) vs oldopt=(dt=92.48,rms=0.765634) #GCAMreg# pass 0 level1 5 level2 1 tsec 170.347 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 0.766479 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.765384) vs oldopt=(dt=92.48,rms=0.765389) #GCMRL# 30 dt 110.976000 rms 0.765 0.143% neg 0 invalid 762 tFOTS 6.2910 tGradient 2.7430 tsec 9.4970 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.76528) vs oldopt=(dt=92.48,rms=0.765287) #GCMRL# 31 dt 129.472000 rms 0.765 0.000% neg 0 invalid 762 tFOTS 6.0210 tGradient 2.5020 tsec 9.0180 #GCMRL# 32 dt 129.472000 rms 0.765 0.009% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6160 tsec 3.1100 #GCMRL# 33 dt 129.472000 rms 0.765 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5880 tsec 3.0810 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 0.780412 #FOTS# QuadFit found better minimum quadopt=(dt=67.3185,rms=0.776656) vs oldopt=(dt=103.68,rms=0.777472) #GCMRL# 35 dt 67.318519 rms 0.777 0.481% neg 0 invalid 762 tFOTS 6.0370 tGradient 2.6180 tsec 9.0960 #FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.770967) vs oldopt=(dt=103.68,rms=0.772064) #GCMRL# 36 dt 145.152000 rms 0.771 0.732% neg 0 invalid 762 tFOTS 5.7540 tGradient 2.5170 tsec 8.6990 #FOTS# QuadFit found better minimum quadopt=(dt=497.664,rms=0.758623) vs oldopt=(dt=414.72,rms=0.759188) #GCMRL# 37 dt 497.664000 rms 0.759 1.601% neg 0 invalid 762 tFOTS 5.5250 tGradient 2.3210 tsec 8.2810 #FOTS# QuadFit found better minimum quadopt=(dt=62.8966,rms=0.75415) vs oldopt=(dt=25.92,rms=0.755351) #GCMRL# 38 dt 62.896552 rms 0.754 0.590% neg 0 invalid 762 tFOTS 6.0300 tGradient 2.4610 tsec 8.9440 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.753726) vs oldopt=(dt=25.92,rms=0.753772) #GCMRL# 39 dt 36.288000 rms 0.754 0.000% neg 0 invalid 762 tFOTS 5.9710 tGradient 2.2080 tsec 8.6630 #GCMRL# 40 dt 36.288000 rms 0.754 0.020% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2930 tsec 2.7580 #GCMRL# 41 dt 36.288000 rms 0.753 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2100 tsec 2.6720 #GCMRL# 42 dt 36.288000 rms 0.753 0.067% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3380 tsec 2.7960 #GCMRL# 43 dt 36.288000 rms 0.752 0.097% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2910 tsec 2.7540 #GCMRL# 44 dt 36.288000 rms 0.751 0.131% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3640 tsec 2.8450 #GCMRL# 45 dt 36.288000 rms 0.750 0.164% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3160 tsec 2.7890 #GCMRL# 46 dt 36.288000 rms 0.748 0.187% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7620 tsec 3.2550 #GCMRL# 47 dt 36.288000 rms 0.747 0.187% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4450 tsec 2.9290 #GCMRL# 48 dt 36.288000 rms 0.746 0.179% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6330 tsec 3.0840 #GCMRL# 49 dt 36.288000 rms 0.744 0.169% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8330 tsec 3.2870 #GCMRL# 50 dt 36.288000 rms 0.743 0.149% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5140 tsec 2.9680 #GCMRL# 51 dt 36.288000 rms 0.742 0.136% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7120 tsec 3.1640 #GCMRL# 52 dt 36.288000 rms 0.741 0.125% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6530 tsec 3.0960 #GCMRL# 53 dt 36.288000 rms 0.740 0.126% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7740 tsec 3.2270 #GCMRL# 54 dt 36.288000 rms 0.740 0.117% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7730 tsec 3.2280 #GCMRL# 55 dt 36.288000 rms 0.739 0.111% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6570 tsec 3.1580 #FOTS# QuadFit found better minimum quadopt=(dt=9.072,rms=0.738774) vs oldopt=(dt=6.48,rms=0.738774) #GCAMreg# pass 0 level1 4 level2 1 tsec 104.203 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 0.739662 #GCMRL# 57 dt 0.000000 rms 0.739 0.120% neg 0 invalid 762 tFOTS 5.5230 tGradient 2.4780 tsec 8.4420 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 0.779663 #GCMRL# 59 dt 0.000000 rms 0.779 0.108% neg 0 invalid 762 tFOTS 4.8720 tGradient 2.3720 tsec 7.6770 #GCAMreg# pass 0 level1 3 level2 1 tsec 18.875 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 0.779663 #GCMRL# 61 dt 0.000000 rms 0.779 0.108% neg 0 invalid 762 tFOTS 5.1590 tGradient 2.1540 tsec 7.8440 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 0.897436 #FOTS# QuadFit found better minimum quadopt=(dt=2.82912,rms=0.866517) vs oldopt=(dt=2.88,rms=0.866527) #GCMRL# 63 dt 2.829120 rms 0.867 3.445% neg 0 invalid 762 tFOTS 6.1190 tGradient 2.1810 tsec 8.8380 #FOTS# QuadFit found better minimum quadopt=(dt=2.18361,rms=0.861053) vs oldopt=(dt=2.88,rms=0.861633) #GCMRL# 64 dt 2.183607 rms 0.861 0.631% neg 0 invalid 762 tFOTS 5.6850 tGradient 1.9750 tsec 8.1260 #FOTS# QuadFit found better minimum quadopt=(dt=1.95902,rms=0.859275) vs oldopt=(dt=2.88,rms=0.85969) #GCMRL# 65 dt 1.959016 rms 0.859 0.000% neg 0 invalid 762 tFOTS 5.7420 tGradient 2.1320 tsec 8.3790 #GCAMreg# pass 0 level1 2 level2 1 tsec 31.139 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 0.860044 #GCMRL# 67 dt 0.000000 rms 0.859 0.090% neg 0 invalid 762 tFOTS 5.2850 tGradient 2.0260 tsec 7.7700 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 0.957471 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.955159) vs oldopt=(dt=0.32,rms=0.955215) #GCMRL# 69 dt 0.384000 rms 0.955 0.241% neg 0 invalid 762 tFOTS 5.8410 tGradient 2.0120 tsec 8.3300 #FOTS# QuadFit found better minimum quadopt=(dt=1.0725,rms=0.946902) vs oldopt=(dt=1.28,rms=0.947052) #GCMRL# 70 dt 1.072497 rms 0.947 0.865% neg 0 invalid 762 tFOTS 5.5670 tGradient 1.9560 tsec 7.9800 #FOTS# QuadFit found better minimum quadopt=(dt=0.256,rms=0.944306) vs oldopt=(dt=0.32,rms=0.94446) #GCMRL# 71 dt 0.256000 rms 0.944 0.274% neg 0 invalid 762 tFOTS 5.5890 tGradient 2.1190 tsec 8.1490 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.942445) vs oldopt=(dt=0.32,rms=0.94257) #GCMRL# 72 dt 0.448000 rms 0.942 0.000% neg 0 invalid 762 tFOTS 5.4710 tGradient 2.0390 tsec 7.9620 #GCMRL# 73 dt 0.448000 rms 0.939 0.378% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1530 tsec 2.6390 #GCMRL# 74 dt 0.448000 rms 0.934 0.523% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9600 tsec 2.4290 #GCMRL# 75 dt 0.448000 rms 0.931 0.295% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8270 tsec 3.3070 #GCMRL# 76 dt 0.448000 rms 0.927 0.472% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5560 tsec 3.0410 #GCMRL# 77 dt 0.448000 rms 0.924 0.356% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2700 tsec 2.7590 #GCMRL# 78 dt 0.448000 rms 0.923 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4000 tsec 2.8850 #GCMRL# 79 dt 0.448000 rms 0.923 -0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1670 tsec 3.0220 #FOTS# QuadFit found better minimum quadopt=(dt=0.256,rms=0.921872) vs oldopt=(dt=0.32,rms=0.921879) #GCMRL# 80 dt 0.256000 rms 0.922 0.128% neg 0 invalid 762 tFOTS 5.4910 tGradient 2.1360 tsec 8.0970 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.920852) vs oldopt=(dt=0.32,rms=0.921004) #GCAMreg# pass 0 level1 1 level2 1 tsec 71.874 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 0.92163 #GCMRL# 82 dt 0.320000 rms 0.920 0.205% neg 0 invalid 762 tFOTS 5.3400 tGradient 2.1130 tsec 7.8840 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.919508) vs oldopt=(dt=0.08,rms=0.919556) #GCMRL# 83 dt 0.112000 rms 0.920 0.000% neg 0 invalid 762 tFOTS 5.5670 tGradient 2.0930 tsec 8.1320 #GCMRL# 84 dt 0.112000 rms 0.919 0.014% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1350 tsec 2.6190 #GCMRL# 85 dt 0.112000 rms 0.919 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1180 tsec 2.6110 #GCMRL# 86 dt 0.112000 rms 0.919 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1220 tsec 2.6120 #GCMRL# 87 dt 0.112000 rms 0.918 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1340 tsec 2.6270 #GCMRL# 88 dt 0.112000 rms 0.918 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1700 tsec 2.6850 #GCMRL# 89 dt 0.112000 rms 0.918 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1460 tsec 2.6660 #GCMRL# 90 dt 0.112000 rms 0.917 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2170 tsec 2.7730 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 0.868725 #FOTS# QuadFit found better minimum quadopt=(dt=0.256,rms=0.861275) vs oldopt=(dt=0.32,rms=0.861513) #GCMRL# 92 dt 0.256000 rms 0.861 0.858% neg 0 invalid 762 tFOTS 5.7690 tGradient 1.5320 tsec 7.7700 #FOTS# QuadFit found better minimum quadopt=(dt=0.028,rms=0.860964) vs oldopt=(dt=0.02,rms=0.860979) #GCMRL# 93 dt 0.028000 rms 0.861 0.000% neg 0 invalid 762 tFOTS 5.6470 tGradient 1.5470 tsec 7.6990 #GCAMreg# pass 0 level1 0 level2 1 tsec 20.695 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 0.861807 #FOTS# QuadFit found better minimum quadopt=(dt=0.012,rms=0.860911) vs oldopt=(dt=0.02,rms=0.860922) #GCMRL# 95 dt 0.012000 rms 0.861 0.104% neg 0 invalid 762 tFOTS 5.4930 tGradient 1.5080 tsec 7.4470 #FOTS# QuadFit found better minimum quadopt=(dt=0.0015,rms=0.860907) vs oldopt=(dt=0.00125,rms=0.860907) #GCMRL# 96 dt 0.001500 rms 0.861 0.000% neg 0 invalid 762 tFOTS 5.4700 tGradient 1.4940 tsec 7.4480 GCAMregister done in 9.34602 min Starting GCAmapRenormalizeWithAlignment() without scales renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.10253 (16) mri peak = 0.09419 (18) Left_Lateral_Ventricle (4): linear fit = 0.75 x + 0.0 (1497 voxels, overlap=0.721) Left_Lateral_Ventricle (4): linear fit = 0.75 x + 0.0 (1497 voxels, peak = 12), gca=12.1 gca peak = 0.17690 (16) mri peak = 0.06845 (27) Right_Lateral_Ventricle (43): linear fit = 1.47 x + 0.0 (983 voxels, overlap=0.756) Right_Lateral_Ventricle (43): linear fit = 1.47 x + 0.0 (983 voxels, peak = 23), gca=23.4 gca peak = 0.28275 (96) mri peak = 0.10830 (75) Right_Pallidum (52): linear fit = 0.81 x + 0.0 (726 voxels, overlap=0.019) Right_Pallidum (52): linear fit = 0.81 x + 0.0 (726 voxels, peak = 77), gca=77.3 gca peak = 0.18948 (93) mri peak = 0.07453 (80) Left_Pallidum (13): linear fit = 0.89 x + 0.0 (728 voxels, overlap=0.124) Left_Pallidum (13): linear fit = 0.89 x + 0.0 (728 voxels, peak = 83), gca=83.2 gca peak = 0.20755 (55) mri peak = 0.05936 (70) Right_Hippocampus (53): linear fit = 1.26 x + 0.0 (623 voxels, overlap=0.128) Right_Hippocampus (53): linear fit = 1.26 x + 0.0 (623 voxels, peak = 70), gca=69.6 gca peak = 0.31831 (58) mri peak = 0.08125 (60) Left_Hippocampus (17): linear fit = 0.89 x + 0.0 (690 voxels, overlap=0.996) Left_Hippocampus (17): linear fit = 0.89 x + 0.0 (690 voxels, peak = 52), gca=51.9 gca peak = 0.11957 (102) mri peak = 0.04445 (103) Right_Cerebral_White_Matter (41): linear fit = 1.03 x + 0.0 (46422 voxels, overlap=0.880) Right_Cerebral_White_Matter (41): linear fit = 1.03 x + 0.0 (46422 voxels, peak = 106), gca=105.6 gca peak = 0.11429 (102) mri peak = 0.04877 (104) Left_Cerebral_White_Matter (2): linear fit = 1.05 x + 0.0 (43402 voxels, overlap=0.785) Left_Cerebral_White_Matter (2): linear fit = 1.05 x + 0.0 (43402 voxels, peak = 108), gca=107.6 gca peak = 0.14521 (59) mri peak = 0.02735 (93) Left_Cerebral_Cortex (3): linear fit = 1.48 x + 0.0 (10312 voxels, overlap=0.000) Left_Cerebral_Cortex (3): linear fit = 1.48 x + 0.0 (10312 voxels, peak = 87), gca=87.0 gca peak = 0.14336 (58) mri peak = 0.02815 (61) Right_Cerebral_Cortex (42): linear fit = 1.08 x + 0.0 (13602 voxels, overlap=0.434) Right_Cerebral_Cortex (42): linear fit = 1.08 x + 0.0 (13602 voxels, peak = 62), gca=62.4 gca peak = 0.13305 (70) mri peak = 0.08178 (80) Right_Caudate (50): linear fit = 1.16 x + 0.0 (674 voxels, overlap=0.116) Right_Caudate (50): linear fit = 1.16 x + 0.0 (674 voxels, peak = 82), gca=81.5 gca peak = 0.15761 (71) mri peak = 0.04337 (58) Left_Caudate (11): linear fit = 1.00 x + 0.0 (436 voxels, overlap=0.735) Left_Caudate (11): linear fit = 1.00 x + 0.0 (436 voxels, peak = 71), gca=71.0 gca peak = 0.13537 (57) mri peak = 0.02886 (59) Left_Cerebellum_Cortex (8): linear fit = 1.12 x + 0.0 (14859 voxels, overlap=0.291) Left_Cerebellum_Cortex (8): linear fit = 1.12 x + 0.0 (14859 voxels, peak = 64), gca=63.6 gca peak = 0.13487 (56) mri peak = 0.02558 (61) Right_Cerebellum_Cortex (47): linear fit = 1.04 x + 0.0 (17041 voxels, overlap=0.031) Right_Cerebellum_Cortex (47): linear fit = 1.04 x + 0.0 (17041 voxels, peak = 59), gca=58.5 gca peak = 0.19040 (84) mri peak = 0.04606 (83) Left_Cerebellum_White_Matter (7): linear fit = 1.05 x + 0.0 (7613 voxels, overlap=0.980) Left_Cerebellum_White_Matter (7): linear fit = 1.05 x + 0.0 (7613 voxels, peak = 89), gca=88.6 gca peak = 0.18871 (83) mri peak = 0.04623 (88) Right_Cerebellum_White_Matter (46): linear fit = 1.03 x + 0.0 (7001 voxels, overlap=0.974) Right_Cerebellum_White_Matter (46): linear fit = 1.03 x + 0.0 (7001 voxels, peak = 86), gca=85.9 gca peak = 0.24248 (57) mri peak = 0.11864 (55) Left_Amygdala (18): linear fit = 0.94 x + 0.0 (286 voxels, overlap=0.994) Left_Amygdala (18): linear fit = 0.94 x + 0.0 (286 voxels, peak = 54), gca=53.9 gca peak = 0.35833 (56) mri peak = 0.10000 (66) Right_Amygdala (54): linear fit = 1.15 x + 0.0 (433 voxels, overlap=0.302) Right_Amygdala (54): linear fit = 1.15 x + 0.0 (433 voxels, peak = 65), gca=64.7 gca peak = 0.12897 (85) mri peak = 0.08825 (78) Left_Thalamus (10): linear fit = 0.90 x + 0.0 (4155 voxels, overlap=0.749) Left_Thalamus (10): linear fit = 0.90 x + 0.0 (4155 voxels, peak = 77), gca=76.9 gca peak = 0.13127 (83) mri peak = 0.06513 (84) Right_Thalamus (49): linear fit = 1.02 x + 0.0 (3878 voxels, overlap=0.859) Right_Thalamus (49): linear fit = 1.02 x + 0.0 (3878 voxels, peak = 85), gca=85.1 gca peak = 0.12974 (78) mri peak = 0.04979 (78) Left_Putamen (12): linear fit = 0.98 x + 0.0 (1925 voxels, overlap=0.984) Left_Putamen (12): linear fit = 0.98 x + 0.0 (1925 voxels, peak = 76), gca=76.1 gca peak = 0.17796 (79) mri peak = 0.05155 (63) Right_Putamen (51): linear fit = 0.82 x + 0.0 (1640 voxels, overlap=0.022) Right_Putamen (51): linear fit = 0.82 x + 0.0 (1640 voxels, peak = 65), gca=65.2 gca peak = 0.10999 (80) mri peak = 0.21622 (80) Brain_Stem (16): linear fit = 1.05 x + 0.0 (9964 voxels, overlap=0.415) Brain_Stem (16): linear fit = 1.05 x + 0.0 (9964 voxels, peak = 84), gca=84.4 gca peak = 0.13215 (88) mri peak = 0.15610 (82) Right_VentralDC (60): linear fit = 0.95 x + 0.0 (952 voxels, overlap=0.702) Right_VentralDC (60): linear fit = 0.95 x + 0.0 (952 voxels, peak = 84), gca=84.0 gca peak = 0.11941 (89) mri peak = 0.18395 (83) Left_VentralDC (28): linear fit = 0.93 x + 0.0 (995 voxels, overlap=0.692) Left_VentralDC (28): linear fit = 0.93 x + 0.0 (995 voxels, peak = 82), gca=82.3 gca peak = 0.20775 (25) mri peak = 0.11580 (12) Third_Ventricle (14): linear fit = 0.49 x + 0.0 (81 voxels, overlap=0.194) Third_Ventricle (14): linear fit = 0.49 x + 0.0 (81 voxels, peak = 12), gca=12.1 gca peak = 0.13297 (21) mri peak = 0.09583 (17) Fourth_Ventricle (15): linear fit = 0.75 x + 0.0 (302 voxels, overlap=0.680) Fourth_Ventricle (15): linear fit = 0.75 x + 0.0 (302 voxels, peak = 16), gca=15.6 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.19087 (28) gca peak Third_Ventricle = 0.20775 (25) gca peak CSF = 0.16821 (33) gca peak Left_Accumbens_area = 0.32850 (63) gca peak Left_undetermined = 0.98480 (28) gca peak Left_vessel = 0.40887 (53) gca peak Left_choroid_plexus = 0.10898 (46) gca peak Right_Inf_Lat_Vent = 0.17798 (26) gca peak Right_Accumbens_area = 0.30137 (64) gca peak Right_vessel = 0.47828 (52) gca peak Right_choroid_plexus = 0.11612 (45) gca peak Fifth_Ventricle = 0.59466 (35) gca peak WM_hypointensities = 0.10053 (78) gca peak non_WM_hypointensities = 0.07253 (60) gca peak Optic_Chiasm = 0.25330 (73) not using caudate to estimate GM means estimating mean gm scale to be 1.13 x + 0.0 estimating mean wm scale to be 1.04 x + 0.0 estimating mean csf scale to be 0.99 x + 0.0 saving intensity scales to talairach.label_intensities.txt GCAmapRenormalizeWithAlignment() took 3.95523 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 0.95076 #FOTS# QuadFit found better minimum quadopt=(dt=181.85,rms=0.891646) vs oldopt=(dt=92.48,rms=0.906338) #GCMRL# 98 dt 181.850220 rms 0.892 6.218% neg 0 invalid 762 tFOTS 5.5910 tGradient 2.6000 tsec 8.7160 #FOTS# QuadFit found better minimum quadopt=(dt=321.031,rms=0.874653) vs oldopt=(dt=369.92,rms=0.874995) #GCMRL# 99 dt 321.031466 rms 0.875 1.906% neg 0 invalid 762 tFOTS 6.0280 tGradient 2.5960 tsec 9.0740 #FOTS# QuadFit found better minimum quadopt=(dt=207.392,rms=0.867204) vs oldopt=(dt=92.48,rms=0.869792) #GCMRL# 100 dt 207.392405 rms 0.867 0.852% neg 0 invalid 762 tFOTS 6.0120 tGradient 2.3300 tsec 8.7750 #FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.861158) vs oldopt=(dt=369.92,rms=0.861376) #GCMRL# 101 dt 295.936000 rms 0.861 0.697% neg 0 invalid 762 tFOTS 5.7150 tGradient 2.7380 tsec 8.9220 #FOTS# QuadFit found better minimum quadopt=(dt=149.943,rms=0.857415) vs oldopt=(dt=92.48,rms=0.858246) #GCMRL# 102 dt 149.942857 rms 0.857 0.435% neg 0 invalid 762 tFOTS 5.9310 tGradient 2.3130 tsec 8.7110 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.851458) vs oldopt=(dt=369.92,rms=0.852624) #GCMRL# 103 dt 517.888000 rms 0.851 0.695% neg 0 invalid 762 tFOTS 5.8440 tGradient 2.3280 tsec 8.6220 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.848685) vs oldopt=(dt=92.48,rms=0.849262) #GCMRL# 104 dt 129.472000 rms 0.849 0.326% neg 0 invalid 762 tFOTS 5.5440 tGradient 2.5710 tsec 8.5830 #FOTS# QuadFit found better minimum quadopt=(dt=2071.55,rms=0.836569) vs oldopt=(dt=1479.68,rms=0.838142) #GCMRL# 105 dt 2071.552000 rms 0.837 1.428% neg 0 invalid 762 tFOTS 6.3660 tGradient 2.4520 tsec 9.2800 #FOTS# QuadFit found better minimum quadopt=(dt=145.356,rms=0.831337) vs oldopt=(dt=92.48,rms=0.832262) #GCMRL# 106 dt 145.355932 rms 0.831 0.625% neg 0 invalid 762 tFOTS 6.0090 tGradient 3.1640 tsec 9.6220 #GCMRL# 107 dt 369.920000 rms 0.830 0.146% neg 0 invalid 762 tFOTS 6.0090 tGradient 2.3980 tsec 8.8550 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.827809) vs oldopt=(dt=369.92,rms=0.828508) #GCMRL# 108 dt 221.952000 rms 0.828 0.278% neg 0 invalid 762 tFOTS 5.9750 tGradient 2.3860 tsec 8.8090 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.827123) vs oldopt=(dt=92.48,rms=0.827217) #GCMRL# 109 dt 129.472000 rms 0.827 0.083% neg 0 invalid 762 tFOTS 5.8840 tGradient 2.4850 tsec 8.8140 #FOTS# QuadFit found better minimum quadopt=(dt=4734.98,rms=0.814704) vs oldopt=(dt=5918.72,rms=0.816113) #GCMRL# 110 dt 4734.976000 rms 0.815 1.501% neg 0 invalid 762 tFOTS 5.7810 tGradient 2.5220 tsec 8.7670 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.808921) vs oldopt=(dt=92.48,rms=0.80916) #GCMRL# 111 dt 110.976000 rms 0.809 0.710% neg 0 invalid 762 tFOTS 5.9230 tGradient 3.1310 tsec 9.5120 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.806997) vs oldopt=(dt=369.92,rms=0.807256) #GCMRL# 112 dt 517.888000 rms 0.807 0.238% neg 0 invalid 762 tFOTS 5.8680 tGradient 3.1520 tsec 9.4690 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.805639) vs oldopt=(dt=369.92,rms=0.806065) #GCMRL# 113 dt 221.952000 rms 0.806 0.168% neg 0 invalid 762 tFOTS 5.8840 tGradient 3.0180 tsec 9.3620 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.805324) vs oldopt=(dt=92.48,rms=0.805361) #GCMRL# 114 dt 129.472000 rms 0.805 0.000% neg 0 invalid 762 tFOTS 6.0580 tGradient 3.1400 tsec 9.6870 #GCMRL# 115 dt 129.472000 rms 0.805 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5210 tsec 4.0890 #GCMRL# 116 dt 129.472000 rms 0.804 0.075% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1760 tsec 3.6520 #GCMRL# 117 dt 129.472000 rms 0.803 0.117% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0490 tsec 3.5290 #GCMRL# 118 dt 129.472000 rms 0.802 0.138% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2960 tsec 3.7710 #GCMRL# 119 dt 129.472000 rms 0.801 0.118% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8250 tsec 3.3120 #GCMRL# 120 dt 129.472000 rms 0.800 0.156% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7660 tsec 3.2640 #GCMRL# 121 dt 129.472000 rms 0.799 0.174% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7370 tsec 3.2090 #GCMRL# 122 dt 129.472000 rms 0.797 0.179% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7290 tsec 3.1910 #GCMRL# 123 dt 129.472000 rms 0.796 0.174% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7480 tsec 3.2030 #GCMRL# 124 dt 129.472000 rms 0.794 0.185% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6880 tsec 3.1520 #GCMRL# 125 dt 129.472000 rms 0.793 0.204% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1710 tsec 3.6250 #GCMRL# 126 dt 129.472000 rms 0.791 0.188% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0390 tsec 3.4980 #GCMRL# 127 dt 129.472000 rms 0.790 0.205% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.5150 tsec 3.9700 #GCMRL# 128 dt 129.472000 rms 0.788 0.183% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.2590 tsec 3.7350 #GCMRL# 129 dt 129.472000 rms 0.787 0.200% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6250 tsec 4.1750 #GCMRL# 130 dt 129.472000 rms 0.785 0.169% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5360 tsec 3.0040 #GCMRL# 131 dt 129.472000 rms 0.784 0.189% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4760 tsec 2.9530 #GCMRL# 132 dt 129.472000 rms 0.782 0.183% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5540 tsec 3.0530 #GCMRL# 133 dt 129.472000 rms 0.781 0.180% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1130 tsec 3.5590 #GCMRL# 134 dt 129.472000 rms 0.780 0.135% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4510 tsec 2.9040 #GCMRL# 135 dt 129.472000 rms 0.779 0.153% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5290 tsec 2.9940 #GCMRL# 136 dt 129.472000 rms 0.778 0.149% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5900 tsec 3.0730 #GCMRL# 137 dt 129.472000 rms 0.776 0.145% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1590 tsec 3.6100 #GCMRL# 138 dt 129.472000 rms 0.776 0.117% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1680 tsec 3.6080 #GCMRL# 139 dt 129.472000 rms 0.775 0.097% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5980 tsec 3.0800 #GCMRL# 140 dt 129.472000 rms 0.774 0.110% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6100 tsec 3.0910 #GCMRL# 141 dt 129.472000 rms 0.773 0.120% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9120 tsec 3.3500 #GCMRL# 142 dt 129.472000 rms 0.772 0.095% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6370 tsec 3.0830 #GCMRL# 143 dt 129.472000 rms 0.771 0.106% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6210 tsec 3.0610 #GCMRL# 144 dt 129.472000 rms 0.771 0.094% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4100 tsec 2.8530 #GCMRL# 145 dt 129.472000 rms 0.770 0.092% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6160 tsec 3.0690 #GCMRL# 146 dt 129.472000 rms 0.769 0.086% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3500 tsec 2.8050 #GCMRL# 147 dt 129.472000 rms 0.769 0.085% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4300 tsec 2.8900 #GCMRL# 148 dt 129.472000 rms 0.768 0.080% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3860 tsec 2.8580 #GCMRL# 149 dt 129.472000 rms 0.768 0.077% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5800 tsec 3.0420 #GCMRL# 150 dt 129.472000 rms 0.767 0.070% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5520 tsec 3.0050 #GCMRL# 151 dt 129.472000 rms 0.767 0.065% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6920 tsec 3.2080 #GCMRL# 152 dt 129.472000 rms 0.766 0.060% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2980 tsec 2.7980 #GCMRL# 153 dt 129.472000 rms 0.766 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2610 tsec 2.7670 #GCMRL# 154 dt 129.472000 rms 0.765 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3920 tsec 2.8720 #GCMRL# 155 dt 129.472000 rms 0.765 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6210 tsec 3.1060 #GCMRL# 156 dt 129.472000 rms 0.764 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3740 tsec 2.8820 #GCMRL# 157 dt 129.472000 rms 0.764 0.046% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2960 tsec 2.7980 #GCMRL# 158 dt 129.472000 rms 0.764 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2690 tsec 2.7560 #GCMRL# 159 dt 129.472000 rms 0.763 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2730 tsec 2.7700 #GCMRL# 160 dt 129.472000 rms 0.763 0.059% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8350 tsec 3.3410 #GCMRL# 161 dt 129.472000 rms 0.763 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3860 tsec 2.8890 #GCMRL# 162 dt 129.472000 rms 0.762 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4140 tsec 2.9010 #GCMRL# 163 dt 129.472000 rms 0.762 0.063% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1480 tsec 3.6230 #GCMRL# 164 dt 129.472000 rms 0.762 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3970 tsec 2.8910 #GCMRL# 165 dt 129.472000 rms 0.761 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9690 tsec 3.4660 #GCMRL# 166 dt 129.472000 rms 0.761 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3740 tsec 2.8480 #GCMRL# 167 dt 129.472000 rms 0.761 0.061% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9400 tsec 3.4210 #GCMRL# 168 dt 129.472000 rms 0.760 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8280 tsec 3.3230 #GCMRL# 169 dt 129.472000 rms 0.760 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4140 tsec 2.9090 #GCMRL# 170 dt 129.472000 rms 0.760 0.059% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0430 tsec 3.5460 #GCMRL# 171 dt 129.472000 rms 0.759 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9110 tsec 3.3940 #GCMRL# 172 dt 129.472000 rms 0.759 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5400 tsec 3.0450 #GCMRL# 173 dt 129.472000 rms 0.759 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9150 tsec 3.4100 #GCMRL# 174 dt 129.472000 rms 0.758 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9830 tsec 3.4650 #GCMRL# 175 dt 129.472000 rms 0.758 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0030 tsec 3.4880 #GCMRL# 176 dt 129.472000 rms 0.758 0.022% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4690 tsec 2.9680 #GCMRL# 177 dt 129.472000 rms 0.758 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5240 tsec 3.0050 #GCMRL# 178 dt 129.472000 rms 0.757 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9410 tsec 3.4320 #GCMRL# 179 dt 129.472000 rms 0.757 0.019% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4600 tsec 2.9550 #GCMRL# 180 dt 129.472000 rms 0.757 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5200 tsec 3.0220 #GCMRL# 181 dt 129.472000 rms 0.757 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9380 tsec 3.4270 #GCMRL# 182 dt 129.472000 rms 0.756 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9010 tsec 3.4070 #GCMRL# 183 dt 129.472000 rms 0.756 0.016% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5110 tsec 3.0120 #GCMRL# 184 dt 129.472000 rms 0.756 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6860 tsec 3.1510 #GCMRL# 185 dt 129.472000 rms 0.756 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5380 tsec 3.0210 #GCMRL# 186 dt 129.472000 rms 0.755 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.0480 tsec 3.5670 #GCMRL# 187 dt 129.472000 rms 0.755 0.020% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9260 tsec 3.4290 #GCMRL# 188 dt 129.472000 rms 0.755 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8900 tsec 3.3850 #GCMRL# 189 dt 129.472000 rms 0.755 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8970 tsec 3.4280 #GCMRL# 190 dt 129.472000 rms 0.755 0.022% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5210 tsec 3.0310 #GCMRL# 191 dt 129.472000 rms 0.754 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.9030 tsec 3.4060 #GCMRL# 192 dt 129.472000 rms 0.754 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1390 tsec 3.6270 #GCMRL# 193 dt 129.472000 rms 0.754 0.019% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5790 tsec 3.1140 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.753854) vs oldopt=(dt=369.92,rms=0.753856) #GCMRL# 194 dt 517.888000 rms 0.754 0.000% neg 0 invalid 762 tFOTS 6.7550 tGradient 2.9880 tsec 10.2070 #GCMRL# 195 dt 517.888000 rms 0.754 0.003% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8930 tsec 3.4090 #GCAMreg# pass 0 level1 5 level2 1 tsec 428.793 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 0.754877 #FOTS# QuadFit found better minimum quadopt=(dt=221.952,rms=0.75245) vs oldopt=(dt=369.92,rms=0.752885) #GCMRL# 197 dt 221.952000 rms 0.752 0.322% neg 0 invalid 762 tFOTS 6.2500 tGradient 2.9950 tsec 9.7160 #GCMRL# 198 dt 369.920000 rms 0.752 0.096% neg 0 invalid 762 tFOTS 6.0960 tGradient 3.0310 tsec 9.5710 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.751335) vs oldopt=(dt=92.48,rms=0.751419) #GCMRL# 199 dt 129.472000 rms 0.751 0.052% neg 0 invalid 762 tFOTS 6.3650 tGradient 3.0540 tsec 9.8780 #FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.750965) vs oldopt=(dt=369.92,rms=0.751049) #GCMRL# 200 dt 517.888000 rms 0.751 0.000% neg 0 invalid 762 tFOTS 6.4620 tGradient 2.6730 tsec 9.5850 #GCMRL# 201 dt 517.888000 rms 0.751 0.047% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.8140 tsec 3.2860 #GCMRL# 202 dt 517.888000 rms 0.751 -0.166% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.1350 tsec 4.0130 #FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.750256) vs oldopt=(dt=92.48,rms=0.750275) #GCMRL# 203 dt 110.976000 rms 0.750 0.062% neg 0 invalid 762 tFOTS 6.3200 tGradient 3.0630 tsec 9.8740 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.750087) vs oldopt=(dt=92.48,rms=0.750096) #GCMRL# 204 dt 129.472000 rms 0.750 0.000% neg 0 invalid 762 tFOTS 6.5860 tGradient 2.8300 tsec 9.8870 #GCMRL# 205 dt 129.472000 rms 0.750 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7890 tsec 3.2980 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 0.754826 #FOTS# QuadFit found better minimum quadopt=(dt=160.932,rms=0.74877) vs oldopt=(dt=103.68,rms=0.749312) #GCMRL# 207 dt 160.932039 rms 0.749 0.802% neg 0 invalid 762 tFOTS 5.9920 tGradient 2.6420 tsec 9.1140 #FOTS# QuadFit found better minimum quadopt=(dt=165.803,rms=0.739245) vs oldopt=(dt=103.68,rms=0.740424) #GCMRL# 208 dt 165.803109 rms 0.739 1.272% neg 0 invalid 762 tFOTS 6.1060 tGradient 2.2880 tsec 8.8610 #FOTS# QuadFit found better minimum quadopt=(dt=76.6667,rms=0.735792) vs oldopt=(dt=25.92,rms=0.736647) #GCMRL# 209 dt 76.666667 rms 0.736 0.467% neg 0 invalid 762 tFOTS 5.7800 tGradient 2.5490 tsec 8.7880 #FOTS# QuadFit found better minimum quadopt=(dt=225.882,rms=0.728742) vs oldopt=(dt=103.68,rms=0.730578) #GCMRL# 210 dt 225.882353 rms 0.729 0.958% neg 0 invalid 762 tFOTS 6.2670 tGradient 2.5390 tsec 9.2680 #FOTS# QuadFit found better minimum quadopt=(dt=54.7097,rms=0.72495) vs oldopt=(dt=25.92,rms=0.725762) #GCMRL# 211 dt 54.709677 rms 0.725 0.520% neg 0 invalid 762 tFOTS 6.1730 tGradient 2.3060 tsec 8.9230 #FOTS# QuadFit found better minimum quadopt=(dt=497.664,rms=0.717049) vs oldopt=(dt=414.72,rms=0.717325) #GCMRL# 212 dt 497.664000 rms 0.717 1.090% neg 0 invalid 762 tFOTS 5.8730 tGradient 2.7500 tsec 9.2320 #FOTS# QuadFit found better minimum quadopt=(dt=76.4235,rms=0.711161) vs oldopt=(dt=25.92,rms=0.713123) #GCMRL# 213 dt 76.423529 rms 0.711 0.821% neg 0 invalid 762 tFOTS 6.1180 tGradient 2.6100 tsec 9.1800 #FOTS# QuadFit found better minimum quadopt=(dt=89.5072,rms=0.708331) vs oldopt=(dt=103.68,rms=0.708392) #GCMRL# 214 dt 89.507246 rms 0.708 0.398% neg 0 invalid 762 tFOTS 6.1930 tGradient 2.6050 tsec 9.2520 #FOTS# QuadFit found better minimum quadopt=(dt=85.095,rms=0.706939) vs oldopt=(dt=103.68,rms=0.707004) #GCMRL# 215 dt 85.094972 rms 0.707 0.197% neg 0 invalid 762 tFOTS 6.5210 tGradient 2.4000 tsec 9.3980 #FOTS# QuadFit found better minimum quadopt=(dt=123.224,rms=0.704959) vs oldopt=(dt=103.68,rms=0.704979) #GCMRL# 216 dt 123.223881 rms 0.705 0.280% neg 0 invalid 762 tFOTS 5.7250 tGradient 2.4970 tsec 8.6820 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.70387) vs oldopt=(dt=25.92,rms=0.70407) #GCMRL# 217 dt 36.288000 rms 0.704 0.155% neg 0 invalid 762 tFOTS 6.1470 tGradient 2.4500 tsec 9.0670 #GCMRL# 218 dt 414.720000 rms 0.700 0.578% neg 0 invalid 762 tFOTS 5.9180 tGradient 2.5450 tsec 8.9210 #FOTS# QuadFit found better minimum quadopt=(dt=9.072,rms=0.699039) vs oldopt=(dt=6.48,rms=0.699243) #GCMRL# 219 dt 9.072000 rms 0.699 0.109% neg 0 invalid 762 tFOTS 4.8220 tGradient 2.5000 tsec 7.8170 #FOTS# QuadFit found better minimum quadopt=(dt=2.268,rms=0.698879) vs oldopt=(dt=1.62,rms=0.698921) #GCMRL# 220 dt 2.268000 rms 0.699 0.000% neg 0 invalid 762 tFOTS 4.5210 tGradient 2.5240 tsec 7.5280 #GCMRL# 221 dt 0.567000 rms 0.699 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5540 tsec 3.7060 #GCMRL# 222 dt 0.070875 rms 0.699 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5820 tsec 3.9850 #GCMRL# 223 dt 0.017719 rms 0.699 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5680 tsec 3.8280 #FOTS# QuadFit found better minimum quadopt=(dt=0.00885937,rms=0.698837) vs oldopt=(dt=0.00632812,rms=0.698837) #GCAMreg# pass 0 level1 4 level2 1 tsec 144.667 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 0.699777 #FOTS# QuadFit found better minimum quadopt=(dt=0.00885937,rms=0.698836) vs oldopt=(dt=0.00632812,rms=0.698837) #GCMRL# 225 dt 0.008859 rms 0.699 0.134% neg 0 invalid 762 tFOTS 3.1850 tGradient 2.8010 tsec 6.4250 #FOTS# QuadFit found better minimum quadopt=(dt=0.00221484,rms=0.698836) vs oldopt=(dt=0.00158203,rms=0.698836) #GCMRL# 226 dt 0.002215 rms 0.699 0.000% neg 0 invalid 762 tFOTS 4.4570 tGradient 2.5820 tsec 7.5260 #GCMRL# 227 dt 0.000554 rms 0.699 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6000 tsec 3.8320 #GCMRL# 228 dt 0.000277 rms 0.699 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5660 tsec 3.5660 #GCMRL# 229 dt 0.000138 rms 0.699 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5650 tsec 3.5630 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 0.714259 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.690334) vs oldopt=(dt=32,rms=0.695483) #GCMRL# 231 dt 44.800000 rms 0.690 3.350% neg 0 invalid 762 tFOTS 5.4690 tGradient 2.4280 tsec 8.3420 #GCMRL# 232 dt 32.000000 rms 0.677 1.971% neg 0 invalid 762 tFOTS 5.5470 tGradient 2.2820 tsec 8.2770 #GCMRL# 233 dt 32.000000 rms 0.668 1.327% neg 0 invalid 762 tFOTS 5.2630 tGradient 2.3000 tsec 8.0050 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.658724) vs oldopt=(dt=32,rms=0.660093) #GCMRL# 234 dt 38.400000 rms 0.659 1.351% neg 0 invalid 762 tFOTS 5.3470 tGradient 2.2760 tsec 8.0520 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.651928) vs oldopt=(dt=32,rms=0.652882) #GCMRL# 235 dt 38.400000 rms 0.652 1.032% neg 0 invalid 762 tFOTS 5.1740 tGradient 2.1730 tsec 7.7950 #GCMRL# 236 dt 32.000000 rms 0.647 0.789% neg 0 invalid 762 tFOTS 5.5080 tGradient 2.1470 tsec 8.0990 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.64158) vs oldopt=(dt=32,rms=0.642327) #GCMRL# 237 dt 38.400000 rms 0.642 0.804% neg 0 invalid 762 tFOTS 5.1570 tGradient 2.3310 tsec 7.9170 #FOTS# QuadFit found better minimum quadopt=(dt=9.6,rms=0.640425) vs oldopt=(dt=8,rms=0.640613) #GCMRL# 238 dt 9.600000 rms 0.640 0.180% neg 0 invalid 762 tFOTS 4.9850 tGradient 2.3690 tsec 7.7990 #FOTS# QuadFit found better minimum quadopt=(dt=0.7,rms=0.640376) vs oldopt=(dt=0.5,rms=0.640399) #GCMRL# 239 dt 0.700000 rms 0.640 0.000% neg 0 invalid 762 tFOTS 4.2050 tGradient 2.5570 tsec 7.2440 #GCMRL# 240 dt 0.350000 rms 0.640 0.007% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6070 tsec 3.5800 #GCMRL# 241 dt 0.087500 rms 0.640 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.5650 tsec 3.9170 #GCMRL# 242 dt 0.043750 rms 0.640 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.4630 tsec 3.4430 #FOTS# QuadFit found better minimum quadopt=(dt=0.00273437,rms=0.640317) vs oldopt=(dt=0.00195312,rms=0.640317) #GCAMreg# pass 0 level1 3 level2 1 tsec 91.473 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 0.641142 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.63625) vs oldopt=(dt=32,rms=0.636801) #GCMRL# 244 dt 44.800000 rms 0.636 0.763% neg 0 invalid 762 tFOTS 5.7200 tGradient 2.4600 tsec 8.6250 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.631372) vs oldopt=(dt=32,rms=0.632353) #GCMRL# 245 dt 44.800000 rms 0.631 0.767% neg 0 invalid 762 tFOTS 5.6470 tGradient 2.2950 tsec 8.4110 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.629384) vs oldopt=(dt=32,rms=0.629398) #GCMRL# 246 dt 38.400000 rms 0.629 0.315% neg 0 invalid 762 tFOTS 5.9650 tGradient 2.2040 tsec 8.6520 #FOTS# QuadFit found better minimum quadopt=(dt=51.6923,rms=0.626015) vs oldopt=(dt=32,rms=0.626331) #GCMRL# 247 dt 51.692308 rms 0.626 0.535% neg 0 invalid 762 tFOTS 5.8250 tGradient 2.3050 tsec 8.5790 #FOTS# QuadFit found better minimum quadopt=(dt=22.2489,rms=0.624002) vs oldopt=(dt=32,rms=0.624427) #GCMRL# 248 dt 22.248927 rms 0.624 0.321% neg 0 invalid 762 tFOTS 5.8090 tGradient 2.0890 tsec 8.3580 #GCMRL# 249 dt 128.000000 rms 0.619 0.862% neg 0 invalid 762 tFOTS 6.1780 tGradient 2.1340 tsec 8.7680 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.617179) vs oldopt=(dt=8,rms=0.617538) #GCMRL# 250 dt 11.200000 rms 0.617 0.233% neg 0 invalid 762 tFOTS 5.6700 tGradient 2.1790 tsec 8.3210 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.616158) vs oldopt=(dt=32,rms=0.616255) #GCMRL# 251 dt 25.600000 rms 0.616 0.165% neg 0 invalid 762 tFOTS 5.6360 tGradient 2.1620 tsec 8.2510 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.614456) vs oldopt=(dt=32,rms=0.614726) #GCMRL# 252 dt 44.800000 rms 0.614 0.276% neg 0 invalid 762 tFOTS 6.0560 tGradient 2.0740 tsec 8.5780 #FOTS# QuadFit found better minimum quadopt=(dt=23.2,rms=0.61377) vs oldopt=(dt=32,rms=0.613937) #GCMRL# 253 dt 23.200000 rms 0.614 0.112% neg 0 invalid 762 tFOTS 5.6870 tGradient 2.0400 tsec 8.1950 #FOTS# QuadFit found better minimum quadopt=(dt=102.4,rms=0.611859) vs oldopt=(dt=128,rms=0.612037) #GCMRL# 254 dt 102.400000 rms 0.612 0.311% neg 0 invalid 762 tFOTS 6.2320 tGradient 2.0400 tsec 8.7200 #FOTS# QuadFit found better minimum quadopt=(dt=21.9645,rms=0.610202) vs oldopt=(dt=32,rms=0.610661) #GCMRL# 255 dt 21.964497 rms 0.610 0.271% neg 0 invalid 762 tFOTS 5.9450 tGradient 2.1960 tsec 8.6020 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.609161) vs oldopt=(dt=32,rms=0.609211) #GCMRL# 256 dt 44.800000 rms 0.609 0.171% neg 0 invalid 762 tFOTS 6.4060 tGradient 2.1810 tsec 9.0530 #GCMRL# 257 dt 32.000000 rms 0.608 0.185% neg 0 invalid 762 tFOTS 6.1890 tGradient 2.3520 tsec 9.1930 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.60744) vs oldopt=(dt=32,rms=0.60745) #GCMRL# 258 dt 25.600000 rms 0.607 0.097% neg 0 invalid 762 tFOTS 5.8190 tGradient 1.9730 tsec 8.2380 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.60637) vs oldopt=(dt=32,rms=0.606554) #GCMRL# 259 dt 44.800000 rms 0.606 0.176% neg 0 invalid 762 tFOTS 6.1100 tGradient 2.1940 tsec 8.7600 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.605975) vs oldopt=(dt=32,rms=0.606059) #GCMRL# 260 dt 25.600000 rms 0.606 0.065% neg 0 invalid 762 tFOTS 5.6360 tGradient 1.9820 tsec 8.0750 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.604932) vs oldopt=(dt=32,rms=0.60506) #GCMRL# 261 dt 44.800000 rms 0.605 0.172% neg 0 invalid 762 tFOTS 6.2230 tGradient 2.0980 tsec 8.7790 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.604649) vs oldopt=(dt=8,rms=0.604721) #GCMRL# 262 dt 11.200000 rms 0.605 0.000% neg 0 invalid 762 tFOTS 5.8550 tGradient 2.2910 tsec 8.6930 #GCMRL# 263 dt 11.200000 rms 0.604 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0610 tsec 2.5790 #GCMRL# 264 dt 11.200000 rms 0.604 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9860 tsec 2.4960 #GCMRL# 265 dt 11.200000 rms 0.604 0.090% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0240 tsec 2.4800 #GCMRL# 266 dt 11.200000 rms 0.603 0.139% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0280 tsec 2.5010 #GCMRL# 267 dt 11.200000 rms 0.602 0.149% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2290 tsec 2.7990 #GCMRL# 268 dt 11.200000 rms 0.601 0.153% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3660 tsec 2.9670 #GCMRL# 269 dt 11.200000 rms 0.601 0.022% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1190 tsec 2.9360 #GCMRL# 270 dt 11.200000 rms 0.601 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0100 tsec 2.5150 #GCMRL# 271 dt 11.200000 rms 0.600 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0720 tsec 2.8250 #GCMRL# 272 dt 0.043750 rms 0.600 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0090 tsec 4.5670 #GCMRL# 273 dt 0.021875 rms 0.600 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0070 tsec 3.0420 #FOTS# QuadFit found better minimum quadopt=(dt=0.0015,rms=0.600332) vs oldopt=(dt=0.00125,rms=0.600332) #GCMRL# 274 dt 0.001500 rms 0.600 0.000% neg 0 invalid 762 tFOTS 4.9420 tGradient 2.2490 tsec 7.6710 #GCMRL# 275 dt 0.000094 rms 0.600 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2150 tsec 4.2840 #GCMRL# 276 dt 0.000047 rms 0.600 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1560 tsec 3.1880 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 0.634103 #GCMRL# 278 dt 0.000000 rms 0.633 0.119% neg 0 invalid 762 tFOTS 5.4530 tGradient 1.9930 tsec 7.8990 #GCMRL# 279 dt 0.150000 rms 0.633 0.000% neg 0 invalid 762 tFOTS 5.5070 tGradient 1.9560 tsec 8.3150 #GCAMreg# pass 0 level1 2 level2 1 tsec 21.936 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 0.634103 #GCMRL# 281 dt 0.000000 rms 0.633 0.119% neg 0 invalid 762 tFOTS 5.7730 tGradient 1.9730 tsec 8.2190 #GCMRL# 282 dt 0.150000 rms 0.633 0.000% neg 0 invalid 762 tFOTS 5.7550 tGradient 1.9630 tsec 8.5950 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 0.699032 #FOTS# QuadFit found better minimum quadopt=(dt=1.792,rms=0.694395) vs oldopt=(dt=1.28,rms=0.694608) #GCMRL# 284 dt 1.792000 rms 0.694 0.663% neg 0 invalid 762 tFOTS 5.6960 tGradient 1.9750 tsec 8.1360 #FOTS# QuadFit found better minimum quadopt=(dt=0.064,rms=0.694365) vs oldopt=(dt=0.08,rms=0.694365) #GCMRL# 285 dt 0.064000 rms 0.694 0.000% neg 0 invalid 762 tFOTS 6.1000 tGradient 1.9560 tsec 8.5690 #GCMRL# 286 dt 0.064000 rms 0.694 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9490 tsec 2.4250 #GCAMreg# pass 0 level1 1 level2 1 tsec 24.74 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 0.69508 #FOTS# QuadFit found better minimum quadopt=(dt=1.024,rms=0.693843) vs oldopt=(dt=1.28,rms=0.69393) #GCMRL# 288 dt 1.024000 rms 0.694 0.178% neg 0 invalid 762 tFOTS 5.7670 tGradient 2.0020 tsec 8.2080 #GCMRL# 289 dt 0.100000 rms 0.694 0.000% neg 0 invalid 762 tFOTS 5.2860 tGradient 1.9880 tsec 7.7420 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 0.606525 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.58771) vs oldopt=(dt=0.32,rms=0.592675) #GCMRL# 291 dt 0.448000 rms 0.588 3.102% neg 0 invalid 762 tFOTS 5.3540 tGradient 1.4970 tsec 7.3260 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.582423) vs oldopt=(dt=0.32,rms=0.583291) #GCMRL# 292 dt 0.384000 rms 0.582 0.900% neg 0 invalid 762 tFOTS 5.8440 tGradient 1.4550 tsec 7.7780 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.579154) vs oldopt=(dt=0.32,rms=0.57969) #GCMRL# 293 dt 0.384000 rms 0.579 0.561% neg 0 invalid 762 tFOTS 5.5170 tGradient 1.4410 tsec 7.4340 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.577047) vs oldopt=(dt=0.32,rms=0.577393) #GCMRL# 294 dt 0.384000 rms 0.577 0.364% neg 0 invalid 762 tFOTS 5.2910 tGradient 1.4430 tsec 7.1780 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.575469) vs oldopt=(dt=0.32,rms=0.575726) #GCMRL# 295 dt 0.384000 rms 0.575 0.273% neg 0 invalid 762 tFOTS 5.7550 tGradient 1.3560 tsec 7.5740 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.574324) vs oldopt=(dt=0.32,rms=0.57451) #GCMRL# 296 dt 0.384000 rms 0.574 0.199% neg 0 invalid 762 tFOTS 5.5560 tGradient 1.4370 tsec 7.4450 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.573346) vs oldopt=(dt=0.32,rms=0.5735) #GCMRL# 297 dt 0.384000 rms 0.573 0.170% neg 0 invalid 762 tFOTS 5.3230 tGradient 1.3550 tsec 7.1350 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.572612) vs oldopt=(dt=0.32,rms=0.572728) #GCMRL# 298 dt 0.384000 rms 0.573 0.128% neg 0 invalid 762 tFOTS 5.4950 tGradient 1.4120 tsec 7.3570 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.571957) vs oldopt=(dt=0.32,rms=0.572059) #GCMRL# 299 dt 0.384000 rms 0.572 0.114% neg 0 invalid 762 tFOTS 5.3370 tGradient 1.3620 tsec 7.1520 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.57145) vs oldopt=(dt=0.32,rms=0.571526) #GCMRL# 300 dt 0.384000 rms 0.571 0.089% neg 0 invalid 762 tFOTS 5.3410 tGradient 1.4100 tsec 7.1990 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.570964) vs oldopt=(dt=0.32,rms=0.571035) #GCMRL# 301 dt 0.384000 rms 0.571 0.085% neg 0 invalid 762 tFOTS 5.2820 tGradient 1.3800 tsec 7.1250 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.5706) vs oldopt=(dt=0.32,rms=0.570651) #GCMRL# 302 dt 0.384000 rms 0.571 0.064% neg 0 invalid 762 tFOTS 5.2910 tGradient 1.3650 tsec 7.2510 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.570228) vs oldopt=(dt=0.32,rms=0.570279) #GCMRL# 303 dt 0.384000 rms 0.570 0.065% neg 0 invalid 762 tFOTS 5.3490 tGradient 1.3600 tsec 7.1750 #FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.569959) vs oldopt=(dt=0.32,rms=0.569993) #GCMRL# 304 dt 0.384000 rms 0.570 0.000% neg 0 invalid 762 tFOTS 5.5890 tGradient 1.3360 tsec 7.4240 #GCMRL# 305 dt 0.384000 rms 0.570 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3680 tsec 1.8610 #GCMRL# 306 dt 0.384000 rms 0.569 0.082% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.4170 tsec 1.9120 #GCMRL# 307 dt 0.192000 rms 0.569 0.019% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3250 tsec 2.3080 #GCMRL# 308 dt 0.192000 rms 0.569 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3480 tsec 1.8290 #GCMRL# 309 dt 0.192000 rms 0.569 0.021% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.5370 tsec 2.3180 #GCMRL# 310 dt 0.192000 rms 0.569 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.4510 tsec 1.9470 #GCMRL# 311 dt 0.192000 rms 0.568 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3430 tsec 1.8360 #GCMRL# 312 dt 0.192000 rms 0.568 0.013% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3500 tsec 2.0900 #GCMRL# 313 dt 0.192000 rms 0.568 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3580 tsec 1.8560 #GCMRL# 314 dt 0.192000 rms 0.568 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3360 tsec 1.8400 #GCMRL# 315 dt 0.192000 rms 0.568 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3570 tsec 1.8320 #GCMRL# 316 dt 0.192000 rms 0.568 0.008% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3590 tsec 2.1210 #GCMRL# 317 dt 0.192000 rms 0.568 0.011% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3460 tsec 1.8350 #GCMRL# 318 dt 0.192000 rms 0.568 0.021% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3710 tsec 1.8330 #GCMRL# 319 dt 0.192000 rms 0.567 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3570 tsec 1.8210 #GCMRL# 320 dt 0.192000 rms 0.567 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3100 tsec 1.7910 #GCMRL# 321 dt 0.192000 rms 0.567 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3510 tsec 1.8430 #GCMRL# 322 dt 0.192000 rms 0.567 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3690 tsec 1.8540 #GCMRL# 323 dt 0.192000 rms 0.567 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3720 tsec 1.8760 #GCMRL# 324 dt 0.192000 rms 0.567 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3450 tsec 1.8450 #GCMRL# 325 dt 0.192000 rms 0.566 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3550 tsec 1.8490 #GCMRL# 326 dt 0.192000 rms 0.566 0.021% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3600 tsec 1.8770 #GCMRL# 327 dt 0.192000 rms 0.566 0.022% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3660 tsec 1.8790 #GCMRL# 328 dt 0.192000 rms 0.566 0.014% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3630 tsec 1.8930 #GCMRL# 329 dt 0.080000 rms 0.566 0.000% neg 0 invalid 762 tFOTS 4.9350 tGradient 1.3550 tsec 6.8060 #GCMRL# 330 dt 0.080000 rms 0.566 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3630 tsec 1.8370 #GCAMreg# pass 0 level1 0 level2 1 tsec 162.281 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 0.566911 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.564567) vs oldopt=(dt=0.08,rms=0.564982) #GCMRL# 332 dt 0.112000 rms 0.565 0.413% neg 0 invalid 762 tFOTS 4.9880 tGradient 1.3510 tsec 6.7970 #FOTS# QuadFit found better minimum quadopt=(dt=0.260417,rms=0.562002) vs oldopt=(dt=0.08,rms=0.56377) #GCMRL# 333 dt 0.260417 rms 0.562 0.454% neg 0 invalid 762 tFOTS 5.2820 tGradient 1.2860 tsec 7.0690 #GCMRL# 334 dt 0.320000 rms 0.561 0.259% neg 0 invalid 762 tFOTS 5.4780 tGradient 1.3200 tsec 7.2610 #GCMRL# 335 dt 0.320000 rms 0.560 0.086% neg 0 invalid 762 tFOTS 6.5940 tGradient 1.3160 tsec 8.5120 #GCMRL# 336 dt 0.320000 rms 0.560 0.000% neg 0 invalid 762 tFOTS 5.5820 tGradient 1.3420 tsec 7.4080 #GCMRL# 337 dt 0.320000 rms 0.560 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.3620 tsec 1.9660 GCAMregister done in 21.3703 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 0.557399 #FOTS# QuadFit found better minimum quadopt=(dt=0.00289,rms=0.556517) vs oldopt=(dt=0.0036125,rms=0.556517) #GCMRL# 339 dt 0.002890 rms 0.557 0.158% neg 0 invalid 762 tFOTS 8.4680 tGradient 2.3550 tsec 11.2720 #GCAMreg# pass 0 level1 5 level2 1 tsec 23.767 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 0.557399 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.556223) vs oldopt=(dt=92.48,rms=0.556241) iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 341 dt 129.472000 rms 0.556 0.211% neg 0 invalid 762 tFOTS 6.3850 tGradient 2.2490 tsec 10.9440 #FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.555991) vs oldopt=(dt=92.48,rms=0.556006) iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 342 dt 129.472000 rms 0.556 0.000% neg 0 invalid 762 tFOTS 6.3230 tGradient 2.4750 tsec 10.7280 #GCMRL# 343 dt 129.472000 rms 0.556 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2530 tsec 2.7490 #GCMRL# 344 dt 129.472000 rms 0.556 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3150 tsec 2.8270 #GCMRL# 345 dt 129.472000 rms 0.555 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3330 tsec 2.8450 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 346 dt 129.472000 rms 0.555 0.068% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2380 tsec 4.1730 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 0.555744 #FOTS# QuadFit found better minimum quadopt=(dt=31.104,rms=0.554392) vs oldopt=(dt=25.92,rms=0.554397) iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 348 dt 31.104000 rms 0.554 0.243% neg 0 invalid 762 tFOTS 6.8600 tGradient 2.1070 tsec 10.7990 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.554193) vs oldopt=(dt=25.92,rms=0.554205) iter 0, gcam->neg = 2 after 8 iterations, nbhd size=1, neg = 0 #GCMRL# 349 dt 36.288000 rms 0.554 0.000% neg 0 invalid 762 tFOTS 6.2540 tGradient 2.0400 tsec 14.0260 #GCMRL# 350 dt 36.288000 rms 0.554 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0310 tsec 2.5370 #GCMRL# 351 dt 36.288000 rms 0.554 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1030 tsec 2.5860 iter 0, gcam->neg = 2 after 10 iterations, nbhd size=1, neg = 0 #GCAMreg# pass 0 level1 4 level2 1 tsec 42.007 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 0.554854 #FOTS# QuadFit found better minimum quadopt=(dt=82.4505,rms=0.551094) vs oldopt=(dt=103.68,rms=0.551284) #GCMRL# 353 dt 82.450450 rms 0.551 0.678% neg 0 invalid 762 tFOTS 6.7040 tGradient 2.1150 tsec 9.2970 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.550337) vs oldopt=(dt=25.92,rms=0.55046) #GCMRL# 354 dt 36.288000 rms 0.550 0.000% neg 0 invalid 762 tFOTS 6.3250 tGradient 2.2150 tsec 9.0320 #GCMRL# 355 dt 36.288000 rms 0.550 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1020 tsec 2.6150 #GCMRL# 356 dt 36.288000 rms 0.550 0.097% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0480 tsec 2.5070 #GCMRL# 357 dt 36.288000 rms 0.549 0.121% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.2000 tsec 2.6990 #GCMRL# 358 dt 36.288000 rms 0.548 0.122% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1690 tsec 2.7420 #GCMRL# 359 dt 36.288000 rms 0.548 0.115% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3730 tsec 2.9750 #GCMRL# 360 dt 36.288000 rms 0.547 0.113% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1840 tsec 2.7180 #FOTS# QuadFit found better minimum quadopt=(dt=124.416,rms=0.546655) vs oldopt=(dt=103.68,rms=0.546659) iter 0, gcam->neg = 1 after 8 iterations, nbhd size=1, neg = 0 setting smoothness cost coefficient to 0.118 #GCAMreg# pass 0 level1 3 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_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 0.547892 #FOTS# QuadFit found better minimum quadopt=(dt=38.2627,rms=0.543041) vs oldopt=(dt=32,rms=0.543083) iter 0, gcam->neg = 27 after 19 iterations, nbhd size=1, neg = 0 #GCMRL# 362 dt 38.262735 rms 0.543 0.868% neg 0 invalid 762 tFOTS 6.6010 tGradient 2.1010 tsec 19.4250 #FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.5397) vs oldopt=(dt=32,rms=0.539734) iter 0, gcam->neg = 17 after 16 iterations, nbhd size=1, neg = 0 #GCMRL# 363 dt 38.400000 rms 0.540 0.626% neg 0 invalid 762 tFOTS 6.6050 tGradient 1.8920 tsec 18.0590 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.537673) vs oldopt=(dt=32,rms=0.537782) iter 0, gcam->neg = 7 after 15 iterations, nbhd size=1, neg = 0 #GCMRL# 364 dt 25.600000 rms 0.538 0.370% neg 0 invalid 762 tFOTS 6.7280 tGradient 2.1820 tsec 17.7780 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.535642) vs oldopt=(dt=32,rms=0.535785) iter 0, gcam->neg = 10 after 6 iterations, nbhd size=1, neg = 0 #GCMRL# 365 dt 44.800000 rms 0.536 0.386% neg 0 invalid 762 tFOTS 6.6310 tGradient 2.1060 tsec 13.4190 #FOTS# QuadFit found better minimum quadopt=(dt=20.2667,rms=0.534706) vs oldopt=(dt=32,rms=0.535077) iter 0, gcam->neg = 18 after 13 iterations, nbhd size=0, neg = 0 #GCMRL# 366 dt 20.266667 rms 0.535 0.000% neg 0 invalid 762 tFOTS 6.8560 tGradient 2.1820 tsec 16.9650 iter 0, gcam->neg = 34 after 15 iterations, nbhd size=0, neg = 0 #GCMRL# 367 dt 20.266667 rms 0.534 0.197% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9890 tsec 10.9040 iter 0, gcam->neg = 69 after 15 iterations, nbhd size=0, neg = 0 #GCMRL# 368 dt 20.266667 rms 0.533 0.174% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9890 tsec 10.9560 iter 0, gcam->neg = 166 after 32 iterations, nbhd size=1, neg = 0 #GCMRL# 369 dt 20.266667 rms 0.532 0.148% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.1240 tsec 19.1030 iter 0, gcam->neg = 256 after 35 iterations, nbhd size=2, neg = 0 #GCMRL# 370 dt 20.266667 rms 0.531 0.103% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8490 tsec 20.1780 iter 0, gcam->neg = 189 after 33 iterations, nbhd size=1, neg = 0 #GCMRL# 371 dt 20.266667 rms 0.531 0.060% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9300 tsec 19.4960 iter 0, gcam->neg = 12 after 21 iterations, nbhd size=1, neg = 0 #GCMRL# 372 dt 32.000000 rms 0.531 0.000% neg 0 invalid 762 tFOTS 6.9330 tGradient 2.0360 tsec 20.6040 iter 0, gcam->neg = 24 after 18 iterations, nbhd size=1, neg = 0 #GCMRL# 373 dt 32.000000 rms 0.530 0.097% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.3060 tsec 12.9080 iter 0, gcam->neg = 85 after 20 iterations, nbhd size=1, neg = 0 #GCMRL# 374 dt 32.000000 rms 0.530 0.082% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0930 tsec 13.2780 iter 0, gcam->neg = 205 after 24 iterations, nbhd size=1, neg = 0 #GCAMreg# pass 0 level1 3 level2 1 tsec 230.96 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 0.530725 #FOTS# QuadFit found better minimum quadopt=(dt=36.2895,rms=0.525918) vs oldopt=(dt=32,rms=0.525937) iter 0, gcam->neg = 55 after 13 iterations, nbhd size=0, neg = 0 #GCMRL# 376 dt 36.289544 rms 0.526 0.920% neg 0 invalid 762 tFOTS 7.2120 tGradient 2.1320 tsec 17.2530 #FOTS# QuadFit found better minimum quadopt=(dt=21.7846,rms=0.524978) vs oldopt=(dt=32,rms=0.525247) iter 0, gcam->neg = 14 after 6 iterations, nbhd size=0, neg = 0 #GCMRL# 377 dt 21.784615 rms 0.525 0.000% neg 0 invalid 762 tFOTS 6.8420 tGradient 2.0280 tsec 13.5370 iter 0, gcam->neg = 10 after 14 iterations, nbhd size=1, neg = 0 #GCMRL# 378 dt 21.784615 rms 0.524 0.213% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9410 tsec 10.3460 iter 0, gcam->neg = 18 after 9 iterations, nbhd size=0, neg = 0 #GCMRL# 379 dt 21.784615 rms 0.523 0.228% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9230 tsec 7.9770 iter 0, gcam->neg = 44 after 20 iterations, nbhd size=1, neg = 0 #GCMRL# 380 dt 21.784615 rms 0.521 0.272% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.8720 tsec 13.3450 iter 0, gcam->neg = 105 after 27 iterations, nbhd size=1, neg = 0 #GCMRL# 381 dt 21.784615 rms 0.520 0.250% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9570 tsec 16.3000 iter 0, gcam->neg = 183 after 23 iterations, nbhd size=1, neg = 0 #GCMRL# 382 dt 21.784615 rms 0.519 0.165% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0960 tsec 15.0040 iter 0, gcam->neg = 341 after 27 iterations, nbhd size=1, neg = 0 #GCMRL# 383 dt 21.784615 rms 0.518 0.112% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0030 tsec 16.4860 iter 0, gcam->neg = 433 after 31 iterations, nbhd size=1, neg = 0 #GCMRL# 384 dt 21.784615 rms 0.518 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0280 tsec 18.3150 #FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.51772) vs oldopt=(dt=32,rms=0.517785) iter 0, gcam->neg = 14 after 13 iterations, nbhd size=1, neg = 0 #GCMRL# 385 dt 25.600000 rms 0.518 0.098% neg 0 invalid 762 tFOTS 6.9240 tGradient 2.0040 tsec 16.7610 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.517422) vs oldopt=(dt=8,rms=0.517482) iter 0, gcam->neg = 3 after 1 iterations, nbhd size=0, neg = 0 #GCMRL# 386 dt 11.200000 rms 0.517 0.000% neg 0 invalid 762 tFOTS 7.0760 tGradient 2.2690 tsec 11.6660 iter 0, gcam->neg = 3 after 16 iterations, nbhd size=1, neg = 0 #GCMRL# 387 dt 11.200000 rms 0.517 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9720 tsec 11.2740 iter 0, gcam->neg = 3 after 10 iterations, nbhd size=1, neg = 0 #GCMRL# 388 dt 11.200000 rms 0.517 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0250 tsec 8.6300 iter 0, gcam->neg = 6 after 1 iterations, nbhd size=0, neg = 0 #GCMRL# 389 dt 11.200000 rms 0.516 0.090% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0120 tsec 4.3420 iter 0, gcam->neg = 19 after 7 iterations, nbhd size=0, neg = 0 #GCMRL# 390 dt 11.200000 rms 0.516 0.086% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0030 tsec 7.1030 iter 0, gcam->neg = 31 after 13 iterations, nbhd size=1, neg = 0 setting smoothness cost coefficient to 0.400 #GCAMreg# pass 0 level1 2 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.40 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.522562 #FOTS# QuadFit found better minimum quadopt=(dt=2.28571,rms=0.521498) vs oldopt=(dt=2.88,rms=0.52151) iter 0, gcam->neg = 2 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 392 dt 2.285714 rms 0.521 0.204% neg 0 invalid 762 tFOTS 7.0740 tGradient 2.0280 tsec 10.9220 #FOTS# QuadFit found better minimum quadopt=(dt=1.008,rms=0.521468) vs oldopt=(dt=0.72,rms=0.521473) iter 0, gcam->neg = 3 after 8 iterations, nbhd size=1, neg = 0 #GCMRL# 393 dt 1.008000 rms 0.521 0.000% neg 0 invalid 762 tFOTS 7.3110 tGradient 1.9930 tsec 14.9960 #GCMRL# 394 dt 1.008000 rms 0.521 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0000 tsec 2.5200 #GCAMreg# pass 0 level1 2 level2 1 tsec 34.166 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 0.522432 #FOTS# QuadFit found better minimum quadopt=(dt=2.15385,rms=0.521411) vs oldopt=(dt=2.88,rms=0.521418) #GCMRL# 396 dt 2.153846 rms 0.521 0.195% neg 0 invalid 762 tFOTS 6.9450 tGradient 1.9840 tsec 9.3900 #FOTS# QuadFit found better minimum quadopt=(dt=2.304,rms=0.521362) vs oldopt=(dt=2.88,rms=0.521363) #GCMRL# 397 dt 2.304000 rms 0.521 0.000% neg 0 invalid 762 tFOTS 6.8670 tGradient 1.9540 tsec 9.3090 iter 0, gcam->neg = 2 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 398 dt 2.304000 rms 0.521 0.013% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.9790 tsec 3.7980 iter 0, gcam->neg = 5 after 9 iterations, nbhd size=1, neg = 0 setting smoothness cost coefficient to 1.000 #GCAMreg# pass 0 level1 1 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_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 0.531971 #GCMRL# 400 dt 0.000050 rms 0.531 0.176% neg 0 invalid 762 tFOTS 8.9120 tGradient 1.9630 tsec 11.3410 #GCAMreg# pass 0 level1 1 level2 1 tsec 23.414 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 0.531971 #FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.530924) vs oldopt=(dt=0.32,rms=0.530937) #GCMRL# 402 dt 0.448000 rms 0.531 0.197% neg 0 invalid 762 tFOTS 6.9530 tGradient 1.9480 tsec 9.4110 #GCMRL# 403 dt 0.320000 rms 0.531 0.000% neg 0 invalid 762 tFOTS 6.9020 tGradient 1.9420 tsec 9.3120 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 0.517439 #FOTS# QuadFit found better minimum quadopt=(dt=1.72058,rms=0.486326) vs oldopt=(dt=1.28,rms=0.488473) iter 0, gcam->neg = 2174 after 20 iterations, nbhd size=1, neg = 0 #GCMRL# 405 dt 1.720583 rms 0.481 7.050% neg 0 invalid 762 tFOTS 7.1010 tGradient 1.3320 tsec 19.8970 #GCMRL# 406 dt 0.000013 rms 0.481 0.000% neg 0 invalid 762 tFOTS 8.8330 tGradient 1.3350 tsec 10.6710 #GCAMreg# pass 0 level1 0 level2 1 tsec 35.632 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 0.482063 #GCMRL# 408 dt 0.000000 rms 0.481 0.240% neg 0 invalid 762 tFOTS 6.4440 tGradient 1.3380 tsec 8.2620 label assignment complete, 0 changed (0.00%) GCAMregister done in 12.6003 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 0.46739 #GCAMreg# pass 0 level1 5 level2 1 tsec 10.867 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 0.46739 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 0.467562 #FOTS# QuadFit found better minimum quadopt=(dt=2.268,rms=0.467557) vs oldopt=(dt=1.62,rms=0.467558) #GCMRL# 412 dt 2.268000 rms 0.468 0.001% neg 0 invalid 762 tFOTS 6.3230 tGradient 1.1680 tsec 7.9430 iter 0, gcam->neg = 1 after 6 iterations, nbhd size=1, neg = 0 #GCMRL# 413 dt 1.620000 rms 0.468 0.000% neg 0 invalid 762 tFOTS 6.3230 tGradient 1.2180 tsec 12.2660 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCAMreg# pass 0 level1 4 level2 1 tsec 26.354 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 0.467557 #FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.467456) vs oldopt=(dt=25.92,rms=0.467468) iter 0, gcam->neg = 4 after 1 iterations, nbhd size=0, neg = 0 #GCMRL# 415 dt 36.288000 rms 0.467 0.022% neg 0 invalid 762 tFOTS 6.7060 tGradient 1.1600 tsec 10.1760 iter 0, gcam->neg = 4 after 4 iterations, nbhd size=0, neg = 0 #GCMRL# 416 dt 25.920000 rms 0.467 0.000% neg 0 invalid 762 tFOTS 6.4130 tGradient 1.2150 tsec 11.4130 iter 0, gcam->neg = 8 after 4 iterations, nbhd size=0, neg = 0 setting smoothness cost coefficient to 0.118 #GCAMreg# pass 0 level1 3 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.12 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.46809 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.467306) vs oldopt=(dt=8,rms=0.467458) iter 0, gcam->neg = 48 after 15 iterations, nbhd size=1, neg = 0 #GCMRL# 418 dt 11.200000 rms 0.467 0.148% neg 0 invalid 762 tFOTS 6.4710 tGradient 1.2740 tsec 16.7340 #FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.467108) vs oldopt=(dt=8,rms=0.467156) iter 0, gcam->neg = 39 after 14 iterations, nbhd size=1, neg = 0 #GCAMreg# pass 0 level1 3 level2 1 tsec 36.236 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 0.467397 #FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.465417) vs oldopt=(dt=32,rms=0.465709) iter 0, gcam->neg = 127 after 28 iterations, nbhd size=1, neg = 0 #GCMRL# 420 dt 44.800000 rms 0.466 0.365% neg 0 invalid 762 tFOTS 6.6780 tGradient 1.1610 tsec 22.7540 iter 0, gcam->neg = 103 after 25 iterations, nbhd size=1, neg = 0 #GCMRL# 421 dt 32.000000 rms 0.465 0.000% neg 0 invalid 762 tFOTS 6.4630 tGradient 1.0480 tsec 21.0000 iter 0, gcam->neg = 88 after 30 iterations, nbhd size=1, neg = 0 #GCMRL# 422 dt 32.000000 rms 0.465 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.0580 tsec 16.8140 iter 0, gcam->neg = 239 after 32 iterations, nbhd size=1, neg = 0 setting smoothness cost coefficient to 0.400 #GCAMreg# pass 0 level1 2 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.40 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.467715 #FOTS# QuadFit found better minimum quadopt=(dt=13.7993,rms=0.464838) vs oldopt=(dt=11.52,rms=0.464922) iter 0, gcam->neg = 363 after 30 iterations, nbhd size=1, neg = 0 #GCMRL# 424 dt 13.799257 rms 0.465 0.562% neg 0 invalid 762 tFOTS 6.4700 tGradient 1.0610 tsec 23.8170 #FOTS# QuadFit found better minimum quadopt=(dt=13.3846,rms=0.46409) vs oldopt=(dt=11.52,rms=0.464112) iter 0, gcam->neg = 281 after 32 iterations, nbhd size=2, neg = 0 #GCMRL# 425 dt 13.384615 rms 0.465 0.000% neg 0 invalid 762 tFOTS 7.4910 tGradient 1.0690 tsec 25.4210 iter 0, gcam->neg = 247 after 28 iterations, nbhd size=1, neg = 0 #GCMRL# 426 dt 13.384615 rms 0.464 0.090% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.0710 tsec 16.0470 iter 0, gcam->neg = 617 after 25 iterations, nbhd size=1, neg = 0 #GCMRL# 427 dt 13.384615 rms 0.464 0.112% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.2980 tsec 15.5000 iter 0, gcam->neg = 1071 after 29 iterations, nbhd size=1, neg = 0 #GCMRL# 428 dt 13.384615 rms 0.464 0.008% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.0550 tsec 16.4990 iter 0, gcam->neg = 1323 after 32 iterations, nbhd size=1, neg = 0 #GCAMreg# pass 0 level1 2 level2 1 tsec 119.272 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 0.463659 #FOTS# QuadFit found better minimum quadopt=(dt=11.8739,rms=0.460975) vs oldopt=(dt=11.52,rms=0.460981) iter 0, gcam->neg = 303 after 33 iterations, nbhd size=1, neg = 0 #GCMRL# 430 dt 11.873926 rms 0.461 0.564% neg 0 invalid 762 tFOTS 6.5200 tGradient 1.0230 tsec 24.8060 #FOTS# QuadFit found better minimum quadopt=(dt=10.6201,rms=0.459826) vs oldopt=(dt=11.52,rms=0.459835) iter 0, gcam->neg = 262 after 29 iterations, nbhd size=1, neg = 0 #GCMRL# 431 dt 10.620087 rms 0.460 0.279% neg 0 invalid 762 tFOTS 6.6560 tGradient 1.0250 tsec 23.0530 #FOTS# QuadFit found better minimum quadopt=(dt=9.37931,rms=0.459304) vs oldopt=(dt=11.52,rms=0.459334) iter 0, gcam->neg = 163 after 27 iterations, nbhd size=1, neg = 0 #GCMRL# 432 dt 9.379310 rms 0.459 0.000% neg 0 invalid 762 tFOTS 6.4990 tGradient 1.0440 tsec 21.9990 iter 0, gcam->neg = 168 after 24 iterations, nbhd size=1, neg = 0 #GCMRL# 433 dt 9.379310 rms 0.459 0.115% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.0690 tsec 14.2770 iter 0, gcam->neg = 303 after 31 iterations, nbhd size=1, neg = 0 #GCMRL# 434 dt 9.379310 rms 0.459 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 1.0390 tsec 17.4230 iter 0, gcam->neg = 411 after 34 iterations, nbhd size=1, neg = 0 setting smoothness cost coefficient to 1.000 #GCAMreg# pass 0 level1 1 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=1.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.46598 #GCMRL# 436 dt 0.000050 rms 0.466 0.000% neg 0 invalid 762 tFOTS 9.6800 tGradient 1.0660 tsec 11.3080 #GCAMreg# pass 0 level1 1 level2 1 tsec 23.19 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 0.46598 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 0.453645 #FOTS# QuadFit found better minimum quadopt=(dt=0.77193,rms=0.444908) vs oldopt=(dt=0.32,rms=0.44797) iter 0, gcam->neg = 1580 after 59 iterations, nbhd size=3, neg = 0 #GCMRL# 439 dt 0.771930 rms 0.451 0.583% neg 0 invalid 762 tFOTS 6.5790 tGradient 0.5180 tsec 36.2940 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.450649) vs oldopt=(dt=0.08,rms=0.45071) #GCMRL# 440 dt 0.112000 rms 0.451 0.000% neg 0 invalid 762 tFOTS 6.4450 tGradient 0.4820 tsec 7.4180 #GCMRL# 441 dt 0.112000 rms 0.450 0.067% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.4880 tsec 0.9730 #GCMRL# 442 dt 0.112000 rms 0.450 0.077% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.5140 tsec 0.9920 #GCMRL# 443 dt 0.112000 rms 0.450 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.4930 tsec 0.9790 iter 0, gcam->neg = 17 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 444 dt 0.112000 rms 0.450 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.4890 tsec 3.2550 #FOTS# QuadFit found better minimum quadopt=(dt=0.558824,rms=0.446946) vs oldopt=(dt=0.32,rms=0.447594) iter 0, gcam->neg = 114 after 14 iterations, nbhd size=1, neg = 0 #GCMRL# 445 dt 0.558824 rms 0.447 0.575% neg 0 invalid 762 tFOTS 6.4190 tGradient 0.4670 tsec 15.2770 iter 0, gcam->neg = 10 after 12 iterations, nbhd size=1, neg = 0 #GCMRL# 446 dt 0.320000 rms 0.446 0.000% neg 0 invalid 762 tFOTS 6.6640 tGradient 0.5210 tsec 14.8260 iter 0, gcam->neg = 8 after 0 iterations, nbhd size=0, neg = 0 #GCMRL# 447 dt 0.320000 rms 0.446 0.149% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.4740 tsec 2.3390 iter 0, gcam->neg = 33 after 2 iterations, nbhd size=0, neg = 0 #GCMRL# 448 dt 0.320000 rms 0.445 0.190% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.4990 tsec 3.3250 iter 0, gcam->neg = 58 after 4 iterations, nbhd size=0, neg = 0 #GCMRL# 449 dt 0.320000 rms 0.444 0.189% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.4950 tsec 4.2520 iter 0, gcam->neg = 117 after 16 iterations, nbhd size=1, neg = 0 #GCMRL# 450 dt 0.320000 rms 0.444 0.012% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.4980 tsec 9.9840 iter 0, gcam->neg = 149 after 14 iterations, nbhd size=1, neg = 0 #GCMRL# 451 dt 0.320000 rms 0.444 0.063% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.4800 tsec 9.0830 iter 0, gcam->neg = 221 after 17 iterations, nbhd size=1, neg = 0 #GCMRL# 452 dt 0.320000 rms 0.444 -0.099% neg 0 invalid 762 tFOTS 0.0000 tGradient 0.4810 tsec 10.6950 #FOTS# QuadFit found better minimum quadopt=(dt=0.192,rms=0.443345) vs oldopt=(dt=0.32,rms=0.443423) #GCMRL# 453 dt 0.192000 rms 0.443 0.053% neg 0 invalid 762 tFOTS 6.5420 tGradient 0.4950 tsec 7.4710 #FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.44326) vs oldopt=(dt=0.08,rms=0.443278) #GCAMreg# pass 0 level1 0 level2 1 tsec 137.601 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 0.44326 #FOTS# QuadFit found better minimum quadopt=(dt=1.024,rms=0.437337) vs oldopt=(dt=1.28,rms=0.43781) iter 0, gcam->neg = 886 after 19 iterations, nbhd size=1, neg = 0 #GCMRL# 455 dt 1.024000 rms 0.438 1.161% neg 0 invalid 762 tFOTS 6.6090 tGradient 0.5030 tsec 17.9480 #FOTS# QuadFit found better minimum quadopt=(dt=2.73438e-05,rms=0.438112) vs oldopt=(dt=1.95313e-05,rms=0.438112) #GCMRL# 456 dt 0.000027 rms 0.438 0.000% neg 0 invalid 762 tFOTS 8.7480 tGradient 0.5360 tsec 9.7810 GCAMregister done in 10.6398 min writing output transformation to transforms/talairach.m3z... GCAMwrite Calls to gcamLogLikelihoodEnergy 4009 tmin = 4.957 Calls to gcamLabelEnergy 3414 tmin = 1.3696 Calls to gcamJacobianEnergy 4009 tmin = 2.0531 Calls to gcamSmoothnessEnergy 4009 tmin = 3.85352 Calls to gcamLogLikelihoodTerm 458 tmin = 0.600333 Calls to gcamLabelTerm 410 tmin = 7.73658 Calls to gcamJacobianTerm 458 tmin = 1.15268 Calls to gcamSmoothnessTerm 458 tmin = 0.47995 Calls to gcamComputeGradient 458 tmin = 16.2551 Calls to gcamComputeMetricProperties 6686 tmin = 6.87448 mri_ca_register took 0 hours, 58 minutes and 14 seconds. #VMPC# mri_ca_register VmPeak 4101060 FSRUNTIME@ mri_ca_register 0.9705 hours 32 threads @#@FSTIME 2020:07:08:13:40:52 mri_ca_register N 9 e 3494.02 S 46.55 U 29047.75 P 832% M 1371212 F 248 R 24167007 W 0 c 83255 w 1243243 I 145123 O 65908 L 5.10 10.86 8.95 @#@FSLOADPOST 2020:07:08:14:39:06 mri_ca_register N 9 5.29 6.73 7.69 #-------------------------------------- #@# SubCort Seg Wed Jul 8 14:39:07 CEST 2020 mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /work/bav5809/software/packages/freesurfer/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz sysname Linux hostname node012 machine x86_64 setenv SUBJECTS_DIR /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142 cd /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /work/bav5809/software/packages/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 = 32 == reading 1 input volumes reading classifier array from /work/bav5809/software/packages/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 6.40 Atlas used for the 3D morph was /work/bav5809/software/packages/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 = 0.08429 (18) Left_Lateral_Ventricle (4): linear fit = 0.85 x + 0.0 (8170 voxels, overlap=0.758) Left_Lateral_Ventricle (4): linear fit = 0.85 x + 0.0 (8170 voxels, peak = 17), gca=16.9 gca peak = 0.20380 (13) mri peak = 0.06768 (24) Right_Lateral_Ventricle (43): linear fit = 1.27 x + 0.0 (2939 voxels, overlap=0.669) Right_Lateral_Ventricle (43): linear fit = 1.27 x + 0.0 (2939 voxels, peak = 17), gca=16.6 gca peak = 0.26283 (96) mri peak = 0.12502 (83) Right_Pallidum (52): linear fit = 0.86 x + 0.0 (141 voxels, overlap=0.029) Right_Pallidum (52): linear fit = 0.86 x + 0.0 (141 voxels, peak = 82), gca=82.1 gca peak = 0.15814 (97) mri peak = 0.10204 (93) Left_Pallidum (13): linear fit = 0.94 x + 0.0 (89 voxels, overlap=0.576) Left_Pallidum (13): linear fit = 0.94 x + 0.0 (89 voxels, peak = 92), gca=91.7 gca peak = 0.27624 (56) mri peak = 0.07547 (70) Right_Hippocampus (53): linear fit = 1.23 x + 0.0 (483 voxels, overlap=0.151) Right_Hippocampus (53): linear fit = 1.23 x + 0.0 (483 voxels, peak = 69), gca=68.6 gca peak = 0.28723 (59) mri peak = 0.06714 (57) Left_Hippocampus (17): linear fit = 1.04 x + 0.0 (414 voxels, overlap=1.015) Left_Hippocampus (17): linear fit = 1.04 x + 0.0 (414 voxels, peak = 62), gca=61.7 gca peak = 0.07623 (103) mri peak = 0.04508 (108) Right_Cerebral_White_Matter (41): linear fit = 1.09 x + 0.0 (29255 voxels, overlap=0.618) Right_Cerebral_White_Matter (41): linear fit = 1.09 x + 0.0 (29255 voxels, peak = 112), gca=111.8 gca peak = 0.07837 (105) mri peak = 0.05421 (108) Left_Cerebral_White_Matter (2): linear fit = 1.04 x + 0.0 (14377 voxels, overlap=0.666) Left_Cerebral_White_Matter (2): linear fit = 1.04 x + 0.0 (14377 voxels, peak = 110), gca=109.7 gca peak = 0.10165 (58) mri peak = 0.03407 (88) Left_Cerebral_Cortex (3): linear fit = 1.40 x + 0.0 (14689 voxels, overlap=0.000) Left_Cerebral_Cortex (3): linear fit = 1.40 x + 0.0 (14689 voxels, peak = 81), gca=81.5 gca peak = 0.11113 (58) mri peak = 0.03530 (64) Right_Cerebral_Cortex (42): linear fit = 1.08 x + 0.0 (19985 voxels, overlap=0.842) Right_Cerebral_Cortex (42): linear fit = 1.08 x + 0.0 (19985 voxels, peak = 62), gca=62.4 gca peak = 0.27796 (67) mri peak = 0.05656 (72) Right_Caudate (50): linear fit = 1.14 x + 0.0 (1681 voxels, overlap=0.634) Right_Caudate (50): linear fit = 1.14 x + 0.0 (1681 voxels, peak = 77), gca=76.7 gca peak = 0.14473 (69) mri peak = 0.05987 (68) Left_Caudate (11): linear fit = 0.96 x + 0.0 (631 voxels, overlap=1.010) Left_Caudate (11): linear fit = 0.96 x + 0.0 (631 voxels, peak = 67), gca=66.6 gca peak = 0.14301 (56) mri peak = 0.04305 (59) Left_Cerebellum_Cortex (8): linear fit = 1.13 x + 0.0 (10800 voxels, overlap=0.737) Left_Cerebellum_Cortex (8): linear fit = 1.13 x + 0.0 (10800 voxels, peak = 64), gca=63.6 gca peak = 0.14610 (55) mri peak = 0.04263 (64) Right_Cerebellum_Cortex (47): linear fit = 1.07 x + 0.0 (11853 voxels, overlap=0.704) Right_Cerebellum_Cortex (47): linear fit = 1.07 x + 0.0 (11853 voxels, peak = 59), gca=58.6 gca peak = 0.16309 (85) mri peak = 0.05428 (88) Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (4138 voxels, overlap=0.835) Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (4138 voxels, peak = 91), gca=90.5 gca peak = 0.15172 (84) mri peak = 0.06043 (88) Right_Cerebellum_White_Matter (46): linear fit = 1.04 x + 0.0 (5121 voxels, overlap=0.953) Right_Cerebellum_White_Matter (46): linear fit = 1.04 x + 0.0 (5121 voxels, peak = 88), gca=87.8 gca peak = 0.30461 (58) mri peak = 0.15059 (56) Left_Amygdala (18): linear fit = 0.94 x + 0.0 (150 voxels, overlap=0.702) Left_Amygdala (18): linear fit = 0.94 x + 0.0 (150 voxels, peak = 55), gca=54.8 gca peak = 0.32293 (57) mri peak = 0.09138 (69) Right_Amygdala (54): linear fit = 1.16 x + 0.0 (383 voxels, overlap=0.063) Right_Amygdala (54): linear fit = 1.16 x + 0.0 (383 voxels, peak = 66), gca=66.4 gca peak = 0.11083 (90) mri peak = 0.08905 (78) Left_Thalamus (10): linear fit = 0.94 x + 0.0 (3129 voxels, overlap=0.710) Left_Thalamus (10): linear fit = 0.94 x + 0.0 (3129 voxels, peak = 85), gca=85.1 gca peak = 0.11393 (83) mri peak = 0.07975 (86) Right_Thalamus (49): linear fit = 1.00 x + 0.0 (2210 voxels, overlap=0.979) Right_Thalamus (49): linear fit = 1.00 x + 0.0 (2210 voxels, peak = 83), gca=82.6 gca peak = 0.08575 (81) mri peak = 0.07077 (72) Left_Putamen (12): linear fit = 0.94 x + 0.0 (270 voxels, overlap=0.783) Left_Putamen (12): linear fit = 0.94 x + 0.0 (270 voxels, peak = 77), gca=76.5 gca peak = 0.08618 (78) mri peak = 0.04083 (73) Right_Putamen (51): linear fit = 0.99 x + 0.0 (3056 voxels, overlap=0.931) Right_Putamen (51): linear fit = 0.99 x + 0.0 (3056 voxels, peak = 77), gca=76.8 gca peak = 0.08005 (78) mri peak = 0.22186 (80) Brain_Stem: unreasonable value (79.2/80.0), not in range [80, 110] - rejecting gca peak = 0.12854 (88) mri peak = 0.13881 (83) Right_VentralDC (60): linear fit = 0.96 x + 0.0 (827 voxels, overlap=0.814) Right_VentralDC (60): linear fit = 0.96 x + 0.0 (827 voxels, peak = 85), gca=84.9 gca peak = 0.15703 (87) mri peak = 0.13097 (83) Left_VentralDC (28): linear fit = 0.95 x + 0.0 (1267 voxels, overlap=0.816) Left_VentralDC (28): linear fit = 0.95 x + 0.0 (1267 voxels, peak = 83), gca=83.1 gca peak = 0.17522 (25) mri peak = 0.12055 (17) Third_Ventricle (14): linear fit = 0.61 x + 0.0 (385 voxels, overlap=0.161) Third_Ventricle (14): linear fit = 0.61 x + 0.0 (385 voxels, peak = 15), gca=15.1 gca peak = 0.17113 (14) mri peak = 0.08781 (14) Fourth_Ventricle (15): linear fit = 0.92 x + 0.0 (163 voxels, overlap=0.712) Fourth_Ventricle (15): linear fit = 0.92 x + 0.0 (163 voxels, peak = 13), gca=12.8 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.16627 (28) gca peak Third_Ventricle = 0.17522 (25) gca peak Brain_Stem = 0.08005 (78) gca peak CSF = 0.20346 (36) gca peak Left_Accumbens_area = 0.70646 (62) gca peak Left_undetermined = 1.00000 (28) gca peak Left_vessel = 0.89917 (53) gca peak Left_choroid_plexus = 0.11689 (35) gca peak Right_Inf_Lat_Vent = 0.25504 (23) gca peak Right_Accumbens_area = 0.31650 (65) gca peak Right_vessel = 0.77268 (52) gca peak Right_choroid_plexus = 0.13275 (38) gca peak Fifth_Ventricle = 0.60973 (33) gca peak WM_hypointensities = 0.11013 (77) gca peak non_WM_hypointensities = 0.11354 (41) gca peak Optic_Chiasm = 0.51646 (76) not using caudate to estimate GM means estimating mean gm scale to be 1.14 x + 0.0 estimating mean wm scale to be 1.07 x + 0.0 estimating mean csf scale to be 1.01 x + 0.0 saving intensity scales to aseg.auto_noCCseg.label_intensities.txt renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.16536 (17) mri peak = 0.08429 (18) Left_Lateral_Ventricle (4): linear fit = 1.03 x + 0.0 (8170 voxels, overlap=0.757) Left_Lateral_Ventricle (4): linear fit = 1.03 x + 0.0 (8170 voxels, peak = 18), gca=17.6 gca peak = 0.15992 (16) mri peak = 0.06768 (24) Right_Lateral_Ventricle (43): linear fit = 1.09 x + 0.0 (2939 voxels, overlap=0.779) Right_Lateral_Ventricle (43): linear fit = 1.09 x + 0.0 (2939 voxels, peak = 17), gca=17.4 gca peak = 0.29807 (82) mri peak = 0.12502 (83) Right_Pallidum (52): linear fit = 1.00 x + 0.0 (141 voxels, overlap=1.002) Right_Pallidum (52): linear fit = 1.00 x + 0.0 (141 voxels, peak = 82), gca=82.0 gca peak = 0.19007 (91) mri peak = 0.10204 (93) Left_Pallidum (13): linear fit = 1.01 x + 0.0 (89 voxels, overlap=0.989) Left_Pallidum (13): linear fit = 1.01 x + 0.0 (89 voxels, peak = 92), gca=92.4 gca peak = 0.23886 (68) mri peak = 0.07547 (70) Right_Hippocampus (53): linear fit = 1.01 x + 0.0 (483 voxels, overlap=0.984) Right_Hippocampus (53): linear fit = 1.01 x + 0.0 (483 voxels, peak = 69), gca=69.0 gca peak = 0.30203 (59) mri peak = 0.06714 (57) Left_Hippocampus (17): linear fit = 0.92 x + 0.0 (414 voxels, overlap=0.977) Left_Hippocampus (17): linear fit = 0.92 x + 0.0 (414 voxels, peak = 54), gca=54.0 gca peak = 0.07174 (112) mri peak = 0.04508 (108) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (29255 voxels, overlap=0.902) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (29255 voxels, peak = 112), gca=112.0 gca peak = 0.07439 (109) mri peak = 0.05421 (108) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (14377 voxels, overlap=0.851) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (14377 voxels, peak = 110), gca=109.5 gca peak = 0.07107 (82) mri peak = 0.03407 (88) Left_Cerebral_Cortex (3): linear fit = 1.01 x + 0.0 (14689 voxels, overlap=0.931) Left_Cerebral_Cortex (3): linear fit = 1.01 x + 0.0 (14689 voxels, peak = 83), gca=83.2 gca peak = 0.10672 (63) mri peak = 0.03530 (64) Right_Cerebral_Cortex (42): linear fit = 1.02 x + 0.0 (19985 voxels, overlap=0.961) Right_Cerebral_Cortex (42): linear fit = 1.02 x + 0.0 (19985 voxels, peak = 65), gca=64.6 gca peak = 0.21908 (76) mri peak = 0.05656 (72) Right_Caudate (50): linear fit = 1.02 x + 0.0 (1681 voxels, overlap=1.008) Right_Caudate (50): linear fit = 1.02 x + 0.0 (1681 voxels, peak = 78), gca=77.9 gca peak = 0.14004 (66) mri peak = 0.05987 (68) Left_Caudate (11): linear fit = 1.00 x + 0.0 (631 voxels, overlap=1.005) Left_Caudate (11): linear fit = 1.00 x + 0.0 (631 voxels, peak = 66), gca=66.0 gca peak = 0.12121 (64) mri peak = 0.04305 (59) Left_Cerebellum_Cortex (8): linear fit = 0.99 x + 0.0 (10800 voxels, overlap=0.995) Left_Cerebellum_Cortex (8): linear fit = 0.99 x + 0.0 (10800 voxels, peak = 63), gca=63.0 gca peak = 0.14370 (59) mri peak = 0.04263 (64) Right_Cerebellum_Cortex (47): linear fit = 1.02 x + 0.0 (11853 voxels, overlap=0.891) Right_Cerebellum_Cortex (47): linear fit = 1.02 x + 0.0 (11853 voxels, peak = 60), gca=60.5 gca peak = 0.15236 (90) mri peak = 0.05428 (88) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (4138 voxels, overlap=0.980) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (4138 voxels, peak = 90), gca=89.6 gca peak = 0.15253 (88) mri peak = 0.06043 (88) Right_Cerebellum_White_Matter (46): linear fit = 0.99 x + 0.0 (5121 voxels, overlap=0.994) Right_Cerebellum_White_Matter (46): linear fit = 0.99 x + 0.0 (5121 voxels, peak = 87), gca=86.7 gca peak = 0.28484 (55) mri peak = 0.15059 (56) Left_Amygdala (18): linear fit = 0.99 x + 0.0 (150 voxels, overlap=0.673) Left_Amygdala (18): linear fit = 0.99 x + 0.0 (150 voxels, peak = 54), gca=54.2 gca peak = 0.29728 (67) mri peak = 0.09138 (69) Right_Amygdala (54): linear fit = 1.01 x + 0.0 (383 voxels, overlap=1.017) Right_Amygdala (54): linear fit = 1.01 x + 0.0 (383 voxels, peak = 68), gca=68.0 gca peak = 0.11853 (85) mri peak = 0.08905 (78) Left_Thalamus (10): linear fit = 0.99 x + 0.0 (3129 voxels, overlap=0.948) Left_Thalamus (10): linear fit = 0.99 x + 0.0 (3129 voxels, peak = 84), gca=83.7 gca peak = 0.10732 (82) mri peak = 0.07975 (86) Right_Thalamus (49): linear fit = 1.01 x + 0.0 (2210 voxels, overlap=0.972) Right_Thalamus (49): linear fit = 1.01 x + 0.0 (2210 voxels, peak = 83), gca=83.2 gca peak = 0.09021 (75) mri peak = 0.07077 (72) Left_Putamen (12): linear fit = 0.99 x + 0.0 (270 voxels, overlap=0.961) Left_Putamen (12): linear fit = 0.99 x + 0.0 (270 voxels, peak = 74), gca=73.9 gca peak = 0.09458 (76) mri peak = 0.04083 (73) Right_Putamen (51): linear fit = 1.00 x + 0.0 (3056 voxels, overlap=0.909) Right_Putamen (51): linear fit = 1.00 x + 0.0 (3056 voxels, peak = 76), gca=75.6 gca peak = 0.07709 (86) mri peak = 0.22186 (80) Brain_Stem (16): linear fit = 0.94 x + 0.0 (8317 voxels, overlap=0.513) Brain_Stem (16): linear fit = 0.94 x + 0.0 (8317 voxels, peak = 80), gca=80.4 gca peak = 0.12559 (87) mri peak = 0.13881 (83) Right_VentralDC (60): linear fit = 1.00 x + 0.0 (827 voxels, overlap=0.776) Right_VentralDC (60): linear fit = 1.00 x + 0.0 (827 voxels, peak = 87), gca=86.6 gca peak = 0.13991 (83) mri peak = 0.13097 (83) Left_VentralDC (28): linear fit = 0.99 x + 0.0 (1267 voxels, overlap=0.849) Left_VentralDC (28): linear fit = 0.99 x + 0.0 (1267 voxels, peak = 82), gca=81.8 gca peak = 0.16340 (27) mri peak = 0.12055 (17) Third_Ventricle (14): linear fit = 0.58 x + 0.0 (385 voxels, overlap=0.153) Third_Ventricle (14): linear fit = 0.58 x + 0.0 (385 voxels, peak = 16), gca=15.8 gca peak = 0.18985 (15) mri peak = 0.08781 (14) Fourth_Ventricle (15): linear fit = 1.03 x + 0.0 (163 voxels, overlap=0.760) Fourth_Ventricle (15): linear fit = 1.03 x + 0.0 (163 voxels, peak = 16), gca=15.5 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.17817 (29) gca peak Third_Ventricle = 0.16340 (27) gca peak CSF = 0.19306 (37) gca peak Left_Accumbens_area = 0.36931 (60) gca peak Left_undetermined = 1.00000 (28) gca peak Left_vessel = 0.89874 (53) gca peak Left_choroid_plexus = 0.11689 (35) gca peak Right_Inf_Lat_Vent = 0.22619 (28) gca peak Right_Accumbens_area = 0.29298 (74) gca peak Right_vessel = 0.77268 (52) gca peak Right_choroid_plexus = 0.13275 (38) gca peak Fifth_Ventricle = 0.73673 (33) gca peak WM_hypointensities = 0.09919 (82) gca peak non_WM_hypointensities = 0.13942 (43) gca peak Optic_Chiasm = 0.51819 (76) not using caudate to estimate GM means estimating mean gm scale to be 0.99 x + 0.0 estimating mean wm scale to be 1.00 x + 0.0 estimating mean csf scale to be 1.05 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 74415 voxels changed in iteration 0 of unlikely voxel relabeling 331 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 54696 gm and wm labels changed (%32 to gray, %68 to white out of all changed labels) 477 hippocampal voxels changed. 0 amygdala voxels changed. Reclassifying using Gibbs Priors pass 1: 79110 changed. image ll: -2.151, PF=0.500 pass 2: 21109 changed. image ll: -2.150, PF=0.500 pass 3: 6411 changed. pass 4: 2253 changed. 51620 voxels changed in iteration 0 of unlikely voxel relabeling 197 voxels changed in iteration 1 of unlikely voxel relabeling 7 voxels changed in iteration 2 of unlikely voxel relabeling 0 voxels changed in iteration 3 of unlikely voxel relabeling 6898 voxels changed in iteration 0 of unlikely voxel relabeling 95 voxels changed in iteration 1 of unlikely voxel relabeling 2 voxels changed in iteration 2 of unlikely voxel relabeling 0 voxels changed in iteration 3 of unlikely voxel relabeling 7190 voxels changed in iteration 0 of unlikely voxel relabeling 89 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 6386 voxels changed in iteration 0 of unlikely voxel relabeling 24 voxels changed in iteration 1 of unlikely voxel relabeling 2 voxels changed in iteration 2 of unlikely voxel relabeling 0 voxels changed in iteration 3 of unlikely voxel relabeling !!!!!!!!! ventricle segment 0 with volume 22877 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 2 with volume 269 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 3 with volume 212 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 0 with volume 13463 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 4 with volume 329 above threshold 100 - not erasing !!!!!!!!!! writing labeled volume to aseg.auto_noCCseg.mgz mri_ca_label utimesec 3579.036565 mri_ca_label stimesec 8.126699 mri_ca_label ru_maxrss 2108596 mri_ca_label ru_ixrss 0 mri_ca_label ru_idrss 0 mri_ca_label ru_isrss 0 mri_ca_label ru_minflt 9309510 mri_ca_label ru_majflt 226 mri_ca_label ru_nswap 0 mri_ca_label ru_inblock 209502 mri_ca_label ru_oublock 644 mri_ca_label ru_msgsnd 0 mri_ca_label ru_msgrcv 0 mri_ca_label ru_nsignals 0 mri_ca_label ru_nvcsw 8286 mri_ca_label ru_nivcsw 1804 auto-labeling took 56 minutes and 2 seconds. @#@FSTIME 2020:07:08:14:39:07 mri_ca_label N 10 e 3363.01 S 8.20 U 3579.04 P 106% M 2108596 F 226 R 9309524 W 0 c 1804 w 8289 I 209502 O 644 L 5.29 6.73 7.69 @#@FSLOADPOST 2020:07:08:15:35:10 mri_ca_label N 10 1.05 1.12 1.30 #-------------------------------------- #@# CC Seg Wed Jul 8 15:35:10 CEST 2020 mri_cc -aseg aseg.auto_noCCseg.mgz -o aseg.auto.mgz -lta /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/transforms/cc_up.lta SEVEREp142 will read input aseg from aseg.auto_noCCseg.mgz writing aseg with cc labels to aseg.auto.mgz will write lta as /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/transforms/cc_up.lta reading aseg from /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/aseg.auto_noCCseg.mgz reading norm from /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/norm.mgz 36143 voxels in left wm, 62701 in right wm, xrange [125, 140] searching rotation angles z=[-6 8], y=[-2 12] searching scale 1 Z rot -5.7 searching scale 1 Z rot -5.4 searching scale 1 Z rot -5.2 searching scale 1 Z rot -4.9 searching scale 1 Z rot -4.7 searching scale 1 Z rot -4.4 searching scale 1 Z rot -4.2 searching scale 1 Z rot -3.9 searching scale 1 Z rot -3.7 searching scale 1 Z rot -3.4 searching scale 1 Z rot -3.2 searching scale 1 Z rot -2.9 searching scale 1 Z rot -2.7 searching scale 1 Z rot -2.4 searching scale 1 Z rot -2.2 searching scale 1 Z rot -1.9 searching scale 1 Z rot -1.7 searching scale 1 Z rot -1.4 searching scale 1 Z rot -1.2 searching scale 1 Z rot -0.9 searching scale 1 Z rot -0.7 searching scale 1 Z rot -0.4 searching scale 1 Z rot -0.2 searching scale 1 Z rot 0.1 searching scale 1 Z rot 0.3 searching scale 1 Z rot 0.6 searching scale 1 Z rot 0.8 searching scale 1 Z rot 1.1 searching scale 1 Z rot 1.3 searching scale 1 Z rot 1.6 searching scale 1 Z rot 1.8 searching scale 1 Z rot 2.1 searching scale 1 Z rot 2.3 searching scale 1 Z rot 2.6 searching scale 1 Z rot 2.8 searching scale 1 Z rot 3.1 searching scale 1 Z rot 3.3 searching scale 1 Z rot 3.6 searching scale 1 Z rot 3.8 searching scale 1 Z rot 4.1 searching scale 1 Z rot 4.3 searching scale 1 Z rot 4.6 searching scale 1 Z rot 4.8 searching scale 1 Z rot 5.1 searching scale 1 Z rot 5.3 searching scale 1 Z rot 5.6 searching scale 1 Z rot 5.8 searching scale 1 Z rot 6.1 searching scale 1 Z rot 6.3 searching scale 1 Z rot 6.6 searching scale 1 Z rot 6.8 searching scale 1 Z rot 7.1 searching scale 1 Z rot 7.3 searching scale 1 Z rot 7.6 searching scale 1 Z rot 7.8 searching scale 1 Z rot 8.1 global minimum found at slice 135.2, rotations (5.12, 1.32) final transformation (x=135.2, yr=5.123, zr=1.315): 0.99574 -0.02296 0.08927 -10.75874; 0.02286 0.99974 0.00205 -17.22353; -0.08929 -0.00000 0.99601 57.40207; 0.00000 0.00000 0.00000 1.00000; updating x range to be [125, 133] in xformed coordinates best xformed slice 128 cc center is found at 128 142 83 eigenvectors: -0.00214 0.00490 0.99999; -0.52677 -0.85000 0.00303; 0.85001 -0.52676 0.00440; error in mid anterior detected - correcting... error in mid anterior detected - correcting... error in mid anterior detected - correcting... writing aseg with callosum to /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/aseg.auto.mgz... corpus callosum segmentation took 0.9 minutes @#@FSTIME 2020:07:08:15:35:10 mri_cc N 7 e 54.01 S 0.39 U 52.30 P 97% M 337116 F 171 R 452375 W 0 c 105 w 746 I 3244 O 640 L 1.05 1.12 1.30 @#@FSLOADPOST 2020:07:08:15:36:04 mri_cc N 7 1.06 1.12 1.28 #-------------------------------------- #@# Merge ASeg Wed Jul 8 15:36:06 CEST 2020 cp aseg.auto.mgz aseg.presurf.mgz #-------------------------------------------- #@# Intensity Normalization2 Wed Jul 8 15:36:07 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/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... MRImask(): AllowDiffGeom = 1 Reading aseg aseg.presurf.mgz normalizing image... processing with aseg removing outliers in the aseg WM... 1957 control points removed 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 89 gm peak at 71 (71), valley at 56 (56) csf peak at 18, setting threshold to 53 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... --------------------------------- 3d normalization pass 2 of 2 white matter peak found at 110 white matter peak found at 90 gm peak at 72 (72), valley at 36 (36) csf peak at 18, setting threshold to 54 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Done iterating --------------------------------- writing output to brain.mgz 3D bias adjustment took 2 minutes and 50 seconds. @#@FSTIME 2020:07:08:15:36:07 mri_normalize N 9 e 173.97 S 3.86 U 273.77 P 159% M 1208992 F 198 R 1290702 W 0 c 553 w 2469 I 5810 O 2584 L 1.06 1.12 1.28 @#@FSLOADPOST 2020:07:08:15:39:01 mri_normalize N 9 1.24 1.14 1.26 #-------------------------------------------- #@# Mask BFS Wed Jul 8 15:39:01 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri mri_mask -T 5 brain.mgz brainmask.mgz brain.finalsurfs.mgz threshold mask volume at 5 DoAbs = 0 Found 1417844 voxels in mask (pct= 8.45) MRImask(): AllowDiffGeom = 1 Writing masked volume to brain.finalsurfs.mgz...done. @#@FSTIME 2020:07:08:15:39:01 mri_mask N 5 e 2.53 S 0.06 U 2.56 P 103% M 72856 F 140 R 17339 W 0 c 27 w 484 I 5151 O 2552 L 1.24 1.14 1.26 @#@FSLOADPOST 2020:07:08:15:39:04 mri_mask N 5 1.14 1.12 1.26 #-------------------------------------------- #@# WM Segmentation Wed Jul 8 15:39:04 CEST 2020 AntsDenoiseImageFs -i brain.mgz -o antsdn.brain.mgz @#@FSTIME 2020:07:08:15:39:04 AntsDenoiseImageFs N 4 e 59.55 S 0.19 U 56.56 P 95% M 349716 F 201 R 224096 W 0 c 58 w 447 I 2586 O 2585 L 1.14 1.12 1.26 @#@FSLOADPOST 2020:07:08:15:40:04 AntsDenoiseImageFs N 4 1.10 1.11 1.24 mri_segment -wsizemm 13 -mprage antsdn.brain.mgz wm.seg.mgz wsizemm = 13, voxres = 1, wsize = 13 WHITE_MATTER_MEAN 110 wsize 13 assuming input volume is MGH (Van der Kouwe) MP-RAGE 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 (101.0): 102.0 +- 6.7 [79.0 --> 125.0] CCS GM (73.0) : 70.7 +- 9.5 [30.0 --> 95.0] white_mean 101.968 white_sigma 6.68957 gray_mean 70.7116 gray_sigma 9.48742 setting bottom of white matter range wm_low to 80.2 setting top of gray matter range gray_hi to 89.7 wm_low 80.199 wm_hi 125 gray_low 30 gray_hi 89.6864 Redoing initial intensity segmentation... Recomputing local statistics to label ambiguous voxels... wm_low 80.199 wm_hi 125 gray_low 30 gray_hi 89.6864 using local geometry to label remaining ambiguous voxels... polvwsize = 5, polvlen = 3, gray_hi = 89.6864, wm_low = 80.199 MRIcpolvMedianCurveSegment(): wsize=5, len=3, gmhi=89.6864, wmlow=80.199 149036 voxels processed (0.89%) 71230 voxels white (0.42%) 77806 voxels non-white (0.46%) Reclassifying voxels using Gaussian border classifier niter=1 MRIreclassify(): wm_low=75.199, gray_hi=89.6864, wsize=13 242773 voxels tested (1.45%) 56365 voxels changed (0.34%) 53534 multi-scale searches (0.32%) Recovering bright white MRIrecoverBrightWhite() wm_low 80.199 wm_hi 125 slack 6.68957 pct_thresh 0.33 intensity_thresh 131.69 nvox_thresh 8.58 255 voxels tested (0.00%) 224 voxels changed (0.00%) removing voxels with positive offset direction... MRIremoveWrongDirection() wsize=3, lowthr=75.199, hithr=89.6864 smoothing input volume with sigma = 0.250 108920 voxels tested (0.65%) 19621 voxels changed (0.12%) thicken = 1 removing 1-dimensional structures... MRIremove1dStructures(): max_iter=10000, thresh=2, WM_MIN_VAL=5 2300 sparsely connected voxels removed in 1 iterations thickening thin strands.... thickness 4 nsegments 20 wm_hi 125 2230 diagonally connected voxels added... MRIthickenThinWMStrands(): thickness=4, nsegments=20 20 segments, 3042 filled MRIfindBrightNonWM(): 393 bright non-wm voxels segmented. MRIfilterMorphology() WM_MIN_VAL=5, DIAGONAL_FILL=230 white matter segmentation took 1.5 minutes writing output to wm.seg.mgz... @#@FSTIME 2020:07:08:15:40:05 mri_segment N 5 e 93.01 S 0.47 U 89.05 P 96% M 142760 F 169 R 356906 W 0 c 178 w 588 I 2587 O 750 L 1.10 1.11 1.24 @#@FSLOADPOST 2020:07:08:15:41:38 mri_segment N 5 1.23 1.14 1.24 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 0.63 minutes reading wm segmentation from wm.seg.mgz... 0 voxels added to wm to prevent paths from MTL structures to cortex 11816 additional wm voxels added 0 additional wm voxels added SEG EDIT: 106185 voxels turned on, 11431 voxels turned off. propagating editing to output volume from wm.seg.mgz 115,126,128 old 106 new 106 115,126,128 old 106 new 106 writing edited volume to wm.asegedit.mgz.... @#@FSTIME 2020:07:08:15:41:38 mri_edit_wm_with_aseg N 5 e 38.34 S 0.42 U 42.91 P 113% M 475580 F 143 R 492099 W 0 c 98 w 1165 I 4724 O 740 L 1.23 1.14 1.24 @#@FSLOADPOST 2020:07:08:15:42:16 mri_edit_wm_with_aseg N 5 1.11 1.12 1.23 mri_pretess wm.asegedit.mgz wm norm.mgz wm.mgz Iteration Number : 1 pass 1 (xy+): 53 found - 53 modified | TOTAL: 53 pass 2 (xy+): 0 found - 53 modified | TOTAL: 53 pass 1 (xy-): 52 found - 52 modified | TOTAL: 105 pass 2 (xy-): 0 found - 52 modified | TOTAL: 105 pass 1 (yz+): 71 found - 71 modified | TOTAL: 176 pass 2 (yz+): 0 found - 71 modified | TOTAL: 176 pass 1 (yz-): 63 found - 63 modified | TOTAL: 239 pass 2 (yz-): 0 found - 63 modified | TOTAL: 239 pass 1 (xz+): 57 found - 57 modified | TOTAL: 296 pass 2 (xz+): 0 found - 57 modified | TOTAL: 296 pass 1 (xz-): 47 found - 47 modified | TOTAL: 343 pass 2 (xz-): 0 found - 47 modified | TOTAL: 343 Iteration Number : 1 pass 1 (+++): 10 found - 10 modified | TOTAL: 10 pass 2 (+++): 0 found - 10 modified | TOTAL: 10 pass 1 (+++): 14 found - 14 modified | TOTAL: 24 pass 2 (+++): 0 found - 14 modified | TOTAL: 24 pass 1 (+++): 29 found - 29 modified | TOTAL: 53 pass 2 (+++): 0 found - 29 modified | TOTAL: 53 pass 1 (+++): 19 found - 19 modified | TOTAL: 72 pass 2 (+++): 0 found - 19 modified | TOTAL: 72 Iteration Number : 1 pass 1 (++): 69 found - 69 modified | TOTAL: 69 pass 2 (++): 0 found - 69 modified | TOTAL: 69 pass 1 (+-): 67 found - 67 modified | TOTAL: 136 pass 2 (+-): 0 found - 67 modified | TOTAL: 136 pass 1 (--): 60 found - 60 modified | TOTAL: 196 pass 2 (--): 0 found - 60 modified | TOTAL: 196 pass 1 (-+): 84 found - 84 modified | TOTAL: 280 pass 2 (-+): 0 found - 84 modified | TOTAL: 280 Iteration Number : 2 pass 1 (xy+): 7 found - 7 modified | TOTAL: 7 pass 2 (xy+): 0 found - 7 modified | TOTAL: 7 pass 1 (xy-): 9 found - 9 modified | TOTAL: 16 pass 2 (xy-): 0 found - 9 modified | TOTAL: 16 pass 1 (yz+): 7 found - 7 modified | TOTAL: 23 pass 2 (yz+): 0 found - 7 modified | TOTAL: 23 pass 1 (yz-): 4 found - 4 modified | TOTAL: 27 pass 2 (yz-): 0 found - 4 modified | TOTAL: 27 pass 1 (xz+): 4 found - 4 modified | TOTAL: 31 pass 2 (xz+): 0 found - 4 modified | TOTAL: 31 pass 1 (xz-): 3 found - 3 modified | TOTAL: 34 pass 2 (xz-): 0 found - 3 modified | TOTAL: 34 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 (++): 1 found - 1 modified | TOTAL: 1 pass 2 (++): 0 found - 1 modified | TOTAL: 1 pass 1 (+-): 2 found - 2 modified | TOTAL: 3 pass 2 (+-): 0 found - 2 modified | TOTAL: 3 pass 1 (--): 1 found - 1 modified | TOTAL: 4 pass 2 (--): 0 found - 1 modified | TOTAL: 4 pass 1 (-+): 1 found - 1 modified | TOTAL: 5 pass 2 (-+): 0 found - 1 modified | TOTAL: 5 Iteration Number : 3 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 1 found - 1 modified | TOTAL: 1 pass 2 (xy-): 0 found - 1 modified | TOTAL: 1 pass 1 (yz+): 0 found - 0 modified | TOTAL: 1 pass 1 (yz-): 0 found - 0 modified | TOTAL: 1 pass 1 (xz+): 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 (++): 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 pass 1 (-+): 0 found - 0 modified | TOTAL: 1 Iteration Number : 4 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 0 found - 0 modified | TOTAL: 0 pass 1 (yz+): 0 found - 0 modified | TOTAL: 0 pass 1 (yz-): 0 found - 0 modified | TOTAL: 0 pass 1 (xz+): 0 found - 0 modified | TOTAL: 0 pass 1 (xz-): 0 found - 0 modified | TOTAL: 0 Iteration Number : 4 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 4 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 Total Number of Modified Voxels = 736 (out of 447167: 0.164592) binarizing input wm segmentation... Ambiguous edge configurations... mri_pretess done @#@FSTIME 2020:07:08:15:42:16 mri_pretess N 4 e 5.17 S 0.31 U 5.08 P 104% M 55452 F 135 R 26194 W 0 c 25 w 466 I 3349 O 742 L 1.11 1.12 1.23 @#@FSLOADPOST 2020:07:08:15:42:21 mri_pretess N 4 1.11 1.12 1.23 #-------------------------------------------- #@# Fill Wed Jul 8 15:42:21 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri mri_fill -a ../scripts/ponscc.cut.log -xform transforms/talairach.lta -segmentation aseg.presurf.mgz 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 1.03905 0.01069 0.15350 -30.66603; -0.06919 0.96707 0.65474 -74.68274; -0.09918 -0.59415 0.82087 117.89958; 0.00000 0.00000 0.00000 1.00000; voxel to talairach voxel transform 1.03905 0.01069 0.15350 -30.66603; -0.06919 0.96707 0.65474 -74.68274; -0.09918 -0.59415 0.82087 117.89958; 0.00000 0.00000 0.00000 1.00000; reading segmented volume aseg.presurf.mgz removing CC from segmentation Looking for area (min, max) = (350, 1400) area[0] = 1270 (min = 350, max = 1400), aspect = 0.44 (min = 0.10, max = 0.75) no need to search using seed (126, 118, 152), TAL = (2.0, 24.0, 10.0) talairach voxel to voxel transform 0.94629 -0.07998 -0.11316 36.38739; -0.00652 0.69453 -0.55275 116.83824; 0.10962 0.49304 0.80446 -54.66296; 0.00000 0.00000 0.00000 1.00000; segmentation indicates cc at (126, 118, 152) --> (2.0, 24.0, 10.0) done. writing output to filled.mgz... filling took 1.3 minutes talairach cc position changed to (2.00, 24.00, 10.00) Erasing brainstem...done. MRImask(): AllowDiffGeom = 1 seed_search_size = 9, min_neighbors = 5 search rh wm seed point around talairach space:(20.00, 24.00, 10.00) SRC: (111.95, 114.07, 137.63) search lh wm seed point around talairach space (-16.00, 24.00, 10.00), SRC: (146.01, 113.84, 141.58) 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 ... @#@FSTIME 2020:07:08:15:42:21 mri_fill N 8 e 80.67 S 1.31 U 80.80 P 101% M 969196 F 183 R 972548 W 0 c 105 w 565 I 1387 O 231 L 1.11 1.12 1.23 @#@FSLOADPOST 2020:07:08:15:43:42 mri_fill N 8 1.18 1.14 1.22 #-------------------------------------------- #@# Tessellate lh Wed Jul 8 15:43:43 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/scripts mri_pretess ../mri/filled.mgz 255 ../mri/norm.mgz ../mri/filled-pretess255.mgz Iteration Number : 1 pass 1 (xy+): 1 found - 1 modified | TOTAL: 1 pass 2 (xy+): 0 found - 1 modified | TOTAL: 1 pass 1 (xy-): 0 found - 0 modified | TOTAL: 1 pass 1 (yz+): 1 found - 1 modified | TOTAL: 2 pass 2 (yz+): 0 found - 1 modified | TOTAL: 2 pass 1 (yz-): 9 found - 9 modified | TOTAL: 11 pass 2 (yz-): 0 found - 9 modified | TOTAL: 11 pass 1 (xz+): 0 found - 0 modified | TOTAL: 11 pass 1 (xz-): 1 found - 1 modified | TOTAL: 12 pass 2 (xz-): 0 found - 1 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 (++): 2 found - 2 modified | TOTAL: 2 pass 2 (++): 0 found - 2 modified | TOTAL: 2 pass 1 (+-): 0 found - 0 modified | TOTAL: 2 pass 1 (--): 4 found - 4 modified | TOTAL: 6 pass 2 (--): 0 found - 4 modified | TOTAL: 6 pass 1 (-+): 1 found - 1 modified | TOTAL: 7 pass 2 (-+): 0 found - 1 modified | TOTAL: 7 Iteration Number : 2 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 0 found - 0 modified | TOTAL: 0 pass 1 (yz+): 0 found - 0 modified | TOTAL: 0 pass 1 (yz-): 0 found - 0 modified | TOTAL: 0 pass 1 (xz+): 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 = 19 (out of 223167: 0.008514) Ambiguous edge configurations... mri_pretess done @#@FSTIME 2020:07:08:15:43:43 mri_pretess N 4 e 4.08 S 0.02 U 3.21 P 79% M 38956 F 134 R 13235 W 0 c 23 w 492 I 2839 O 231 L 1.18 1.14 1.22 @#@FSLOADPOST 2020:07:08:15:43:47 mri_pretess N 4 1.08 1.12 1.22 mri_tessellate ../mri/filled-pretess255.mgz 255 ../surf/lh.orig.nofix 7.1.0 7.1.0 slice 50: 3068 vertices, 3292 faces slice 60: 12155 vertices, 12546 faces slice 70: 23785 vertices, 24254 faces slice 80: 36775 vertices, 37247 faces slice 90: 48261 vertices, 48683 faces slice 100: 59835 vertices, 60327 faces slice 110: 71066 vertices, 71558 faces slice 120: 81517 vertices, 82021 faces slice 130: 91322 vertices, 91807 faces slice 140: 99802 vertices, 100225 faces slice 150: 106086 vertices, 106451 faces slice 160: 111187 vertices, 111520 faces slice 170: 115033 vertices, 115302 faces slice 180: 116376 vertices, 116559 faces slice 190: 116402 vertices, 116576 faces slice 200: 116402 vertices, 116576 faces slice 210: 116402 vertices, 116576 faces slice 220: 116402 vertices, 116576 faces slice 230: 116402 vertices, 116576 faces slice 240: 116402 vertices, 116576 faces slice 250: 116402 vertices, 116576 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 2020:07:08:15:43:47 mri_tessellate N 3 e 2.96 S 0.04 U 2.48 P 85% M 34516 F 144 R 8050 W 0 c 9 w 348 I 232 O 5462 L 1.08 1.12 1.22 @#@FSLOADPOST 2020:07:08:15:43:50 mri_tessellate N 3 1.08 1.11 1.21 rm -f ../mri/filled-pretess255.mgz mris_extract_main_component ../surf/lh.orig.nofix ../surf/lh.orig.nofix counting number of connected components... 116402 voxel in cpt #1: X=-174 [v=116402,e=349728,f=233152] located at (-32.230442, -27.874401, -7.347150) For the whole surface: X=-174 [v=116402,e=349728,f=233152] One single component has been found nothing to do done @#@FSTIME 2020:07:08:15:43:51 mris_extract_main_component N 2 e 2.58 S 0.15 U 2.23 P 92% M 230004 F 139 R 64846 W 0 c 17 w 867 I 5470 O 8193 L 1.08 1.11 1.21 @#@FSLOADPOST 2020:07:08:15:43:54 mris_extract_main_component N 2 0.99 1.10 1.21 #-------------------------------------------- #@# Smooth1 lh Wed Jul 8 15:43:54 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/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 2020:07:08:15:43:54 mris_smooth N 5 e 4.18 S 0.43 U 5.93 P 152% M 188604 F 171 R 84826 W 0 c 29 w 1250 I 8211 O 8194 L 0.99 1.10 1.21 @#@FSLOADPOST 2020:07:08:15:43:58 mris_smooth N 5 0.99 1.10 1.21 #-------------------------------------------- #@# Inflation1 lh Wed Jul 8 15:43:58 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/scripts mris_inflate -no-save-sulc ../surf/lh.smoothwm.nofix ../surf/lh.inflated.nofix Not saving sulc Reading ../surf/lh.smoothwm.nofix avg radius = 46.1 mm, total surface area = 60212 mm^2 step 000: RMS=0.176 (target=0.015) step 005: RMS=0.140 (target=0.015) step 010: RMS=0.114 (target=0.015) step 015: RMS=0.102 (target=0.015) step 020: RMS=0.095 (target=0.015) step 025: RMS=0.090 (target=0.015) step 030: RMS=0.086 (target=0.015) step 035: RMS=0.082 (target=0.015) step 040: RMS=0.078 (target=0.015) step 045: RMS=0.077 (target=0.015) step 050: RMS=0.075 (target=0.015) step 055: RMS=0.075 (target=0.015) step 060: RMS=0.075 (target=0.015) writing inflated surface to ../surf/lh.inflated.nofix inflation took 0.2 minutes inflation complete. Not saving sulc mris_inflate utimesec 72.710419 mris_inflate stimesec 2.087951 mris_inflate ru_maxrss 283156 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 169467 mris_inflate ru_majflt 187 mris_inflate ru_nswap 0 mris_inflate ru_inblock 8214 mris_inflate ru_oublock 8195 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 21517 mris_inflate ru_nivcsw 357 @#@FSTIME 2020:07:08:15:43:59 mris_inflate N 3 e 10.71 S 2.10 U 72.71 P 697% M 285584 F 187 R 169482 W 0 c 357 w 21519 I 8214 O 8195 L 0.91 1.08 1.20 @#@FSLOADPOST 2020:07:08:15:44:09 mris_inflate N 3 1.01 1.09 1.20 #-------------------------------------------- #@# QSphere lh Wed Jul 8 15:44:10 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/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 7.1.0 7.1.0 == Number of threads available to mris_sphere for OpenMP = 32 == scaling brain by 0.399... inflating... Entering MRISinflateToSphere() inflating to sphere (rms error < 2.00) 000: dt: 0.0000, rms radial error=171.747, avgs=0 005/300: dt: 0.9000, rms radial error=171.500, avgs=0 010/300: dt: 0.9000, rms radial error=170.965, avgs=0 015/300: dt: 0.9000, rms radial error=170.262, avgs=0 020/300: dt: 0.9000, rms radial error=169.461, avgs=0 025/300: dt: 0.9000, rms radial error=168.630, avgs=0 030/300: dt: 0.9000, rms radial error=167.765, avgs=0 035/300: dt: 0.9000, rms radial error=166.881, avgs=0 040/300: dt: 0.9000, rms radial error=165.981, avgs=0 045/300: dt: 0.9000, rms radial error=165.078, avgs=0 050/300: dt: 0.9000, rms radial error=164.176, avgs=0 055/300: dt: 0.9000, rms radial error=163.275, avgs=0 060/300: dt: 0.9000, rms radial error=162.378, avgs=0 065/300: dt: 0.9000, rms radial error=161.484, avgs=0 070/300: dt: 0.9000, rms radial error=160.597, avgs=0 075/300: dt: 0.9000, rms radial error=159.713, avgs=0 080/300: dt: 0.9000, rms radial error=158.833, avgs=0 085/300: dt: 0.9000, rms radial error=157.958, avgs=0 090/300: dt: 0.9000, rms radial error=157.087, avgs=0 095/300: dt: 0.9000, rms radial error=156.220, avgs=0 100/300: dt: 0.9000, rms radial error=155.358, avgs=0 105/300: dt: 0.9000, rms radial error=154.500, avgs=0 110/300: dt: 0.9000, rms radial error=153.646, avgs=0 115/300: dt: 0.9000, rms radial error=152.797, avgs=0 120/300: dt: 0.9000, rms radial error=151.952, avgs=0 125/300: dt: 0.9000, rms radial error=151.111, avgs=0 130/300: dt: 0.9000, rms radial error=150.275, avgs=0 135/300: dt: 0.9000, rms radial error=149.443, avgs=0 140/300: dt: 0.9000, rms radial error=148.616, avgs=0 145/300: dt: 0.9000, rms radial error=147.793, avgs=0 150/300: dt: 0.9000, rms radial error=146.974, avgs=0 155/300: dt: 0.9000, rms radial error=146.159, avgs=0 160/300: dt: 0.9000, rms radial error=145.348, avgs=0 165/300: dt: 0.9000, rms radial error=144.541, avgs=0 170/300: dt: 0.9000, rms radial error=143.739, avgs=0 175/300: dt: 0.9000, rms radial error=142.940, avgs=0 180/300: dt: 0.9000, rms radial error=142.146, avgs=0 185/300: dt: 0.9000, rms radial error=141.356, avgs=0 190/300: dt: 0.9000, rms radial error=140.570, avgs=0 195/300: dt: 0.9000, rms radial error=139.788, avgs=0 200/300: dt: 0.9000, rms radial error=139.010, avgs=0 205/300: dt: 0.9000, rms radial error=138.237, avgs=0 210/300: dt: 0.9000, rms radial error=137.468, avgs=0 215/300: dt: 0.9000, rms radial error=136.702, avgs=0 220/300: dt: 0.9000, rms radial error=135.941, avgs=0 225/300: dt: 0.9000, rms radial error=135.184, avgs=0 230/300: dt: 0.9000, rms radial error=134.430, avgs=0 235/300: dt: 0.9000, rms radial error=133.679, avgs=0 240/300: dt: 0.9000, rms radial error=132.933, avgs=0 245/300: dt: 0.9000, rms radial error=132.191, avgs=0 250/300: dt: 0.9000, rms radial error=131.453, avgs=0 255/300: dt: 0.9000, rms radial error=130.720, avgs=0 260/300: dt: 0.9000, rms radial error=129.990, avgs=0 265/300: dt: 0.9000, rms radial error=129.264, avgs=0 270/300: dt: 0.9000, rms radial error=128.543, avgs=0 275/300: dt: 0.9000, rms radial error=127.825, avgs=0 280/300: dt: 0.9000, rms radial error=127.112, avgs=0 285/300: dt: 0.9000, rms radial error=126.402, avgs=0 290/300: dt: 0.9000, rms radial error=125.696, avgs=0 295/300: dt: 0.9000, rms radial error=124.995, avgs=0 300/300: dt: 0.9000, rms radial error=124.297, avgs=0 spherical inflation complete. projecting onto sphere... surface projected - minimizing metric distortion... vertex spacing 1.04 +- 0.70 (0.00-->8.14) (max @ vno 90001 --> 90002) face area 0.04 +- 0.06 (-0.38-->1.45) epoch 1 (K=10.0), pass 1, starting sse = 12601.01 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00021 epoch 2 (K=40.0), pass 1, starting sse = 1950.46 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.02/13 = 0.00154 epoch 3 (K=160.0), pass 1, starting sse = 217.01 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.07/13 = 0.00523 epoch 4 (K=640.0), pass 1, starting sse = 32.12 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.05/14 = 0.00351 final distance error %100000.00 writing spherical brain to ../surf/lh.qsphere.nofix mris_sphere utimesec 545.227114 mris_sphere stimesec 18.327638 mris_sphere ru_maxrss 1556816 mris_sphere ru_ixrss 0 mris_sphere ru_idrss 0 mris_sphere ru_isrss 0 mris_sphere ru_minflt 676730 mris_sphere ru_majflt 217 mris_sphere ru_nswap 0 mris_sphere ru_inblock 8338 mris_sphere ru_oublock 8196 mris_sphere ru_msgsnd 0 mris_sphere ru_msgrcv 0 mris_sphere ru_nsignals 0 mris_sphere ru_nvcsw 113447 mris_sphere ru_nivcsw 2496 spherical transformation took 0.0176 hours FSRUNTIME@ mris_sphere 0.0176 hours 32 threads @#@FSTIME 2020:07:08:15:44:10 mris_sphere N 9 e 64.73 S 18.42 U 545.22 P 870% M 1558944 F 217 R 676743 W 0 c 2496 w 113449 I 8338 O 8196 L 1.01 1.09 1.20 @#@FSLOADPOST 2020:07:08:15:45:15 mris_sphere N 9 7.62 3.15 1.92 #@# Fix Topology lh Wed Jul 8 15:45:15 CEST 2020 mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 SEVEREp142 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' 7.1.0 7.1.0 before topology correction, eno=-174 (nv=116402, nf=233152, ne=349728, g=88) using quasi-homeomorphic spherical map to tessellate cortical surface... Correction of the Topology Finding true center and radius of Spherical Surface...done Surface centered at (0,0,0) with radius 100.0 in 9 iterations marking ambiguous vertices... 21029 ambiguous faces found in tessellation segmenting defects... 83 defects found, arbitrating ambiguous regions... analyzing neighboring defects... -merging segment 60 into 51 -merging segment 79 into 78 81 defects to be corrected 0 vertices coincident reading input surface /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/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: -9.3281 (-4.6641) -vertex loglikelihood: -6.6952 (-3.3476) -normal dot loglikelihood: -3.6295 (-3.6295) -quad curv loglikelihood: -5.9503 (-2.9751) Total Loglikelihood : -25.6031 CORRECTING DEFECT 0 (vertices=525, convex hull=118, v0=233) After retessellation of defect 0 (v0=233), euler #=-73 (104234,309224,204917) : difference with theory (-78) = -5 CORRECTING DEFECT 1 (vertices=44, convex hull=77, v0=439) After retessellation of defect 1 (v0=439), euler #=-72 (104252,309307,204983) : difference with theory (-77) = -5 CORRECTING DEFECT 2 (vertices=32, convex hull=98, v0=1022) After retessellation of defect 2 (v0=1022), euler #=-71 (104268,309395,205056) : difference with theory (-76) = -5 CORRECTING DEFECT 3 (vertices=172, convex hull=152, v0=1940) After retessellation of defect 3 (v0=1940), euler #=-70 (104341,309681,205270) : difference with theory (-75) = -5 CORRECTING DEFECT 4 (vertices=32, convex hull=35, v0=2243) After retessellation of defect 4 (v0=2243), euler #=-69 (104344,309703,205290) : difference with theory (-74) = -5 CORRECTING DEFECT 5 (vertices=14, convex hull=30, v0=2275) After retessellation of defect 5 (v0=2275), euler #=-68 (104346,309720,205306) : difference with theory (-73) = -5 CORRECTING DEFECT 6 (vertices=217, convex hull=56, v0=3068) After retessellation of defect 6 (v0=3068), euler #=-67 (104362,309793,205364) : difference with theory (-72) = -5 CORRECTING DEFECT 7 (vertices=248, convex hull=236, v0=5179) After retessellation of defect 7 (v0=5179), euler #=-66 (104443,310149,205640) : difference with theory (-71) = -5 CORRECTING DEFECT 8 (vertices=32, convex hull=78, v0=8073) After retessellation of defect 8 (v0=8073), euler #=-65 (104460,310230,205705) : difference with theory (-70) = -5 CORRECTING DEFECT 9 (vertices=127, convex hull=114, v0=11076) After retessellation of defect 9 (v0=11076), euler #=-64 (104502,310408,205842) : difference with theory (-69) = -5 CORRECTING DEFECT 10 (vertices=84, convex hull=78, v0=12343) After retessellation of defect 10 (v0=12343), euler #=-63 (104531,310530,205936) : difference with theory (-68) = -5 CORRECTING DEFECT 11 (vertices=700, convex hull=179, v0=14301) After retessellation of defect 11 (v0=14301), euler #=-62 (104645,310970,206263) : difference with theory (-67) = -5 CORRECTING DEFECT 12 (vertices=391, convex hull=84, v0=15349) After retessellation of defect 12 (v0=15349), euler #=-61 (104682,311116,206373) : difference with theory (-66) = -5 CORRECTING DEFECT 13 (vertices=62, convex hull=89, v0=17503) After retessellation of defect 13 (v0=17503), euler #=-60 (104707,311230,206463) : difference with theory (-65) = -5 CORRECTING DEFECT 14 (vertices=177, convex hull=123, v0=17572) After retessellation of defect 14 (v0=17572), euler #=-59 (104763,311459,206637) : difference with theory (-64) = -5 CORRECTING DEFECT 15 (vertices=41, convex hull=62, v0=17671) After retessellation of defect 15 (v0=17671), euler #=-58 (104779,311531,206694) : difference with theory (-63) = -5 CORRECTING DEFECT 16 (vertices=191, convex hull=203, v0=17705) After retessellation of defect 16 (v0=17705), euler #=-57 (104861,311874,206956) : difference with theory (-62) = -5 CORRECTING DEFECT 17 (vertices=367, convex hull=178, v0=17710) After retessellation of defect 17 (v0=17710), euler #=-57 (104951,312234,207226) : difference with theory (-61) = -4 CORRECTING DEFECT 18 (vertices=65, convex hull=48, v0=18824) After retessellation of defect 18 (v0=18824), euler #=-56 (104957,312272,207259) : difference with theory (-60) = -4 CORRECTING DEFECT 19 (vertices=71, convex hull=143, v0=22862) After retessellation of defect 19 (v0=22862), euler #=-55 (105006,312476,207415) : difference with theory (-59) = -4 CORRECTING DEFECT 20 (vertices=166, convex hull=154, v0=28247) After retessellation of defect 20 (v0=28247), euler #=-54 (105066,312729,207609) : difference with theory (-58) = -4 CORRECTING DEFECT 21 (vertices=92, convex hull=48, v0=28766) After retessellation of defect 21 (v0=28766), euler #=-53 (105075,312777,207649) : difference with theory (-57) = -4 CORRECTING DEFECT 22 (vertices=200, convex hull=39, v0=29902) After retessellation of defect 22 (v0=29902), euler #=-52 (105100,312868,207716) : difference with theory (-56) = -4 CORRECTING DEFECT 23 (vertices=467, convex hull=73, v0=31619) After retessellation of defect 23 (v0=31619), euler #=-51 (105109,312926,207766) : difference with theory (-55) = -4 CORRECTING DEFECT 24 (vertices=30, convex hull=73, v0=35940) After retessellation of defect 24 (v0=35940), euler #=-50 (105118,312982,207814) : difference with theory (-54) = -4 CORRECTING DEFECT 25 (vertices=130, convex hull=123, v0=36994) After retessellation of defect 25 (v0=36994), euler #=-49 (105184,313240,208007) : difference with theory (-53) = -4 CORRECTING DEFECT 26 (vertices=206, convex hull=157, v0=40385) After retessellation of defect 26 (v0=40385), euler #=-48 (105247,313502,208207) : difference with theory (-52) = -4 CORRECTING DEFECT 27 (vertices=27, convex hull=27, v0=43907) After retessellation of defect 27 (v0=43907), euler #=-47 (105251,313525,208227) : difference with theory (-51) = -4 CORRECTING DEFECT 28 (vertices=41, convex hull=34, v0=47169) After retessellation of defect 28 (v0=47169), euler #=-46 (105255,313548,208247) : difference with theory (-50) = -4 CORRECTING DEFECT 29 (vertices=62, convex hull=78, v0=48091) After retessellation of defect 29 (v0=48091), euler #=-45 (105281,313658,208332) : difference with theory (-49) = -4 CORRECTING DEFECT 30 (vertices=151, convex hull=144, v0=48476) After retessellation of defect 30 (v0=48476), euler #=-44 (105309,313807,208454) : difference with theory (-48) = -4 CORRECTING DEFECT 31 (vertices=170, convex hull=135, v0=49054) After retessellation of defect 31 (v0=49054), euler #=-44 (105329,313934,208561) : difference with theory (-47) = -3 CORRECTING DEFECT 32 (vertices=1783, convex hull=701, v0=50415) An extra large defect has been detected... This often happens because cerebellum or dura has not been removed from wm.mgz. This may cause recon-all to run very slowly or crash. if so, see https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/TopologicalDefect_freeview After retessellation of defect 32 (v0=50415), euler #=-46 (105777,315687,209864) : difference with theory (-46) = 0 CORRECTING DEFECT 33 (vertices=51, convex hull=41, v0=58666) After retessellation of defect 33 (v0=58666), euler #=-45 (105777,315703,209881) : difference with theory (-45) = 0 CORRECTING DEFECT 34 (vertices=8, convex hull=20, v0=60702) After retessellation of defect 34 (v0=60702), euler #=-44 (105779,315715,209892) : difference with theory (-44) = 0 CORRECTING DEFECT 35 (vertices=6, convex hull=23, v0=62222) After retessellation of defect 35 (v0=62222), euler #=-43 (105779,315722,209900) : difference with theory (-43) = 0 CORRECTING DEFECT 36 (vertices=100, convex hull=39, v0=66553) After retessellation of defect 36 (v0=66553), euler #=-42 (105783,315751,209926) : difference with theory (-42) = 0 CORRECTING DEFECT 37 (vertices=115, convex hull=41, v0=71516) After retessellation of defect 37 (v0=71516), euler #=-41 (105793,315800,209966) : difference with theory (-41) = 0 CORRECTING DEFECT 38 (vertices=866, convex hull=228, v0=72051) After retessellation of defect 38 (v0=72051), euler #=-41 (105881,316181,210259) : difference with theory (-40) = 1 CORRECTING DEFECT 39 (vertices=62, convex hull=34, v0=73054) After retessellation of defect 39 (v0=73054), euler #=-40 (105893,316228,210295) : difference with theory (-39) = 1 CORRECTING DEFECT 40 (vertices=191, convex hull=186, v0=75330) After retessellation of defect 40 (v0=75330), euler #=-39 (105961,316534,210534) : difference with theory (-38) = 1 CORRECTING DEFECT 41 (vertices=27, convex hull=65, v0=76285) After retessellation of defect 41 (v0=76285), euler #=-38 (105975,316602,210589) : difference with theory (-37) = 1 CORRECTING DEFECT 42 (vertices=464, convex hull=198, v0=78490) After retessellation of defect 42 (v0=78490), euler #=-37 (106100,317067,210930) : difference with theory (-36) = 1 CORRECTING DEFECT 43 (vertices=37, convex hull=68, v0=79568) After retessellation of defect 43 (v0=79568), euler #=-36 (106122,317163,211005) : difference with theory (-35) = 1 CORRECTING DEFECT 44 (vertices=16, convex hull=17, v0=84319) After retessellation of defect 44 (v0=84319), euler #=-35 (106127,317182,211020) : difference with theory (-34) = 1 CORRECTING DEFECT 45 (vertices=9, convex hull=28, v0=84421) After retessellation of defect 45 (v0=84421), euler #=-34 (106132,317205,211039) : difference with theory (-33) = 1 CORRECTING DEFECT 46 (vertices=62, convex hull=65, v0=85209) After retessellation of defect 46 (v0=85209), euler #=-33 (106150,317286,211103) : difference with theory (-32) = 1 CORRECTING DEFECT 47 (vertices=56, convex hull=43, v0=85938) After retessellation of defect 47 (v0=85938), euler #=-32 (106158,317326,211136) : difference with theory (-31) = 1 CORRECTING DEFECT 48 (vertices=73, convex hull=39, v0=87584) After retessellation of defect 48 (v0=87584), euler #=-31 (106168,317369,211170) : difference with theory (-30) = 1 CORRECTING DEFECT 49 (vertices=96, convex hull=90, v0=88255) After retessellation of defect 49 (v0=88255), euler #=-30 (106185,317457,211242) : difference with theory (-29) = 1 CORRECTING DEFECT 50 (vertices=176, convex hull=132, v0=88360) After retessellation of defect 50 (v0=88360), euler #=-29 (106253,317720,211438) : difference with theory (-28) = 1 CORRECTING DEFECT 51 (vertices=228, convex hull=201, v0=89491) After retessellation of defect 51 (v0=89491), euler #=-27 (106360,318141,211754) : difference with theory (-27) = 0 CORRECTING DEFECT 52 (vertices=22, convex hull=19, v0=90028) After retessellation of defect 52 (v0=90028), euler #=-26 (106365,318161,211770) : difference with theory (-26) = 0 CORRECTING DEFECT 53 (vertices=63, convex hull=83, v0=92695) After retessellation of defect 53 (v0=92695), euler #=-25 (106397,318294,211872) : difference with theory (-25) = 0 CORRECTING DEFECT 54 (vertices=119, convex hull=97, v0=92944) After retessellation of defect 54 (v0=92944), euler #=-24 (106441,318471,212006) : difference with theory (-24) = 0 CORRECTING DEFECT 55 (vertices=343, convex hull=183, v0=93966) After retessellation of defect 55 (v0=93966), euler #=-23 (106491,318710,212196) : difference with theory (-23) = 0 CORRECTING DEFECT 56 (vertices=37, convex hull=96, v0=94012) After retessellation of defect 56 (v0=94012), euler #=-22 (106504,318787,212261) : difference with theory (-22) = 0 CORRECTING DEFECT 57 (vertices=56, convex hull=77, v0=94955) After retessellation of defect 57 (v0=94955), euler #=-21 (106539,318923,212363) : difference with theory (-21) = 0 CORRECTING DEFECT 58 (vertices=39, convex hull=81, v0=95409) After retessellation of defect 58 (v0=95409), euler #=-20 (106558,319011,212433) : difference with theory (-20) = 0 CORRECTING DEFECT 59 (vertices=19, convex hull=35, v0=95676) After retessellation of defect 59 (v0=95676), euler #=-19 (106563,319037,212455) : difference with theory (-19) = 0 CORRECTING DEFECT 60 (vertices=45, convex hull=33, v0=98815) After retessellation of defect 60 (v0=98815), euler #=-18 (106572,319074,212484) : difference with theory (-18) = 0 CORRECTING DEFECT 61 (vertices=21, convex hull=52, v0=99396) After retessellation of defect 61 (v0=99396), euler #=-17 (106581,319120,212522) : difference with theory (-17) = 0 CORRECTING DEFECT 62 (vertices=50, convex hull=78, v0=99501) After retessellation of defect 62 (v0=99501), euler #=-16 (106598,319204,212590) : difference with theory (-16) = 0 CORRECTING DEFECT 63 (vertices=285, convex hull=104, v0=99727) After retessellation of defect 63 (v0=99727), euler #=-15 (106654,319417,212748) : difference with theory (-15) = 0 CORRECTING DEFECT 64 (vertices=129, convex hull=85, v0=100617) After retessellation of defect 64 (v0=100617), euler #=-14 (106673,319514,212827) : difference with theory (-14) = 0 CORRECTING DEFECT 65 (vertices=154, convex hull=62, v0=101887) After retessellation of defect 65 (v0=101887), euler #=-13 (106688,319591,212890) : difference with theory (-13) = 0 CORRECTING DEFECT 66 (vertices=109, convex hull=119, v0=101960) After retessellation of defect 66 (v0=101960), euler #=-12 (106739,319795,213044) : difference with theory (-12) = 0 CORRECTING DEFECT 67 (vertices=180, convex hull=110, v0=104746) After retessellation of defect 67 (v0=104746), euler #=-11 (106767,319926,213148) : difference with theory (-11) = 0 CORRECTING DEFECT 68 (vertices=20, convex hull=64, v0=105621) After retessellation of defect 68 (v0=105621), euler #=-10 (106776,319977,213191) : difference with theory (-10) = 0 CORRECTING DEFECT 69 (vertices=150, convex hull=51, v0=106043) After retessellation of defect 69 (v0=106043), euler #=-9 (106801,320075,213265) : difference with theory (-9) = 0 CORRECTING DEFECT 70 (vertices=10, convex hull=24, v0=106948) After retessellation of defect 70 (v0=106948), euler #=-8 (106802,320085,213275) : difference with theory (-8) = 0 CORRECTING DEFECT 71 (vertices=19, convex hull=34, v0=107356) After retessellation of defect 71 (v0=107356), euler #=-7 (106804,320103,213292) : difference with theory (-7) = 0 CORRECTING DEFECT 72 (vertices=49, convex hull=70, v0=108587) After retessellation of defect 72 (v0=108587), euler #=-6 (106834,320223,213383) : difference with theory (-6) = 0 CORRECTING DEFECT 73 (vertices=15, convex hull=23, v0=108926) After retessellation of defect 73 (v0=108926), euler #=-5 (106837,320239,213397) : difference with theory (-5) = 0 CORRECTING DEFECT 74 (vertices=133, convex hull=97, v0=110281) After retessellation of defect 74 (v0=110281), euler #=-5 (106884,320442,213553) : difference with theory (-4) = 1 CORRECTING DEFECT 75 (vertices=141, convex hull=111, v0=110766) After retessellation of defect 75 (v0=110766), euler #=-4 (106893,320523,213626) : difference with theory (-3) = 1 CORRECTING DEFECT 76 (vertices=89, convex hull=98, v0=112010) After retessellation of defect 76 (v0=112010), euler #=-3 (106932,320684,213749) : difference with theory (-2) = 1 CORRECTING DEFECT 77 (vertices=55, convex hull=99, v0=113497) After retessellation of defect 77 (v0=113497), euler #=-1 (106953,320790,213836) : difference with theory (-1) = 0 CORRECTING DEFECT 78 (vertices=55, convex hull=74, v0=115221) After retessellation of defect 78 (v0=115221), euler #=0 (106978,320894,213916) : difference with theory (0) = 0 CORRECTING DEFECT 79 (vertices=15, convex hull=27, v0=115951) After retessellation of defect 79 (v0=115951), euler #=1 (106978,320902,213925) : difference with theory (1) = 0 CORRECTING DEFECT 80 (vertices=25, convex hull=62, v0=116331) After retessellation of defect 80 (v0=116331), euler #=2 (106982,320940,213960) : difference with theory (2) = 0 computing original vertex metric properties... storing new metric properties... computing tessellation statistics... vertex spacing 0.90 +- 0.29 (0.06-->13.42) (max @ vno 90769 --> 91329) face area -nan +- -nan (1000.00-->-1.00) performing soap bubble on retessellated vertices for 0 iterations... vertex spacing 0.90 +- 0.29 (0.06-->13.42) (max @ vno 90769 --> 91329) face area -nan +- -nan (1000.00-->-1.00) tessellation finished, orienting corrected surface... 281 mutations (34.7%), 528 crossovers (65.3%), 584 vertices were eliminated building final representation... 9420 vertices and 0 faces have been removed from triangulation after topology correction, eno=2 (nv=106982, nf=213960, ne=320940, g=0) writing corrected surface to /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf/lh.orig.premesh... 0.000 % of the vertices (0 vertices) exhibit an orientation change removing intersecting faces 000: 1076 intersecting 001: 119 intersecting 002: 11 intersecting 003: 9 intersecting terminating search with 0 intersecting topology fixing took 7.7 minutes FSRUNTIME@ mris_fix_topology lh 0.1287 hours 32 threads #VMPC# mris_fix_topology VmPeak 2880044 @#@FSTIME 2020:07:08:15:45:15 mris_fix_topology N 14 e 467.69 S 13.97 U 7332.85 P 1570% M 755400 F 287 R 906758 W 0 c 19968 w 1133225 I 17050 O 10251 L 7.62 3.15 1.92 @#@FSLOADPOST 2020:07:08:15:53:03 mris_fix_topology N 14 14.44 12.98 7.41 mris_euler_number ../surf/lh.orig.premesh euler # = v-e+f = 2g-2: 106982 - 320940 + 213960 = 2 --> 0 holes F =2V-4: 213960 = 213964-4 (0) 2E=3F: 641880 = 641880 (0) total defect index = 0 Wed Jul 8 15:53:09 CEST 2020 setenv SUBJECTS_DIR /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142 cd /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/scripts /work/bav5809/software/packages/freesurfer/bin/defect2seg --s SEVEREp142 --lh-only freesurfer-linux-centos6_x86_64-7.1.0-20200511-813297b defect2seg 7.1.0 Linux node012 4.14.185-1.0.27.el7.rrz.x86_64 #1 SMP Wed Jun 24 17:36:57 CEST 2020 x86_64 x86_64 x86_64 GNU/Linux pid 2944 mri_label2vol --defects /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf/lh.orig.nofix /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf/lh.defect_labels /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/orig.mgz 1000 0 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/lh.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 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri/lh.surface.defects.mgz mris_defects_pointset -s /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf/lh.orig.nofix -d /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf/lh.defect_labels -o /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf/lh.defects.pointset Reading in surface /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf/lh.orig.nofix Reading in defect segmentation /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf/lh.defect_labels #VMPC# mris_defects_pointset 2621712 mris_defects_pointset done Started at Wed Jul 8 15:53:09 CEST 2020 Ended at Wed Jul 8 15:53:20 CEST 2020 Defect2seg-Run-Time-Sec 11 Defect2seg-Run-Time-Min 0.22 Defect2seg-Run-Time-Hours 0.00 tkmeditfv SEVEREp142 orig.mgz -defect -c /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf/?h.defects.pointset defect2seg Done @#@FSTIME 2020:07:08:15:53:09 defect2seg N 3 e 11.08 S 0.64 U 6.62 P 65% M 218084 F 400 R 149407 W 0 c 115 w 3457 I 30107 O 215 L 13.36 12.78 7.37 @#@FSLOADPOST 2020:07:08:15:53:20 defect2seg N 3 10.47 12.17 7.26 mris_remesh --remesh --iters 3 --input /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf/lh.orig.premesh --output /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf/lh.orig iters = 3 standard remeshing without target adjusted l: 0.717464 remeshing to edge length 0.717464 with 3 iterations avg qual before : 0.882923 after: 0.971364 Removing intersections removing intersecting faces 000: 18 intersecting terminating search with 0 intersecting Remeshed surface quality stats nv0 = 106982 nv = 109909 1.02736 Area 219814 0.30778 0.03439 0.001379 0.4675 Corner 659442 60.00000 8.77799 0.538138 176.7207 Edge 329721 0.85103 0.08306 0.033059 1.2836 Hinge 329721 11.16480 11.44173 0.000011 179.5957 mris_remesh done @#@FSTIME 2020:07:08:15:53:20 mris_remesh N 7 e 33.00 S 1.35 U 36.99 P 116% M 704184 F 188 R 580378 W 0 c 361 w 17882 I 7541 O 7729 L 10.47 12.17 7.26 @#@FSLOADPOST 2020:07:08:15:53:53 mris_remesh N 7 6.74 11.10 7.06 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/scripts mris_remove_intersection ../surf/lh.orig ../surf/lh.orig intersection removal took 0.00 hours writing corrected surface to ../surf/lh.orig @#@FSTIME 2020:07:08:15:53:55 mris_remove_intersection N 2 e 3.94 S 0.23 U 3.82 P 102% M 277536 F 144 R 134504 W 0 c 37 w 1041 I 7746 O 7729 L 6.20 10.91 7.02 @#@FSLOADPOST 2020:07:08:15:53:58 mris_remove_intersection N 2 6.20 10.91 7.02 rm ../surf/lh.inflated rm: cannot remove '../surf/lh.inflated': No such file or directory #-------------------------------------------- #@# AutoDetGWStats lh Wed Jul 8 15:54:00 CEST 2020 cd /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri mris_autodet_gwstats --o ../surf/autodet.gw.stats.lh.dat --i brain.finalsurfs.mgz --wm wm.mgz --surf ../surf/lh.orig.premesh 7.1.0 cd /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri setenv SUBJECTS_DIR /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142 mris_autodet_gwstats --o ../surf/autodet.gw.stats.lh.dat --i brain.finalsurfs.mgz --wm wm.mgz --surf ../surf/lh.orig.premesh border white: 217860 voxels (1.30%) border gray 254869 voxels (1.52%) 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=22378, wmmin=5, clip=110 Binarizing thresholding at 5 computing class statistics... low=30, hi=110.000000 CCS WM (92.0): 92.7 +- 8.0 [70.0 --> 110.0] CCS GM (72.0) : 70.7 +- 11.6 [30.0 --> 110.0] white_mean = 92.6638 +/- 7.97722, gray_mean = 70.6611 +/- 11.6446 using class modes intead of means, discounting robust sigmas.... MRIScomputeClassModes(): min=0 max=229 nbins=230 intensity peaks found at WM=95+-6.1, GM=65+-12.2 white_mode = 95, gray_mode = 65 std_scale = 1 Applying sanity checks, max_scale_down = 0.2 setting MIN_GRAY_AT_WHITE_BORDER to 53.4 (was 70.000000) setting MAX_BORDER_WHITE to 103.0 (was 105.000000) setting MIN_BORDER_WHITE to 65.0 (was 85.000000) setting MAX_CSF to 41.7 (was 40.000000) setting MAX_GRAY to 87.0 (was 95.000000) setting MAX_GRAY_AT_CSF_BORDER to 53.4 (was 75.000000) setting MIN_GRAY_AT_CSF_BORDER to 30.1 (was 40.000000) When placing the white surface white_border_hi = 102.977; white_border_low = 65; white_outside_low = 53.3554; white_inside_hi = 120; white_outside_hi = 102.977; When placing the pial surface pial_border_hi = 53.3554; pial_border_low = 30.0662; pial_outside_low = 10; pial_inside_hi = 87.0228; pial_outside_hi = 47.5331; #VMPC# mris_autodet_gwstats VmPeak 2390820 mris_autodet_gwstats done @#@FSTIME 2020:07:08:15:54:01 mris_autodet_gwstats N 8 e 6.98 S 0.51 U 7.79 P 119% M 204956 F 187 R 112536 W 0 c 65 w 1689 I 10836 O 1 L 5.70 10.73 6.98 @#@FSLOADPOST 2020:07:08:15:54:07 mris_autodet_gwstats N 8 5.32 10.57 6.95 #-------------------------------------------- #@# WhitePreAparc lh Wed Jul 8 15:54:08 CEST 2020 cd /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri mris_place_surface --adgws-in ../surf/autodet.gw.stats.lh.dat --wm wm.mgz --invol brain.finalsurfs.mgz --lh --i ../surf/lh.orig --o ../surf/lh.white.preaparc --white --seg aseg.presurf.mgz --nsmooth 5 7.1.0 7.1.0 cd /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri setenv SUBJECTS_DIR /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142 mris_place_surface --adgws-in ../surf/autodet.gw.stats.lh.dat --wm wm.mgz --invol brain.finalsurfs.mgz --lh --i ../surf/lh.orig --o ../surf/lh.white.preaparc --white --seg aseg.presurf.mgz --nsmooth 5 Reading in input surface ../surf/lh.orig Smoothing surface with 5 iterations removing intersecting faces 000: 10 intersecting step 1 with no progress (num=17, old_num=10) 001: 17 intersecting 002: 16 intersecting step 1 with no progress (num=16, old_num=16) 003: 16 intersecting step 2 with no progress (num=22, old_num=16) 004: 22 intersecting step 3 with no progress (num=25, old_num=22) 005: 25 intersecting 006: 15 intersecting step 1 with no progress (num=18, old_num=15) 007: 18 intersecting 008: 15 intersecting step 1 with no progress (num=15, old_num=15) 009: 15 intersecting step 2 with no progress (num=15, old_num=15) 010: 15 intersecting step 3 with no progress (num=15, old_num=15) 011: 15 intersecting step 4 with no progress (num=22, old_num=15) 012: 22 intersecting 013: 19 intersecting step 1 with no progress (num=21, old_num=19) 014: 21 intersecting 015: 12 intersecting step 1 with no progress (num=12, old_num=12) 016: 12 intersecting step 2 with no progress (num=12, old_num=12) 017: 12 intersecting step 3 with no progress (num=12, old_num=12) 018: 12 intersecting step 4 with no progress (num=12, old_num=12) 019: 12 intersecting step 5 with no progress (num=12, old_num=12) 020: 12 intersecting step 6 with no progress (num=12, old_num=12) 021: 12 intersecting step 7 with no progress (num=12, old_num=12) 022: 12 intersecting step 8 with no progress (num=12, old_num=12) 023: 12 intersecting step 9 with no progress (num=12, old_num=12) 024: 12 intersecting step 10 with no progress (num=12, old_num=12) 025: 12 intersecting step 11 with no progress (num=12, old_num=12) 026: 12 intersecting step 12 with no progress (num=12, old_num=12) 027: 12 intersecting step 13 with no progress (num=12, old_num=12) 028: 12 intersecting step 14 with no progress (num=12, old_num=12) 029: 12 intersecting step 15 with no progress (num=12, old_num=12) 030: 12 intersecting step 16 with no progress (num=12, old_num=12) terminating search with 10 intersecting Area 219814 0.26690 0.06802 0.000006 0.5563 Corner 659442 60.00000 9.55007 0.027493 179.9081 Edge 329721 0.78734 0.12332 0.001847 1.3471 Hinge 329721 7.22913 7.44218 0.000030 179.3949 Not reading in aparc Reading in input volume brain.finalsurfs.mgz Reading in wm volume wm.mgz MRIclipBrightWM(): nthresh=22378, wmmin=5, clip=110 MRIfindBrightNonWM(): 751 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 #FML# MRISripMidline(): nmarked=6642, nmarked2=1, nripped=6642 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 MRISripSegs(): -2 2 0.5 ripped 0 vertex 54955: xyz = (-45.3255,-17.5992,31.8447) oxyz = (-45.3255,-17.5992,31.8447) wxzy = (-45.3255,-17.5992,31.8447) pxyz = (0,0,0) 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=6642 #FML# MRISripMidline(): nmarked=6642, nmarked2=1, nripped=6642 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 = 102.9772190; border_low = 65.0000000; outside_low = 53.3553960; outside_hi = 102.9772190; sigma = 2; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 BorderValsHiRes=0 nvertices=109909 Gdiag_no=-1 vno start=0, stop=109909 Replacing 255s with 0s #SI# sigma=2 had to be increased for 596 vertices, nripped=6642 mean border=77.7, 464 (464) missing vertices, mean dist 0.3 [0.9 (%31.7)->0.9 (%68.3))] %45 local maxima, %49 large gradients and % 0 min vals, 0 gradients ignored MRIScomputeBorderValues_new() finished in 0.0150 min Finding expansion regions mean absolute distance = 0.90 +- 1.09 4025 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=node0, 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=2245421.8, rms=10.158 001: dt: 0.5000, sse=1331027.9, rms=7.675 (24.444%) 002: dt: 0.5000, sse=865710.2, rms=5.994 (21.902%) 003: dt: 0.5000, sse=621852.9, rms=4.874 (18.686%) 004: dt: 0.5000, sse=500104.3, rms=4.163 (14.591%) 005: dt: 0.5000, sse=423709.7, rms=3.752 (9.872%) 006: dt: 0.5000, sse=386835.4, rms=3.520 (6.180%) rms = 3.3934/3.5200, sse=389664.0/386835.4, time step reduction 1 of 3 to 0.250 0 1 0 007: dt: 0.5000, sse=389664.0, rms=3.393 (3.599%) 008: dt: 0.2500, sse=249074.2, rms=2.350 (30.751%) 009: dt: 0.2500, sse=228607.2, rms=2.099 (10.678%) rms = 2.0219/2.0989, sse=234551.1/228607.2, time step reduction 2 of 3 to 0.125 0 1 0 010: dt: 0.2500, sse=234551.1, rms=2.022 (3.671%) 011: dt: 0.1250, sse=215983.4, rms=1.965 (2.826%) rms = 1.9453/1.9648, sse=216664.8/215983.4, time step reduction 3 of 3 to 0.062 0 1 1 012: dt: 0.1250, sse=216664.8, rms=1.945 (0.988%) maximum number of reductions reached, breaking from loop positioning took 1.2 minutes Iteration 1 ========================================= n_averages=2, current_sigma=1 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6642 removing 4 vertices from ripped group in thread:0 removing 4 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6788, nmarked2=6, nripped=6788 Ripping WMSA Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 247 247 MRISripSegs(): -2 2 0.5 ripped 0 Computing target border values Entering MRIScomputeBorderValues_new(): inside_hi = 120.0000000; border_hi = 102.9772190; border_low = 65.0000000; outside_low = 53.3553960; outside_hi = 102.9772190; sigma = 1; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 BorderValsHiRes=0 nvertices=109909 Gdiag_no=-1 vno start=0, stop=109909 Replacing 255s with 0s #SI# sigma=1 had to be increased for 413 vertices, nripped=6788 mean border=79.4, 451 (223) missing vertices, mean dist -0.2 [0.6 (%58.5)->0.3 (%41.5))] %48 local maxima, %46 large gradients and % 0 min vals, 0 gradients ignored MRIScomputeBorderValues_new() finished in 0.0105 min Finding expansion regions mean absolute distance = 0.46 +- 0.85 4550 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=node0, 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=459856.8, rms=3.682 013: dt: 0.5000, sse=340480.2, rms=2.721 (26.099%) 014: dt: 0.5000, sse=334710.8, rms=2.640 (2.974%) rms = 2.7516/2.6400, sse=341571.0/334710.8, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 015: dt: 0.2500, sse=273078.9, rms=2.076 (21.374%) 016: dt: 0.2500, sse=256014.7, rms=1.806 (13.010%) 017: dt: 0.2500, sse=248176.1, rms=1.739 (3.697%) rms = 1.7061/1.7389, sse=255117.1/248176.1, time step reduction 2 of 3 to 0.125 0 1 1 018: dt: 0.2500, sse=255117.0, rms=1.706 (1.886%) rms = 1.6680/1.7061, sse=260884.9/255117.1, time step reduction 3 of 3 to 0.062 0 1 1 019: dt: 0.1250, sse=260884.9, rms=1.668 (2.236%) maximum number of reductions reached, breaking from loop positioning took 0.8 minutes Iteration 2 ========================================= n_averages=1, current_sigma=0.5 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6788 removing 2 vertices from ripped group in thread:0 removing 3 vertices from ripped group in thread:0 #FML# MRISripMidline(): nmarked=6870, nmarked2=7, nripped=6870 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 = 102.9772190; border_low = 65.0000000; outside_low = 53.3553960; outside_hi = 102.9772190; sigma = 0.5; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 BorderValsHiRes=0 nvertices=109909 Gdiag_no=-1 vno start=0, stop=109909 Replacing 255s with 0s #SI# sigma=0.5 had to be increased for 404 vertices, nripped=6870 mean border=81.1, 422 (154) missing vertices, mean dist -0.2 [0.6 (%57.5)->0.3 (%42.5))] %59 local maxima, %35 large gradients and % 0 min vals, 0 gradients ignored MRIScomputeBorderValues_new() finished in 0.0059 min Finding expansion regions mean absolute distance = 0.44 +- 0.66 4720 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=node0, 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=406360.3, rms=3.134 020: dt: 0.5000, sse=316797.3, rms=2.334 (25.529%) rms = 2.3626/2.3342, sse=295961.1/316797.3, time step reduction 1 of 3 to 0.250 0 0 1 RMS increased, rejecting step 021: dt: 0.2500, sse=263673.2, rms=1.917 (17.887%) 022: dt: 0.2500, sse=247054.4, rms=1.663 (13.219%) 023: dt: 0.2500, sse=239335.0, rms=1.587 (4.590%) rms = 1.5581/1.5870, sse=235481.9/239335.0, time step reduction 2 of 3 to 0.125 0 0 1 024: dt: 0.2500, sse=235482.0, rms=1.558 (1.817%) rms = 1.5197/1.5581, sse=243300.2/235481.9, time step reduction 3 of 3 to 0.062 0 1 1 025: dt: 0.1250, sse=243300.2, rms=1.520 (2.465%) maximum number of reductions reached, breaking from loop positioning took 0.7 minutes Iteration 3 ========================================= n_averages=0, current_sigma=0.25 Freezing midline and others Ripping frozen voxels INFO: rip surface needed but not specified, so using input surface Freezing midline and others Entering: MRISripMidline(): inhibiting deformation at non-cortical midline structures... which=1, fix_mtl=0, using annot = 0 #FML0# MRISripMidline(): nripped=6870 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=6918, nmarked2=8, nripped=6918 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 = 102.9772190; border_low = 65.0000000; outside_low = 53.3553960; outside_hi = 102.9772190; sigma = 0.25; max_thickness = 10; step_size=0.5; STEP_SIZE=0.1; which = 1 thresh = 0 flags = 0 BorderValsHiRes=0 nvertices=109909 Gdiag_no=-1 vno start=0, stop=109909 Replacing 255s with 0s #SI# sigma=0.25 had to be increased for 361 vertices, nripped=6918 mean border=82.2, 482 (113) missing vertices, mean dist -0.1 [0.5 (%54.1)->0.3 (%45.9))] %68 local maxima, %25 large gradients and % 0 min vals, 0 gradients ignored MRIScomputeBorderValues_new() finished in 0.0040 min Finding expansion regions mean absolute distance = 0.41 +- 0.54 3585 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=node0, 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=302049.7, rms=2.281 026: dt: 0.5000, sse=272376.6, rms=1.844 (19.171%) rms = 2.1067/1.8440, sse=289581.3/272376.6, time step reduction 1 of 3 to 0.250 0 1 1 RMS increased, rejecting step 027: dt: 0.2500, sse=237997.2, rms=1.420 (23.004%) 028: dt: 0.2500, sse=233788.7, rms=1.283 (9.606%) 029: dt: 0.2500, sse=228007.5, rms=1.226 (4.476%) rms = 1.2364/1.2260, sse=210571.0/228007.5, time step reduction 2 of 3 to 0.125 0 0 1 RMS increased, rejecting step rms = 1.2115/1.2260, sse=208150.4/228007.5, time step reduction 3 of 3 to 0.062 0 0 1 030: dt: 0.1250, sse=208150.4, rms=1.212 (1.178%) maximum number of reductions reached, breaking from loop positioning took 0.7 minutes Writing output to ../surf/lh.white.preaparc #ET# mris_place_surface 3.46 minutes #VMPC# mris_make_surfaces VmPeak 7725828 mris_place_surface done @#@FSTIME 2020:07:08:15:54:08 mris_place_surface N 16 e 256.96 S 7.47 U 2580.95 P 1007% M 2065716 F 281 R 4417051 W 0 c 4684 w 41839 I 11684 O 7729 L 5.32 10.57 6.95 @#@FSLOADPOST 2020:07:08:15:58:25 mris_place_surface N 16 13.00 11.07 7.92 #-------------------------------------------- #@# CortexLabel lh Wed Jul 8 15:58:25 CEST 2020 cd /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/mri mri_label2label --label-cortex ../surf/lh.white.preaparc aseg.presurf.mgz 0 ../label/lh.cortex.label Generating cortex label... RemoveHipAmgy=0 NucAccIsMedialWall=0 10 non-cortical segments detected only using segment with 7217 vertices erasing segment 1 (vno[0] = 66281) erasing segment 2 (vno[0] = 66465) erasing segment 3 (vno[0] = 69876) erasing segment 4 (vno[0] = 72494) erasing segment 5 (vno[0] = 73018) erasing segment 6 (vno[0] = 73029) erasing segment 7 (vno[0] = 77013) erasing segment 8 (vno[0] = 93611) erasing segment 9 (vno[0] = 98791) @#@FSTIME 2020:07:08:15:58:25 mri_label2label N 5 e 18.65 S 0.26 U 19.87 P 107% M 305328 F 189 R 155287 W 0 c 42 w 1209 I 8386 O 8973 L 11.96 10.89 7.88 @#@FSLOADPOST 2020:07:08:15:58:44 mri_label2label N 5 9.69 10.44 7.78 #-------------------------------------------- #@# CortexLabel+HipAmyg lh Wed Jul 8 15:58:44 CEST 2020 cd /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/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 12 non-cortical segments detected only using segment with 6001 vertices erasing segment 1 (vno[0] = 48853) erasing segment 2 (vno[0] = 53848) erasing segment 3 (vno[0] = 66281) erasing segment 4 (vno[0] = 66465) erasing segment 5 (vno[0] = 69876) erasing segment 6 (vno[0] = 72494) erasing segment 7 (vno[0] = 73018) erasing segment 8 (vno[0] = 73029) erasing segment 9 (vno[0] = 77013) erasing segment 10 (vno[0] = 93611) erasing segment 11 (vno[0] = 103273) @#@FSTIME 2020:07:08:15:58:44 mri_label2label N 5 e 16.53 S 0.23 U 19.96 P 122% M 312172 F 186 R 157091 W 0 c 25 w 1210 I 8386 O 9060 L 9.69 10.44 7.78 @#@FSLOADPOST 2020:07:08:15:59:01 mri_label2label N 5 7.22 9.82 7.63 #-------------------------------------------- #@# Smooth2 lh Wed Jul 8 15:59:01 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/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 2020:07:08:15:59:01 mris_smooth N 7 e 4.23 S 0.35 U 5.78 P 145% M 172780 F 164 R 78378 W 0 c 21 w 1262 I 7749 O 7730 L 7.22 9.82 7.63 @#@FSLOADPOST 2020:07:08:15:59:05 mris_smooth N 7 7.22 9.82 7.63 #-------------------------------------------- #@# Inflation2 lh Wed Jul 8 15:59:05 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/scripts mris_inflate ../surf/lh.smoothwm ../surf/lh.inflated Reading ../surf/lh.smoothwm avg radius = 45.7 mm, total surface area = 68527 mm^2 step 000: RMS=0.187 (target=0.015) step 005: RMS=0.127 (target=0.015) step 010: RMS=0.100 (target=0.015) step 015: RMS=0.084 (target=0.015) step 020: RMS=0.071 (target=0.015) step 025: RMS=0.061 (target=0.015) step 030: RMS=0.052 (target=0.015) step 035: RMS=0.046 (target=0.015) step 040: RMS=0.041 (target=0.015) step 045: RMS=0.037 (target=0.015) step 050: RMS=0.033 (target=0.015) step 055: RMS=0.032 (target=0.015) step 060: RMS=0.030 (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 74.534012 mris_inflate stimesec 2.522472 mris_inflate ru_maxrss 262860 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 138066 mris_inflate ru_majflt 201 mris_inflate ru_nswap 0 mris_inflate ru_inblock 7751 mris_inflate ru_oublock 8590 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 20366 mris_inflate ru_nivcsw 326 @#@FSTIME 2020:07:08:15:59:05 mris_inflate N 2 e 12.41 S 2.54 U 74.53 P 620% M 265884 F 201 R 138081 W 0 c 326 w 20368 I 7751 O 8590 L 6.64 9.66 7.59 @#@FSLOADPOST 2020:07:08:15:59:18 mris_inflate N 2 5.77 9.38 7.52 #-------------------------------------------- #@# Curv .H and .K lh Wed Jul 8 15:59:18 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/surf mris_curvature -w -seed 1234 lh.white.preaparc setting seed for random number generator to 1234 total integrated curvature = 54.641*4pi (686.633) --> -54 handles ICI = 249.6, FI = 1416.8, variation=25733.392 writing Gaussian curvature to ./lh.white.preaparc.K...done. writing mean curvature to ./lh.white.preaparc.H...done. @#@FSTIME 2020:07:08:15:59:19 mris_curvature N 4 e 3.80 S 0.10 U 3.13 P 85% M 127340 F 174 R 37792 W 0 c 25 w 1090 I 7749 O 1717 L 5.77 9.38 7.52 @#@FSLOADPOST 2020:07:08:15:59:23 mris_curvature N 4 5.39 9.24 7.49 rm -f lh.white.H ln -s lh.white.preaparc.H lh.white.H rm -f lh.white.K ln -s lh.white.preaparc.K lh.white.K mris_curvature -seed 1234 -thresh .999 -n -a 5 -w -distances 10 10 lh.inflated setting seed for random number generator to 1234 normalizing curvature values. averaging curvature patterns 5 times. sampling 10 neighbors out to a distance of 10 mm 125 vertices thresholded to be in k1 ~ [-0.74 1.35], k2 ~ [-0.45 0.24] total integrated curvature = 0.127*4pi (1.602) --> 1 handles ICI = 1.1, FI = 8.3, variation=139.558 108 vertices thresholded to be in [-0.15 0.10] writing Gaussian curvature to ./lh.inflated.K...thresholding curvature at 99.90% level curvature mean = 0.000, std = 0.004 78 vertices thresholded to be in [-0.36 0.36] done. writing mean curvature to ./lh.inflated.H...curvature mean = -0.018, std = 0.027 done. @#@FSTIME 2020:07:08:15:59:23 mris_curvature N 12 e 52.56 S 0.57 U 54.46 P 104% M 301228 F 184 R 252916 W 0 c 75 w 1308 I 7752 O 1717 L 5.39 9.24 7.49 @#@FSLOADPOST 2020:07:08:16:00:16 mris_curvature N 12 2.88 7.89 7.13 #-------------------------------------------- #@# Sphere lh Wed Jul 8 16:00:16 CEST 2020 /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/scripts mris_sphere -rusage /work/bav5809/subjects/freesurfer_output/T1/lh/SEVEREp142/SEVEREp142/touch/rusage.mris_sphere.lh.dat -seed 1234 ../surf/lh.inflated ../surf/lh.sphere setting seed for random number genererator to 1234 7.1.0 7.1.0 == Number of threads available to mris_sphere for OpenMP = 32 == reading original vertex positions... scaling brain by 0.375... projecting onto sphere... surface projected - minimizing metric distortion... MRISunfold() max_passes = 1 ------- tol=5.0e-01, sigma=0.0, host=node0, nav=1024, nbrs=2, l_area=1.000, l_dist=1.000 using quadratic fit line minimization 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 1.000000 desired_rms_height -1.000000 momentum 0.900000 nbhd_size 7 max_nbrs 8 niterations 25 nsurfaces 0 SURFACES 3 flags 0 (0) use curv 0 no sulc 0 no rigid align 0 mris->nsize 2 mris->hemisphere 0 randomSeed 1234 singular matrix in quadratic form @#@FSTIME 2020:07:08:16:00:16 mris_sphere N 6 e 422.03 S 42.72 U 3583.99 P 859% M 4300456 F 268 R 2039263 W 0 c 12549 w 857922 I 10360 O 8591749 L 2.88 7.89 7.13 @#@FSLOADPOST 2020:07:08:16:07:18 mris_sphere N 6 29.98 19.18 12.04 Linux node012 4.14.185-1.0.27.el7.rrz.x86_64 #1 SMP Wed Jun 24 17:36:57 CEST 2020 x86_64 x86_64 x86_64 GNU/Linux recon-all -s SEVEREp142 exited with ERRORS at Wed Jul 8 16:07:19 CEST 2020 To report a problem, see http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting