Freesurfer

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来自Mengfan Han的邮件
by Mengfan Han 30 Mar '26

30 Mar '26

External Email - Use Caution confirm 966cbfbf7fb28d5af1cce330d59bb5e743988cb9

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Re: Updates/bug fixes available for the 8.2.0 release
by fsbuild 28 Mar '26

28 Mar '26

External Email - Use Caution As of 3/28/26 here has been an update to a previously patched file, so please run the update script again; nothing should change if everything is up to date. - R. On 3/25/26 17:55, fsbuild (fsbuild(a)contbay.com) wrote:         External Email - Use Caution         Some updates/bug fixes are available for an existing installation of a Freesurfer 8.2.0 release.   These include fixes for recon-all expert options and an update on Ubuntu24 only for gems bindings.  Please see the instructions at, https://secure-web.cisco.com/1RK92XfeESPTdWFVUxu3bUMk_yQM1ETYtcsEv3yVJEDOYV…

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Issues with 'recon-all' in FreeSurfer 7.4.1 When Processing 7T MRI Data
by HanLai 26 Mar '26

26 Mar '26

External Email - Use Caution Hello developpers, When applying FreeSurfer 7.4.1 to analyze 7T structural MRI scans, I encountered following errors. Please see the recon-all file for details. Could you help me identify the issue and suggest how to resolve it? I look forward to your response. cd /home/laihan/Desktop/7T_fs2025/LH2024_173 setenv SUBJECTS_DIR /home/laihan/Desktop/7T_fs2025 /usr/local/freesurfer/bin/recon-all -s LH2024_173 -all subjid LH2024_173 setenv SUBJECTS_DIR /home/laihan/Desktop/7T_fs2025 FREESURFER_HOME /usr/local/freesurfer Actual FREESURFER_HOME /usr/local/freesurfer build-stamp.txt: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 Linux laihan-VirtualBox 5.15.0-139-generic #149~20.04.1-Ubuntu SMP Wed Apr 16 08:29:56 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux cputime unlimited filesize unlimited datasize unlimited stacksize 8192 kbytes coredumpsize 0 kbytes memoryuse unlimited vmemoryuse unlimited descriptors 1024 memorylocked 65536 kbytes maxproc 93642 maxlocks unlimited maxsignal 93642 maxmessage 819200 maxnice 0 maxrtprio 0 maxrttime unlimited total used free shared buff/cache available Mem: 22Gi 1.3Gi 16Gi 11Mi 4.7Gi 21Gi Swap: 2.0Gi 0B 2.0Gi ######################################## program versions used 7.4.1 (freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460) 7.4.1 ProgramName: lta_convert ProgramArguments: lta_convert -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_and ProgramArguments: mri_and -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_annotation2label ProgramArguments: mri_annotation2label -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_aparc2aseg ProgramArguments: mri_aparc2aseg -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_surf2volseg ProgramArguments: mri_surf2volseg -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_binarize ProgramArguments: mri_binarize -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_ca_label ProgramArguments: mri_ca_label -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_ca_normalize ProgramArguments: mri_ca_normalize -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_ca_register ProgramArguments: mri_ca_register -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_cc ProgramArguments: mri_cc -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_compute_overlap ProgramArguments: mri_compute_overlap -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_compute_seg_overlap ProgramArguments: mri_compute_seg_overlap -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_concat ProgramArguments: mri_concat -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_concatenate_lta ProgramArguments: mri_concatenate_lta -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 mri_convert -all-info ProgramName: mri_convert ProgramArguments: mri_convert -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_diff ProgramArguments: mri_diff -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_edit_wm_with_aseg ProgramArguments: mri_edit_wm_with_aseg -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_em_register ProgramArguments: mri_em_register -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_fill ProgramArguments: mri_fill -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_fuse_segmentations ProgramArguments: mri_fuse_segmentations -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_fwhm ProgramArguments: mri_fwhm -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_gcut ProgramArguments: mri_gcut -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_info ProgramArguments: mri_info -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_label2label ProgramArguments: mri_label2label -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_label2vol ProgramArguments: mri_label2vol -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_log_likelihood ProgramArguments: mri_log_likelihood -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_mask ProgramArguments: mri_mask -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_matrix_multiply ProgramArguments: mri_matrix_multiply -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:38-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_normalize ProgramArguments: mri_normalize -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_normalize_tp2 ProgramArguments: mri_normalize_tp2 -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_or ProgramArguments: mri_or -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_relabel_hypointensities ProgramArguments: mri_relabel_hypointensities -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_relabel_nonwm_hypos ProgramArguments: mri_relabel_nonwm_hypos -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_remove_neck ProgramArguments: mri_remove_neck -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 7.4.1 ProgramName: mri_robust_register ProgramArguments: mri_robust_register -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 7.4.1 ProgramName: mri_robust_template ProgramArguments: mri_robust_template -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_anatomical_stats ProgramArguments: mris_anatomical_stats -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_ca_label ProgramArguments: mris_ca_label -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_calc ProgramArguments: mris_calc -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_convert ProgramArguments: mris_convert -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_curvature ProgramArguments: mris_curvature -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_curvature_stats ProgramArguments: mris_curvature_stats -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_diff ProgramArguments: mris_diff -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_divide_parcellation ProgramArguments: mris_divide_parcellation -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_segment ProgramArguments: mri_segment -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_segstats ProgramArguments: mri_segstats -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_euler_number ProgramArguments: mris_euler_number -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_fix_topology ProgramArguments: mris_fix_topology -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_topo_fixer ProgramArguments: mris_topo_fixer -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_jacobian ProgramArguments: mris_jacobian -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_label2annot ProgramArguments: mris_label2annot -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_left_right_register ProgramArguments: mris_left_right_register -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_place_surface ProgramArguments: mris_place_surface -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mrisp_paint ProgramArguments: mrisp_paint -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_register ProgramArguments: mris_register -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_smooth ProgramArguments: mris_smooth -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_sphere ProgramArguments: mris_sphere -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_surface_stats ProgramArguments: mris_surface_stats -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_stats2seg ProgramArguments: mri_stats2seg -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_thickness ProgramArguments: mris_thickness -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_thickness_diff ProgramArguments: mris_thickness_diff -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_topo_fixer ProgramArguments: mris_topo_fixer -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_surf2surf ProgramArguments: mri_surf2surf -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_surf2vol ProgramArguments: mri_surf2vol -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_surfcluster ProgramArguments: mri_surfcluster -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mris_volmask ProgramArguments: mris_volmask -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_tessellate ProgramArguments: mri_tessellate -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_vol2surf ProgramArguments: mri_vol2surf -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_vol2vol ProgramArguments: mri_vol2vol -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_voldiff ProgramArguments: mri_voldiff -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: mri_watershed ProgramArguments: mri_watershed -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 ProgramName: tkregister2 ProgramArguments: tkregister2_cmdl -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 mri_motion_correct.fsl 7.4.1 mri_convert -all-info ProgramName: mri_convert ProgramArguments: mri_convert -all-info ProgramVersion: 7.4.1 TimeStamp: 2026/03/26-07:18:39-GMT BuildTime: Jun 14 2023 04:06:48 BuildStamp: freesurfer-linux-ubuntu20_x86_64-7.4.1-20230614-7eb8460 User: laihan Machine: laihan-VirtualBox Platform: Linux PlatformVersion: 5.15.0-139-generic CompilerName: GCC CompilerVersion: 8.4.0 Program nu_correct, built from: Package MNI N3, version 1.12.0, compiled by nicks@terrier (x86_64-unknown-linux-gnu) on 2015-06-19 at 01:25:34 ####################################### GCADIR /usr/local/freesurfer/average GCA RB_all_2020-01-02.gca GCASkull RB_all_withskull_2020_01_02.gca AvgCurvTif folding.atlas.acfb40.noaparc.i12.2016-08-02.tif GCSDIR /usr/local/freesurfer/average GCS DKaparc.atlas.acfb40.noaparc.i12.2016-08-02.gcs ####################################### #-------------------------------------------- #@# MotionCor 2026年 03月 26日星期四 15:18:40 CST Found 1 runs /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/orig/001.mgz Checking for (invalid) multi-frame inputs... Only one run found so motion correction will not be performed. I'll copy the run to rawavg and continue. cp /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/orig/001.mgz /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/rawavg.mgz mri_info /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/rawavg.mgz rawavg.mgz ======================================== Volume information for /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/rawavg.mgz type: MGH dimensions: 240 x 300 x 320 voxel sizes: 0.650000, 0.650000, 0.650000 type: SHORT (4) fov: 208.000 dof: 1 xstart: -78.0, xend: 78.0 ystart: -97.5, yend: 97.5 zstart: -104.0, zend: 104.0 TR: 4300.00 msec, TE: 0.00 msec, TI: 0.00 msec, flip angle: 0.00 degrees nframes: 1 PhEncDir: UNKNOWN FieldStrength: 0.000000 ras xform present xform info: x_r = 0.9991, y_r = -0.0324, z_r = 0.0269, c_r = 0.1569 : x_a = 0.0395, y_a = 0.9424, z_a = -0.3322, c_a = 5.8322 : x_s = -0.0146, y_s = 0.3329, z_s = 0.9428, c_s = -3.0108 talairach xfm : Orientation : RAS Primary Slice Direction: axial voxel to ras transform: 0.6494 -0.0211 0.0175 -77.4074 0.0257 0.6126 -0.2159 -54.5861 -0.0095 0.2164 0.6128 -132.3927 0.0000 0.0000 0.0000 1.0000 voxel-to-ras determinant 0.274625 ras to voxel transform: 1.5371 0.0608 -0.0224 119.3368 -0.0499 1.4498 0.5122 143.0944 0.0413 -0.5110 1.4505 167.3413 0.0000 0.0000 0.0000 1.0000 /home/laihan/Desktop/7T_fs2025/LH2024_173 mri_convert /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/rawavg.mgz /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/orig.mgz --conform mri_convert /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/rawavg.mgz /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/orig.mgz --conform reading from /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/rawavg.mgz... TR=4300.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (0.999113, 0.039518, -0.0145547) j_ras = (-0.032424, 0.942387, 0.332949) k_ras = (0.0268736, -0.332182, 0.942833) changing data type from short to uchar (noscale = 0)... MRIchangeType: Building histogram 0 4093 1000, flo=0, fhi=0.999, dest_type=0 Reslicing using trilinear interpolation writing to /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/orig.mgz... @#@FSTIME 2026:03:26:15:18:45 mri_convert N 3 e 14.75 S 1.37 U 10.94 P 83% M 75744 F 0 R 25781 W 0 c 301 w 1 I 0 O 11176 L 6.19 2.48 1.06 @#@FSLOADPOST 2026:03:26:15:18:59 mri_convert N 3 7.34 2.84 1.19 mri_add_xform_to_header -c /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/transforms/talairach.xfm /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/orig.mgz /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/orig.mgz INFO: extension is mgz @#@FSTIME 2026:03:26:15:19:00 mri_add_xform_to_header N 4 e 2.93 S 0.77 U 1.44 P 75% M 23320 F 0 R 4572 W 0 c 76 w 3 I 0 O 11176 L 8.03 3.06 1.27 @#@FSLOADPOST 2026:03:26:15:19:03 mri_add_xform_to_header N 4 8.03 3.06 1.27 mri_info /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/orig.mgz orig.mgz ======================================== Volume information for /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/orig.mgz type: MGH dimensions: 256 x 256 x 256 voxel sizes: 1.000000, 1.000000, 1.000000 type: UCHAR (0) fov: 256.000 dof: 1 xstart: -128.0, xend: 128.0 ystart: -128.0, yend: 128.0 zstart: -128.0, zend: 128.0 TR: 4300.00 msec, TE: 0.00 msec, TI: 0.00 msec, flip angle: 0.00 degrees nframes: 1 PhEncDir: UNKNOWN FieldStrength: 0.000000 ras xform present xform info: x_r = -1.0000, y_r = 0.0000, z_r = 0.0000, c_r = 0.1570 : x_a = 0.0000, y_a = 0.0000, z_a = 1.0000, c_a = 5.8322 : x_s = 0.0000, y_s = -1.0000, z_s = 0.0000, c_s = -3.0108 talairach xfm : /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/transforms/talairach.xfm Orientation : LIA Primary Slice Direction: coronal voxel to ras transform: -1.0000 0.0000 0.0000 128.1570 0.0000 0.0000 1.0000 -122.1678 0.0000 -1.0000 0.0000 124.9892 0.0000 0.0000 0.0000 1.0000 voxel-to-ras determinant -1 ras to voxel transform: -1.0000 -0.0000 -0.0000 128.1570 -0.0000 -0.0000 -1.0000 124.9892 0.0000 1.0000 -0.0000 122.1678 -0.0000 -0.0000 -0.0000 1.0000 #-------------------------------------------- #@# Talairach 2026年 03月 26日星期四 15:19:03 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/mri mri_nu_correct.mni --no-rescale --i orig.mgz --o orig_nu.mgz --ants-n4 --n 1 --proto-iters 1000 --distance 50 /usr/bin/bc /home/laihan/Desktop/7T_fs2025/LH2024_173/mri /usr/local/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 7.4.1 Linux laihan-VirtualBox 5.15.0-139-generic #149~20.04.1-Ubuntu SMP Wed Apr 16 08:29:56 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2026年 03月 26日星期四 15:19:04 CST tmpdir is ./tmp.mri_nu_correct.mni.10763 cd /home/laihan/Desktop/7T_fs2025/LH2024_173/mri AntsN4BiasFieldCorrectionFs -i orig.mgz -o ./tmp.mri_nu_correct.mni.10763/nu0.mgz --dtype uchar AntsN4BiasFieldCorrectionFs done mri_convert ./tmp.mri_nu_correct.mni.10763/nu0.mgz orig_nu.mgz --like orig.mgz --conform mri_convert ./tmp.mri_nu_correct.mni.10763/nu0.mgz orig_nu.mgz --like orig.mgz --conform reading from ./tmp.mri_nu_correct.mni.10763/nu0.mgz... TR=4300.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 9.31323e-10, 0) j_ras = (0, 0, -1) k_ras = (0, 1, 0) INFO: transform src into the like-volume: orig.mgz writing to orig_nu.mgz... 2026年 03月 26日星期四 15:25:47 CST mri_nu_correct.mni done @#@FSTIME 2026:03:26:15:19:04 mri_nu_correct.mni N 12 e 403.70 S 13.40 U 371.70 P 95% M 505032 F 5 R 155986 W 0 c 4609 w 98 I 8 O 22032 L 8.03 3.06 1.27 @#@FSLOADPOST 2026:03:26:15:25:48 mri_nu_correct.mni N 12 13.17 10.23 5.32 talairach_avi --i orig_nu.mgz --xfm transforms/talairach.auto.xfm talairach_avi log file is transforms/talairach_avi.log... mv -f /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/talsrcimg_to_711-2C_as_mni_average_305_t4_vox2vox.txt /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/transforms/talsrcimg_to_711-2C_as_mni_average_305_t4_vox2vox.txt Started at 2026年 03月 26日星期四 15:25:48 CST Ended at 2026年 03月 26日星期四 15:26:27 CST talairach_avi done @#@FSTIME 2026:03:26:15:25:48 talairach_avi N 4 e 38.53 S 5.57 U 22.81 P 73% M 255600 F 0 R 399344 W 0 c 673 w 180 I 24 O 295280 L 13.17 10.23 5.32 @#@FSLOADPOST 2026:03:26:15:26:27 talairach_avi N 4 12.88 10.56 5.64 cp transforms/talairach.auto.xfm transforms/talairach.xfm lta_convert --src orig.mgz --trg /usr/local/freesurfer/average/mni305.cor.mgz --inxfm transforms/talairach.xfm --outlta transforms/talairach.xfm.lta --subject fsaverage --ltavox2vox 7.4.1 --src: orig.mgz src image (geometry). --trg: /usr/local/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 1.09464 0.02802 -0.02637 0.23447; 0.00060 1.09806 0.31542 -19.87995; 0.04807 -0.23331 1.16222 -3.76094; 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 2026年 03月 26日星期四 15:26:29 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/mri talairach_afd -T 0.005 -xfm transforms/talairach.xfm talairach_afd: Talairach Transform: transforms/talairach.xfm OK (p=0.8097, pval=0.8469 >= threshold=0.0050) @#@FSTIME 2026:03:26:15:26:29 talairach_afd N 4 e 0.01 S 0.00 U 0.01 P 92% M 5768 F 0 R 224 W 0 c 1 w 1 I 0 O 0 L 12.88 10.56 5.64 @#@FSLOADPOST 2026:03:26:15:26:30 talairach_afd N 4 12.88 10.56 5.64 awk -f /usr/local/freesurfer/bin/extract_talairach_avi_QA.awk /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/transforms/talairach_avi.log tal_QC_AZS /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/transforms/talairach_avi.log TalAviQA: 0.97097 z-score: -1 #-------------------------------------------- #@# Nu Intensity Correction 2026年 03月 26日星期四 15:26:30 CST mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --n 2 --ants-n4 /usr/bin/bc /home/laihan/Desktop/7T_fs2025/LH2024_173/mri /usr/local/freesurfer/bin/mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --n 2 --ants-n4 nIters 2 mri_nu_correct.mni 7.4.1 Linux laihan-VirtualBox 5.15.0-139-generic #149~20.04.1-Ubuntu SMP Wed Apr 16 08:29:56 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2026年 03月 26日星期四 15:26:31 CST tmpdir is ./tmp.mri_nu_correct.mni.14035 cd /home/laihan/Desktop/7T_fs2025/LH2024_173/mri AntsN4BiasFieldCorrectionFs -i orig.mgz -o ./tmp.mri_nu_correct.mni.14035/nu0.mgz --dtype uchar AntsN4BiasFieldCorrectionFs done mri_binarize --i ./tmp.mri_nu_correct.mni.14035/nu0.mgz --min -1 --o ./tmp.mri_nu_correct.mni.14035/ones.mgz 7.4.1 cwd /home/laihan/Desktop/7T_fs2025/LH2024_173/mri cmdline mri_binarize --i ./tmp.mri_nu_correct.mni.14035/nu0.mgz --min -1 --o ./tmp.mri_nu_correct.mni.14035/ones.mgz sysname Linux hostname laihan-VirtualBox machine x86_64 user laihan input ./tmp.mri_nu_correct.mni.14035/nu0.mgz frame 0 nErode3d 0 nErode2d 0 output ./tmp.mri_nu_correct.mni.14035/ones.mgz Binarizing based on threshold min -1 max +infinity binval 1 binvalnot 0 fstart = 0, fend = 0, nframes = 1 Starting parallel 1 Found 16777216 values in range Counting number of voxels in first frame Found 16777215 voxels in final mask Writing output to ./tmp.mri_nu_correct.mni.14035/ones.mgz Count: 16777215 16777215.000000 16777216 99.999994 mri_binarize done mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.14035/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.14035/sum.junk --avgwf ./tmp.mri_nu_correct.mni.14035/input.mean.dat 7.4.1 cwd cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.14035/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.14035/sum.junk --avgwf ./tmp.mri_nu_correct.mni.14035/input.mean.dat sysname Linux hostname laihan-VirtualBox machine x86_64 user laihan whitesurfname white UseRobust 0 Loading ./tmp.mri_nu_correct.mni.14035/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 Writing to ./tmp.mri_nu_correct.mni.14035/input.mean.dat mri_segstats done mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.14035/ones.mgz --i ./tmp.mri_nu_correct.mni.14035/nu0.mgz --sum ./tmp.mri_nu_correct.mni.14035/sum.junk --avgwf ./tmp.mri_nu_correct.mni.14035/output.mean.dat 7.4.1 cwd cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.14035/ones.mgz --i ./tmp.mri_nu_correct.mni.14035/nu0.mgz --sum ./tmp.mri_nu_correct.mni.14035/sum.junk --avgwf ./tmp.mri_nu_correct.mni.14035/output.mean.dat sysname Linux hostname laihan-VirtualBox machine x86_64 user laihan whitesurfname white UseRobust 0 Loading ./tmp.mri_nu_correct.mni.14035/ones.mgz Loading ./tmp.mri_nu_correct.mni.14035/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 Writing to ./tmp.mri_nu_correct.mni.14035/output.mean.dat mri_segstats done mris_calc -o ./tmp.mri_nu_correct.mni.14035/nu0.mgz ./tmp.mri_nu_correct.mni.14035/nu0.mgz mul 1.01685068718214292877 Saving result to './tmp.mri_nu_correct.mni.14035/nu0.mgz' (type = MGH ) [ ok ] mri_convert ./tmp.mri_nu_correct.mni.14035/nu0.mgz nu.mgz --like orig.mgz mri_convert ./tmp.mri_nu_correct.mni.14035/nu0.mgz nu.mgz --like orig.mgz reading from ./tmp.mri_nu_correct.mni.14035/nu0.mgz... TR=4300.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 9.31323e-10, 0) j_ras = (0, 0, -1) k_ras = (0, 1, 0) INFO: transform src into the like-volume: orig.mgz writing to nu.mgz... mri_make_uchar nu.mgz transforms/talairach.xfm nu.mgz type change took 0 minutes and 11 seconds. FIRST_PERCENTILE 0.010000 WM_PERCENTILE 0.900000 MAX_R 50.000000 i1 = 23, i2 = 68 #mri_make_uchar# mapping 59 175 to 3 110 : b -52.3689 m 0.92702 : thresh 56.4917 maxsat 331.567 : nzero 10508871 nsat 0 2026年 03月 26日星期四 15:33:52 CST mri_nu_correct.mni done @#@FSTIME 2026:03:26:15:26:30 mri_nu_correct.mni N 9 e 441.89 S 19.44 U 391.74 P 93% M 614232 F 44 R 512499 W 0 c 4776 w 211 I 8096 O 58152 L 12.01 10.41 5.62 @#@FSLOADPOST 2026:03:26:15:33:52 mri_nu_correct.mni N 9 13.16 12.31 8.36 mri_add_xform_to_header -c /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/transforms/talairach.xfm nu.mgz nu.mgz INFO: extension is mgz @#@FSTIME 2026:03:26:15:33:53 mri_add_xform_to_header N 4 e 2.43 S 0.53 U 1.45 P 81% M 23380 F 1 R 4573 W 0 c 47 w 2 I 256 O 10624 L 13.16 12.31 8.36 @#@FSLOADPOST 2026:03:26:15:33:55 mri_add_xform_to_header N 4 13.16 12.31 8.36 #-------------------------------------------- #@# Intensity Normalization 2026年 03月 26日星期四 15:33:55 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/mri mri_normalize -g 1 -seed 1234 -mprage nu.mgz T1.mgz using max gradient = 1.000 setting seed for random number genererator to 1234 assuming input volume is MGH (Van der Kouwe) MP-RAGE reading mri_src from nu.mgz... normalizing image... NOT doing gentle normalization with control points/label talairach transform 1.09464 0.02802 -0.02637 0.23447; 0.00060 1.09806 0.31542 -19.87995; 0.04807 -0.23331 1.16222 -3.76094; 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 = 13 Starting OpenSpline(): npoints = 13 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 20 (20) csf peak at 38, setting threshold to 60 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 71 (71), valley at 18 (18) csf peak at 36, setting threshold to 59 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Done iterating --------------------------------- writing output to T1.mgz 3D bias adjustment took 3 minutes and 13 seconds. @#@FSTIME 2026:03:26:15:33:56 mri_normalize N 7 e 194.65 S 10.18 U 173.95 P 94% M 583792 F 8 R 263276 W 0 c 2286 w 6 I 1712 O 10240 L 13.23 12.34 8.39 @#@FSLOADPOST 2026:03:26:15:37:11 mri_normalize N 7 12.36 12.45 9.21 #-------------------------------------------- #@# Skull Stripping 2026年 03月 26日星期四 15:37:11 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/mri mri_em_register -skull nu.mgz /usr/local/freesurfer/average/RB_all_withskull_2020_01_02.gca transforms/talairach_with_skull.lta aligning to atlas containing skull, setting unknown_nbr_spacing = 5 == Number of threads available to mri_em_register for OpenMP = 1 == reading 1 input volumes... logging results to talairach_with_skull.log reading '/usr/local/freesurfer/average/RB_all_withskull_2020_01_02.gca'... GCAread took 0 minutes and 7 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=0.0 skull bounding box = (0, 0, 0) --> (255, 255, 255) finding center of left hemi white matter using (85, 85, 128) as brain centroid of Right_Cerebral_White_Matter... MRImask(): AllowDiffGeom = 1 mean wm in atlas = 108, using box (53,53,96) --> (116, 116,159) to find MRI wm before smoothing, mri peak at 114 robust fit to distribution - 99 +- 6.8 after smoothing, mri peak at 99, scaling input intensities by 1.091 scaling channel 0 by 1.09091 initial log_p = -4.481 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.617397 @ (0.000, 0.000, 0.000) max log p = -4.592958 @ (5.263, 5.263, -5.263) max log p = -4.523126 @ (-7.895, -2.632, -7.895) max log p = -4.523126 @ (0.000, 0.000, 0.000) max log p = -4.517795 @ (3.289, 0.658, -0.658) max log p = -4.508459 @ (-0.987, -0.329, -0.329) max log p = -4.508459 @ (0.000, 0.000, 0.000) max log p = -4.508459 @ (0.000, 0.000, 0.000) Found translation: (-0.3, 3.0, -14.1): log p = -4.508 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.383, old_max_log_p =-4.508 (thresh=-4.5) 1.14016 -0.14499 -0.03378 -1.00088; 0.16136 1.18391 0.27585 -69.93784; 0.00000 -0.31997 1.03837 25.57001; 0.00000 0.00000 0.00000 1.00000; iteration took 4 minutes and 10 seconds. **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.359, old_max_log_p =-4.383 (thresh=-4.4) 1.15129 0.01573 -0.01814 -25.68600; 0.01053 1.32255 0.13011 -43.06945; 0.02264 -0.22605 1.31141 -23.50074; 0.00000 0.00000 0.00000 1.00000; iteration took 3 minutes and 51 seconds. **************************************** Nine parameter search. iteration 2 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.334, old_max_log_p =-4.359 (thresh=-4.4) 1.15129 0.01573 -0.01814 -25.68600; 0.01339 1.28173 0.30017 -59.06294; 0.02107 -0.39675 1.28321 1.82214; 0.00000 0.00000 0.00000 1.00000; iteration took 3 minutes and 28 seconds. **************************************** Nine parameter search. iteration 3 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.334, old_max_log_p =-4.334 (thresh=-4.3) 1.15129 0.01573 -0.01814 -25.68600; 0.01339 1.28173 0.30017 -59.06294; 0.02107 -0.39675 1.28321 1.82214; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 iteration took 3 minutes and 16 seconds. **************************************** Nine parameter search. iteration 4 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.264, old_max_log_p =-4.334 (thresh=-4.3) 1.08637 -0.02845 -0.02908 -6.19083; 0.05045 1.26952 0.25242 -58.08510; 0.01837 -0.34801 1.26793 -3.81555; 0.00000 0.00000 0.00000 1.00000; iteration took 3 minutes and 3 seconds. **************************************** Nine parameter search. iteration 5 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.264, old_max_log_p =-4.264 (thresh=-4.3) 1.08637 -0.02845 -0.02908 -6.19083; 0.05045 1.26952 0.25242 -58.08510; 0.01837 -0.34801 1.26793 -3.81555; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 iteration took 3 minutes and 6 seconds. **************************************** Nine parameter search. iteration 6 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.259, old_max_log_p =-4.264 (thresh=-4.3) 1.08510 -0.02841 -0.02904 -6.50572; 0.05045 1.26952 0.25242 -58.08510; 0.01837 -0.34801 1.26793 -3.81555; 0.00000 0.00000 0.00000 1.00000; iteration took 2 minutes and 41 seconds. **************************************** Nine parameter search. iteration 7 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.253, old_max_log_p =-4.259 (thresh=-4.3) 1.08382 -0.02838 -0.02901 -6.35112; 0.05033 1.26654 0.25182 -57.61589; 0.01837 -0.34801 1.26793 -3.81555; 0.00000 0.00000 0.00000 1.00000; iteration took 2 minutes and 45 seconds. **************************************** Nine parameter search. iteration 8 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.253, old_max_log_p =-4.253 (thresh=-4.2) 1.08382 -0.02838 -0.02901 -6.35112; 0.05033 1.26654 0.25182 -57.61589; 0.01837 -0.34801 1.26793 -3.81555; 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 1.08382 -0.02838 -0.02901 -6.35112; 0.05033 1.26654 0.25182 -57.61589; 0.01837 -0.34801 1.26793 -3.81555; 0.00000 0.00000 0.00000 1.00000; nsamples 3292 Quasinewton: input matrix 1.08382 -0.02838 -0.02901 -6.35112; 0.05033 1.26654 0.25182 -57.61589; 0.01837 -0.34801 1.26793 -3.81555; 0.00000 0.00000 0.00000 1.00000; IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 3 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 011: -log(p) = -0.0 tol 0.000010 Resulting transform: 1.08382 -0.02838 -0.02901 -6.35112; 0.05033 1.26654 0.25182 -57.61589; 0.01837 -0.34801 1.26793 -3.81555; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -4.253 (old=-4.481) transform before final EM align: 1.08382 -0.02838 -0.02901 -6.35112; 0.05033 1.26654 0.25182 -57.61589; 0.01837 -0.34801 1.26793 -3.81555; 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 1.08382 -0.02838 -0.02901 -6.35112; 0.05033 1.26654 0.25182 -57.61589; 0.01837 -0.34801 1.26793 -3.81555; 0.00000 0.00000 0.00000 1.00000; nsamples 364986 Quasinewton: input matrix 1.08382 -0.02838 -0.02901 -6.35112; 0.05033 1.26654 0.25182 -57.61589; 0.01837 -0.34801 1.26793 -3.81555; 0.00000 0.00000 0.00000 1.00000; IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 6 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 013: -log(p) = 4.7 tol 0.000000 final transform: 1.08382 -0.02838 -0.02901 -6.35112; 0.05033 1.26654 0.25182 -57.61589; 0.01837 -0.34801 1.26793 -3.81555; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach_with_skull.lta... #VMPC# mri_em_register VmPeak 789496 FSRUNTIME@ mri_em_register 0.5309 hours 1 threads registration took 31 minutes and 51 seconds. @#@FSTIME 2026:03:26:15:37:11 mri_em_register N 4 e 1911.46 S 59.25 U 1759.53 P 95% M 629332 F 5 R 166630 W 0 c 17958 w 198 I 73640 O 32 L 12.36 12.45 9.21 @#@FSLOADPOST 2026:03:26:16:09:03 mri_em_register N 4 11.54 12.02 11.90 mri_watershed -T1 -brain_atlas /usr/local/freesurfer/average/RB_all_withskull_2020_01_02.gca transforms/talairach_with_skull.lta T1.mgz brainmask.auto.mgz Mode: T1 normalized volume Mode: Use the information of atlas (default parms, --help for details) ********************************************************* The input file is T1.mgz The output file is brainmask.auto.mgz Weighting the input with atlas information before watershed *************************WATERSHED************************** Sorting... first estimation of the COG coord: x=128 y=108 z=119 r=94 first estimation of the main basin volume: 3585425 voxels Looking for seedpoints 2 found in the cerebellum 12 found in the rest of the brain global maximum in x=149, y=102, z=88, Imax=255 CSF=13, 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=1316205093478418 voxels, voxel volume =1.000 = 1316205093478418 mmm3 = 1316205075890.176 cm3 done. PostAnalyze...Basin Prior 219 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=127,y=115, z=115, r=8853 iterations ^^^^^^^^ couldn't find WM with original limits - expanding ^^^^^^ GLOBAL CSF_MIN=1, CSF_intensity=2, CSF_MAX=4 , nb = 23508 RIGHT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=3 , nb = 2106 LEFT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=4 , nb = 1620 RIGHT_BRAIN CSF_MIN=0, CSF_intensity=36, CSF_MAX=52 , nb = 9666 LEFT_BRAIN CSF_MIN=1, CSF_intensity=2, CSF_MAX=4 , nb = 9666 OTHER CSF_MIN=1, CSF_intensity=2, CSF_MAX=4 , nb = 450 CSF_MAX TRANSITION GM_MIN GM GLOBAL before analyzing : 4, 5, 32, 59 after analyzing : 4, 23, 32, 32 RIGHT_CER before analyzing : 3, 4, 33, 50 after analyzing : 3, 23, 33, 29 LEFT_CER before analyzing : 4, 5, 30, 57 after analyzing : 4, 21, 30, 30 RIGHT_BRAIN before analyzing : 52, 45, 34, 60 after analyzing : 41, 45, 45, 48 LEFT_BRAIN before analyzing : 4, 5, 32, 60 after analyzing : 4, 23, 32, 32 OTHER before analyzing : 4, 7, 63, 91 after analyzing : 4, 44, 63, 55 mri_strip_skull: done peeling brain highly tesselated surface with 10242 vertices matching...59 iterations *********************VALIDATION********************* curvature mean = -0.013, std = 0.009 curvature mean = 69.743, std = 6.900 No Rigid alignment: -atlas Mode Off (basic atlas / no registration) before rotation: sse = 3.21, sigma = 5.06 after rotation: sse = 3.21, sigma = 5.06 Localization of inacurate regions: Erosion-Dilation steps the sse mean is 3.43, its var is 4.81 before Erosion-Dilatation 0.88% of inacurate vertices after Erosion-Dilatation 0.00% of inacurate vertices Validation of the shape of the surface done. Scaling of atlas fields onto current surface fields ********FINAL ITERATIVE TEMPLATE DEFORMATION******** Compute Local values csf/gray Fine Segmentation...37 iterations mri_strip_skull: done peeling brain Brain Size = 1441710 voxels, voxel volume = 1.000 mm3 = 1441710 mmm3 = 1441.710 cm3 ****************************** Saving brainmask.auto.mgz done mri_watershed done @#@FSTIME 2026:03:26:16:09:03 mri_watershed N 6 e 31.58 S 3.40 U 26.23 P 93% M 820032 F 0 R 207754 W 0 c 473 w 1 I 0 O 2392 L 11.54 12.02 11.90 @#@FSLOADPOST 2026:03:26:16:09:35 mri_watershed N 6 11.86 12.06 11.92 cp brainmask.auto.mgz brainmask.mgz #------------------------------------- #@# EM Registration 2026年 03月 26日星期四 16:09:37 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/mri mri_em_register -uns 3 -mask brainmask.mgz nu.mgz /usr/local/freesurfer/average/RB_all_2020-01-02.gca transforms/talairach.lta setting unknown_nbr_spacing = 3 using MR volume brainmask.mgz to mask input volume... == Number of threads available to mri_em_register for OpenMP = 1 == reading 1 input volumes... logging results to talairach.log reading '/usr/local/freesurfer/average/RB_all_2020-01-02.gca'... GCAread took 0 minutes and 2 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=29.0 skull bounding box = (60, 55, 45) --> (197, 190, 200) finding center of left hemi white matter using (106, 100, 123) as brain centroid of Right_Cerebral_White_Matter... MRImask(): AllowDiffGeom = 1 mean wm in atlas = 107, using box (89,83,104) --> (122, 116,142) to find MRI wm before smoothing, mri peak at 98 robust fit to distribution - 99 +- 4.5 after smoothing, mri peak at 99, scaling input intensities by 1.081 scaling channel 0 by 1.08081 initial log_p = -4.151 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.215926 @ (-10.526, 10.526, -10.526) max log p = -4.041299 @ (5.263, -5.263, -5.263) max log p = -3.997254 @ (2.632, -2.632, -2.632) max log p = -3.969913 @ (1.316, -1.316, 1.316) max log p = -3.951037 @ (-1.974, 1.974, 5.921) max log p = -3.945054 @ (2.961, -3.618, -6.250) max log p = -3.945054 @ (0.000, 0.000, 0.000) max log p = -3.945054 @ (0.000, 0.000, 0.000) Found translation: (-0.3, -0.3, -17.4): log p = -3.945 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.851, old_max_log_p =-3.945 (thresh=-3.9) 1.00000 0.00000 0.00000 -0.32895; 0.00000 1.06580 0.14032 -17.93880; 0.00000 -0.14032 1.06580 -1.23404; 0.00000 0.00000 0.00000 1.00000; iteration took 3 minutes and 24 seconds. **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-3.851, old_max_log_p =-3.851 (thresh=-3.8) 1.00000 0.00000 0.00000 -0.32895; 0.00000 1.06580 0.14032 -17.93880; 0.00000 -0.14032 1.06580 -1.23404; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 iteration took 3 minutes and 11 seconds. **************************************** Nine parameter search. iteration 2 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.817, old_max_log_p =-3.851 (thresh=-3.8) 1.01393 -0.10268 -0.02630 12.71583; 0.09374 0.98429 0.22803 -36.23561; 0.00206 -0.24545 1.04656 12.53991; 0.00000 0.00000 0.00000 1.00000; iteration took 2 minutes and 59 seconds. **************************************** Nine parameter search. iteration 3 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-3.817, old_max_log_p =-3.817 (thresh=-3.8) 1.01393 -0.10268 -0.02630 12.71583; 0.09374 0.98429 0.22803 -36.23561; 0.00206 -0.24545 1.04656 12.53991; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 iteration took 3 minutes and 18 seconds. **************************************** Nine parameter search. iteration 4 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.787, old_max_log_p =-3.817 (thresh=-3.8) 1.01347 -0.09452 -0.02426 11.56754; 0.08558 0.98211 0.24558 -36.50866; 0.00052 -0.26095 1.04023 14.84594; 0.00000 0.00000 0.00000 1.00000; iteration took 2 minutes and 48 seconds. **************************************** Nine parameter search. iteration 5 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-3.783, old_max_log_p =-3.787 (thresh=-3.8) 1.01466 -0.09464 -0.02429 11.43251; 0.08558 0.98211 0.24558 -36.03991; 0.00052 -0.26125 1.04145 14.73980; 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 1.01466 -0.09464 -0.02429 11.43251; 0.08558 0.98211 0.24558 -36.03991; 0.00052 -0.26125 1.04145 14.73980; 0.00000 0.00000 0.00000 1.00000; nsamples 2841 Quasinewton: input matrix 1.01466 -0.09464 -0.02429 11.43251; 0.08558 0.98211 0.24558 -36.03991; 0.00052 -0.26125 1.04145 14.73980; 0.00000 0.00000 0.00000 1.00000; IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 3 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 008: -log(p) = -0.0 tol 0.000010 Resulting transform: 1.01466 -0.09464 -0.02429 11.43251; 0.08558 0.98211 0.24558 -36.03991; 0.00052 -0.26125 1.04145 14.73980; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -3.783 (old=-4.151) transform before final EM align: 1.01466 -0.09464 -0.02429 11.43251; 0.08558 0.98211 0.24558 -36.03991; 0.00052 -0.26125 1.04145 14.73980; 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 1.01466 -0.09464 -0.02429 11.43251; 0.08558 0.98211 0.24558 -36.03991; 0.00052 -0.26125 1.04145 14.73980; 0.00000 0.00000 0.00000 1.00000; nsamples 315638 Quasinewton: input matrix 1.01466 -0.09464 -0.02429 11.43251; 0.08558 0.98211 0.24558 -36.03991; 0.00052 -0.26125 1.04145 14.73980; 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) = 4.2 tol 0.000000 final transform: 1.01466 -0.09464 -0.02429 11.43251; 0.08558 0.98211 0.24558 -36.03991; 0.00052 -0.26125 1.04145 14.73980; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach.lta... #VMPC# mri_em_register VmPeak 776936 FSRUNTIME@ mri_em_register 0.3383 hours 1 threads registration took 20 minutes and 18 seconds. @#@FSTIME 2026:03:26:16:09:37 mri_em_register N 7 e 1218.04 S 40.87 U 1105.80 P 94% M 616464 F 0 R 166620 W 0 c 12163 w 2 I 0 O 32 L 12.19 12.12 11.94 @#@FSLOADPOST 2026:03:26:16:29:55 mri_em_register N 7 12.00 12.10 12.22 #-------------------------------------- #@# CA Normalize 2026年 03月 26日星期四 16:29:55 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/mri mri_ca_normalize -c ctrl_pts.mgz -mask brainmask.mgz nu.mgz /usr/local/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 '/usr/local/freesurfer/average/RB_all_2020-01-02.gca'... reading transform from 'transforms/talairach.lta'... reading input volume from nu.mgz... resetting wm mean[0]: 98 --> 107 resetting gm mean[0]: 61 --> 61 input volume #1 is the most T1-like using real data threshold=29.0 skull bounding box = (60, 55, 45) --> (197, 190, 200) finding center of left hemi white matter using (106, 100, 123) as brain centroid of Right_Cerebral_White_Matter... mean wm in atlas = 107, using box (89,83,104) --> (122, 116,142) to find MRI wm before smoothing, mri peak at 98 robust fit to distribution - 99 +- 4.5 after smoothing, mri peak at 99, scaling input intensities by 1.081 scaling channel 0 by 1.08081 using 246437 sample points... INFO: compute sample coordinates transform 1.01466 -0.09464 -0.02429 11.43251; 0.08558 0.98211 0.24558 -36.03991; 0.00052 -0.26125 1.04145 14.73980; 0.00000 0.00000 0.00000 1.00000; INFO: transform used finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (125, 51, 46) --> (193, 165, 200) Left_Cerebral_White_Matter: limiting intensities to 99.0 --> 132.0 10 of 988 (1.0%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (62, 54, 45) --> (128, 167, 201) Right_Cerebral_White_Matter: limiting intensities to 97.0 --> 132.0 0 of 1057 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (130, 138, 74) --> (181, 182, 124) Left_Cerebellum_White_Matter: limiting intensities to 133.0 --> 132.0 11 of 11 (100.0%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (88, 139, 72) --> (131, 185, 125) Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 1 of 7 (14.3%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (113, 128, 107) --> (149, 197, 135) Brain_Stem: limiting intensities to 100.0 --> 132.0 0 of 14 (0.0%) samples deleted using 2077 total control points for intensity normalization... bias field = 0.939 +- 0.061 9 of 2055 control points discarded finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (125, 51, 46) --> (193, 165, 200) Left_Cerebral_White_Matter: limiting intensities to 89.0 --> 132.0 0 of 1539 (0.0%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (62, 54, 45) --> (128, 167, 201) Right_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 0 of 1626 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (130, 138, 74) --> (181, 182, 124) Left_Cerebellum_White_Matter: limiting intensities to 101.0 --> 132.0 0 of 61 (0.0%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (88, 139, 72) --> (131, 185, 125) Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 5 of 5 (100.0%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (113, 128, 107) --> (149, 197, 135) Brain_Stem: limiting intensities to 88.0 --> 132.0 0 of 58 (0.0%) samples deleted using 3289 total control points for intensity normalization... bias field = 1.034 +- 0.073 40 of 3245 control points discarded finding control points in Left_Cerebral_White_Matter.... found 40230 control points for structure... bounding box (125, 51, 46) --> (193, 165, 200) Left_Cerebral_White_Matter: limiting intensities to 89.0 --> 132.0 0 of 1770 (0.0%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39478 control points for structure... bounding box (62, 54, 45) --> (128, 167, 201) Right_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 0 of 1816 (0.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3105 control points for structure... bounding box (130, 138, 74) --> (181, 182, 124) Left_Cerebellum_White_Matter: limiting intensities to 89.0 --> 132.0 0 of 40 (0.0%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2710 control points for structure... bounding box (88, 139, 72) --> (131, 185, 125) Right_Cerebellum_White_Matter: limiting intensities to 103.0 --> 132.0 6 of 7 (85.7%) samples deleted finding control points in Brain_Stem.... found 3475 control points for structure... bounding box (113, 128, 107) --> (149, 197, 135) Brain_Stem: limiting intensities to 88.0 --> 132.0 54 of 155 (34.8%) samples deleted using 3788 total control points for intensity normalization... bias field = 1.050 +- 0.060 26 of 3640 control points discarded writing normalized volume to norm.mgz... writing control points to ctrl_pts.mgz freeing GCA...done. normalization took 1 minutes and 41 seconds. @#@FSTIME 2026:03:26:16:29:55 mri_ca_normalize N 8 e 100.92 S 6.84 U 87.58 P 93% M 694564 F 0 R 408839 W 0 c 1286 w 1 I 0 O 3400 L 12.00 12.10 12.22 @#@FSLOADPOST 2026:03:26:16:31:36 mri_ca_normalize N 8 11.28 11.79 12.09 #-------------------------------------- #@# CA Reg 2026年 03月 26日星期四 16:31:37 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/mri mri_ca_register -nobigventricles -T transforms/talairach.lta -align-after -mask brainmask.mgz norm.mgz /usr/local/freesurfer/average/RB_all_2020-01-02.gca transforms/talairach.m3z not handling expanded ventricles... using previously computed transform transforms/talairach.lta renormalizing sequences with structure alignment, equivalent to: -renormalize -regularize_mean 0.500 -regularize 0.500 using MR volume brainmask.mgz to mask input volume... == Number of threads available to mri_ca_register for OpenMP = 1 == reading 1 input volumes... logging results to talairach.log reading input volume 'norm.mgz'... reading GCA '/usr/local/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 0.982857 #GCAMreg# pass 0 level1 5 level2 1 tsec 457.852 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 0.749498 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 0.752259 #GCAMreg# pass 0 level1 4 level2 1 tsec 328.857 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 0.727082 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 0.765617 #GCAMreg# pass 0 level1 3 level2 1 tsec 149.7 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 0.765243 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 0.868357 #GCAMreg# pass 0 level1 2 level2 1 tsec 223.437 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 0.836884 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 0.922258 #GCAMreg# pass 0 level1 1 level2 1 tsec 385.07 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 0.901878 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 0.840685 #GCAMreg# pass 0 level1 0 level2 1 tsec 110.315 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 0.834529 GCAMregister done in 42.2216 min Starting GCAmapRenormalizeWithAlignment() without scales renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.10253 (16) mri peak = 0.09901 (16) Left_Lateral_Ventricle (4): linear fit = 0.67 x + 0.0 (708 voxels, overlap=0.549) Left_Lateral_Ventricle (4): linear fit = 0.67 x + 0.0 (708 voxels, peak = 11), gca=10.6 gca peak = 0.17690 (16) mri peak = 0.07591 ( 5) Right_Lateral_Ventricle (43): linear fit = 0.38 x + 0.0 (661 voxels, overlap=0.273) Right_Lateral_Ventricle (43): linear fit = 0.40 x + 0.0 (661 voxels, peak = 6), gca=6.4 gca peak = 0.28275 (96) mri peak = 0.05719 (94) Right_Pallidum (52): linear fit = 0.86 x + 0.0 (838 voxels, overlap=1.003) Right_Pallidum (52): linear fit = 0.86 x + 0.0 (838 voxels, peak = 82), gca=82.1 gca peak = 0.18948 (93) mri peak = 0.06767 (97) Left_Pallidum (13): linear fit = 1.02 x + 0.0 (853 voxels, overlap=0.958) Left_Pallidum (13): linear fit = 1.02 x + 0.0 (853 voxels, peak = 95), gca=95.3 gca peak = 0.20755 (55) mri peak = 0.04326 (90) Right_Hippocampus (53): linear fit = 1.47 x + 0.0 (528 voxels, overlap=0.315) Right_Hippocampus (53): linear fit = 1.47 x + 0.0 (528 voxels, peak = 81), gca=80.6 gca peak = 0.31831 (58) mri peak = 0.05897 (59) Left_Hippocampus (17): linear fit = 1.00 x + 0.0 (467 voxels, overlap=0.943) Left_Hippocampus (17): linear fit = 1.00 x + 0.0 (467 voxels, peak = 58), gca=58.0 gca peak = 0.11957 (102) mri peak = 0.09141 (97) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (60399 voxels, overlap=0.873) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (60399 voxels, peak = 102), gca=102.0 gca peak = 0.11429 (102) mri peak = 0.08975 (101) Left_Cerebral_White_Matter (2): linear fit = 1.01 x + 0.0 (65173 voxels, overlap=0.782) Left_Cerebral_White_Matter (2): linear fit = 1.01 x + 0.0 (65173 voxels, peak = 104), gca=103.5 gca peak = 0.14521 (59) mri peak = 0.03452 (65) Left_Cerebral_Cortex (3): linear fit = 1.12 x + 0.0 (14353 voxels, overlap=0.559) Left_Cerebral_Cortex (3): linear fit = 1.12 x + 0.0 (14353 voxels, peak = 66), gca=66.4 gca peak = 0.14336 (58) mri peak = 0.03562 (64) Right_Cerebral_Cortex (42): linear fit = 1.10 x + 0.0 (12551 voxels, overlap=0.719) Right_Cerebral_Cortex (42): linear fit = 1.10 x + 0.0 (12551 voxels, peak = 64), gca=63.5 gca peak = 0.13305 (70) mri peak = 0.10686 (75) Right_Caudate (50): linear fit = 1.13 x + 0.0 (1076 voxels, overlap=0.344) Right_Caudate (50): linear fit = 1.13 x + 0.0 (1076 voxels, peak = 79), gca=79.4 gca peak = 0.15761 (71) mri peak = 0.10499 (78) Left_Caudate (11): linear fit = 1.08 x + 0.0 (1040 voxels, overlap=0.567) Left_Caudate (11): linear fit = 1.08 x + 0.0 (1040 voxels, peak = 76), gca=76.3 gca peak = 0.13537 (57) mri peak = 0.03630 (51) Left_Cerebellum_Cortex (8): linear fit = 1.04 x + 0.0 (5471 voxels, overlap=0.984) Left_Cerebellum_Cortex (8): linear fit = 1.04 x + 0.0 (5471 voxels, peak = 60), gca=59.6 gca peak = 0.13487 (56) mri peak = 0.04177 (95) Right_Cerebellum_Cortex (47): linear fit = 1.71 x + 0.0 (7580 voxels, overlap=0.001) Right_Cerebellum_Cortex (47): linear fit = 1.71 x + 0.0 (7580 voxels, peak = 95), gca=95.5 gca peak = 0.19040 (84) mri peak = 0.04841 (99) Left_Cerebellum_White_Matter (7): linear fit = 1.18 x + 0.0 (4969 voxels, overlap=0.003) Left_Cerebellum_White_Matter (7): linear fit = 1.18 x + 0.0 (4969 voxels, peak = 100), gca=99.5 gca peak = 0.18871 (83) mri peak = 0.03861 (49) Right_Cerebellum_White_Matter (46): linear fit = 0.71 x + 0.0 (3452 voxels, overlap=0.003) Right_Cerebellum_White_Matter (46): linear fit = 0.71 x + 0.0 (3452 voxels, peak = 59), gca=59.3 gca peak = 0.24248 (57) mri peak = 0.07028 (65) Left_Amygdala (18): linear fit = 1.13 x + 0.0 (343 voxels, overlap=0.293) Left_Amygdala (18): linear fit = 1.13 x + 0.0 (343 voxels, peak = 65), gca=64.7 gca peak = 0.35833 (56) mri peak = 0.06686 (56) Right_Amygdala (54): linear fit = 0.99 x + 0.0 (381 voxels, overlap=0.977) Right_Amygdala (54): linear fit = 0.99 x + 0.0 (381 voxels, peak = 55), gca=55.2 gca peak = 0.12897 (85) mri peak = 0.07913 (90) Left_Thalamus (10): linear fit = 1.02 x + 0.0 (5961 voxels, overlap=0.941) Left_Thalamus (10): linear fit = 1.02 x + 0.0 (5961 voxels, peak = 87), gca=87.1 gca peak = 0.13127 (83) mri peak = 0.08064 (90) Right_Thalamus (49): linear fit = 1.08 x + 0.0 (5090 voxels, overlap=0.695) Right_Thalamus (49): linear fit = 1.08 x + 0.0 (5090 voxels, peak = 89), gca=89.2 gca peak = 0.12974 (78) mri peak = 0.10759 (84) Left_Putamen (12): linear fit = 1.07 x + 0.0 (2401 voxels, overlap=0.703) Left_Putamen (12): linear fit = 1.07 x + 0.0 (2401 voxels, peak = 83), gca=83.1 gca peak = 0.17796 (79) mri peak = 0.07649 (84) Right_Putamen (51): linear fit = 1.04 x + 0.0 (2343 voxels, overlap=0.861) Right_Putamen (51): linear fit = 1.04 x + 0.0 (2343 voxels, peak = 83), gca=82.6 gca peak = 0.10999 (80) mri peak = 0.08935 (83) Brain_Stem (16): linear fit = 1.10 x + 0.0 (7865 voxels, overlap=0.415) Brain_Stem (16): linear fit = 1.10 x + 0.0 (7865 voxels, peak = 88), gca=87.6 gca peak = 0.13215 (88) mri peak = 0.07227 (92) Right_VentralDC (60): linear fit = 1.03 x + 0.0 (969 voxels, overlap=0.730) Right_VentralDC (60): linear fit = 1.03 x + 0.0 (969 voxels, peak = 91), gca=91.1 gca peak = 0.11941 (89) mri peak = 0.08696 (88) Left_VentralDC (28): linear fit = 1.02 x + 0.0 (1130 voxels, overlap=0.848) Left_VentralDC (28): linear fit = 1.02 x + 0.0 (1130 voxels, peak = 91), gca=91.2 gca peak = 0.20775 (25) mri peak = 0.05944 (15) gca peak = 0.13297 (21) mri peak = 0.04901 (23) Fourth_Ventricle (15): linear fit = 1.36 x + 0.0 (343 voxels, overlap=0.494) Fourth_Ventricle (15): linear fit = 1.36 x + 0.0 (343 voxels, peak = 28), gca=28.5 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_Cerebellum_Cortex = 0.13487 (56) gca peak Right_Accumbens_area = 0.30137 (64) gca peak Right_vessel = 0.47828 (52) gca peak Right_choroid_plexus = 0.11612 (45) gca peak Fifth_Ventricle = 0.59466 (35) gca peak WM_hypointensities = 0.10053 (78) gca peak non_WM_hypointensities = 0.07253 (60) gca peak Optic_Chiasm = 0.25330 (73) not using caudate to estimate GM means setting label Right_Cerebellum_Cortex based on Left_Cerebellum_Cortex = 1.04 x + 0: 60 estimating mean gm scale to be 1.13 x + 0.0 estimating mean wm scale to be 1.01 x + 0.0 estimating mean csf scale to be 0.81 x + 0.0 saving intensity scales to talairach.label_intensities.txt GCAmapRenormalizeWithAlignment() took 12.4209 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 0.841094 #GCAMreg# pass 0 level1 5 level2 1 tsec 1665.03 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 0.707435 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 0.703124 #GCAMreg# pass 0 level1 4 level2 1 tsec 1967.96 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 0.621143 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 0.620245 #GCAMreg# pass 0 level1 3 level2 1 tsec 1369.61 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 0.56847 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 0.595477 #GCAMreg# pass 0 level1 2 level2 1 tsec 89.224 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 0.595477 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 0.65501 #GCAMreg# pass 0 level1 1 level2 1 tsec 95.719 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 0.649871 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 0.589436 #GCAMreg# pass 0 level1 0 level2 1 tsec 519.398 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 0.55074 GCAMregister done in 129.752 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 0.538276 #GCAMreg# pass 0 level1 5 level2 1 tsec 94.69 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 0.538276 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 0.538308 #GCAMreg# pass 0 level1 4 level2 1 tsec 116.672 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 0.536704 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 0.529752 iter 0, gcam->neg = 3 after 2 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 4 after 10 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 3 after 9 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 10 after 14 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 15 after 10 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 1 after 3 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 2 after 3 iterations, nbhd size=0, neg = 0 #GCAMreg# pass 0 level1 3 level2 1 tsec 342.173 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 0.517422 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 2 after 3 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 1 after 1 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 1 after 3 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 1 after 4 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 3 after 3 iterations, nbhd size=0, 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 0.500094 #GCAMreg# pass 0 level1 2 level2 1 tsec 86.439 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 0.500094 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 0.512132 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 #GCAMreg# pass 0 level1 1 level2 1 tsec 95.373 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 0.511457 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 resetting metric properties... setting smoothness cost coefficient to 2.000 #GCAMreg# pass 0 level1 0 level2 0 tsec 0 sigma 2 l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.00 tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=2.0,type=2, relabel=0, neg=yes blurring input image with Gaussian with sigma=2.000... GCAMRegisterLevel(): init RMS 0.49824 iter 0, gcam->neg = 1316 after 20 iterations, nbhd size=1, neg = 0 #GCAMreg# pass 0 level1 0 level2 1 tsec 116.007 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 0.463835 label assignment complete, 0 changed (0.00%) GCAMregister done in 31.6958 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 0.449278 #GCAMreg# pass 0 level1 5 level2 1 tsec 52.069 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 0.449278 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 0.449432 #GCAMreg# pass 0 level1 4 level2 1 tsec 45.585 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 0.449432 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 0.449795 #GCAMreg# pass 0 level1 3 level2 1 tsec 94.185 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 0.449647 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 0.446287 #GCAMreg# pass 0 level1 2 level2 1 tsec 94.365 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 0.446128 iter 0, gcam->neg = 3 after 18 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 36 after 14 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 2 after 8 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 6 after 9 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 19 after 11 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 19 after 15 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 22 after 2 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 23 after 15 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 10 after 4 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 14 after 12 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 10 after 4 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 12 after 15 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 38 after 13 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 38 after 15 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 39 after 18 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 0.444865 #GCAMreg# pass 0 level1 1 level2 1 tsec 90.585 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 0.444864 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 0.435325 iter 0, gcam->neg = 735 after 17 iterations, nbhd size=1, neg = 0 #GCAMreg# pass 0 level1 0 level2 1 tsec 114.688 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 0.425894 iter 0, gcam->neg = 1 after 0 iterations, nbhd size=0, neg = 0 iter 0, gcam->neg = 25 after 12 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 51 after 12 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 39 after 14 iterations, nbhd size=1, neg = 0 iter 0, gcam->neg = 53 after 13 iterations, nbhd size=1, neg = 0 GCAMregister done in 26.8787 min writing output transformation to transforms/talairach.m3z... GCAMwrite Calls to gcamLogLikelihoodEnergy 4165 tmin = 34.3708 Calls to gcamLabelEnergy 3567 tmin = 2.97007 Calls to gcamJacobianEnergy 4165 tmin = 18.9345 Calls to gcamSmoothnessEnergy 4165 tmin = 25.3153 Calls to gcamLogLikelihoodTerm 612 tmin = 11.4983 Calls to gcamLabelTerm 554 tmin = 15.9492 Calls to gcamJacobianTerm 612 tmin = 23.9757 Calls to gcamSmoothnessTerm 612 tmin = 6.49982 Calls to gcamComputeGradient 612 tmin = 101.815 Calls to gcamComputeMetricProperties 6059 tmin = 28.2795 mri_ca_register took 4 hours, 3 minutes and 32 seconds. #VMPC# mri_ca_register VmPeak 2022876 FSRUNTIME@ mri_ca_register 4.0588 hours 1 threads @#@FSTIME 2026:03:26:16:31:37 mri_ca_register N 9 e 14611.97 S 542.47 U 12422.85 P 88% M 1330648 F 0 R 998917 W 0 c 207100 w 3 I 0 O 64368 L 11.28 11.79 12.09 @#@FSLOADPOST 2026:03:26:20:35:09 mri_ca_register N 9 13.12 12.98 13.04 #-------------------------------------- #@# SubCort Seg 2026年 03月 26日星期四 20:35:09 CST mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /usr/local/freesurfer/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz sysname Linux hostname laihan-VirtualBox machine x86_64 setenv SUBJECTS_DIR /home/laihan/Desktop/7T_fs2025 cd /home/laihan/Desktop/7T_fs2025/LH2024_173/mri mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /usr/local/freesurfer/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz relabeling unlikely voxels with window_size = 9 and prior threshold 0.30 using Gibbs prior factor = 0.500 renormalizing sequences with structure alignment, equivalent to: -renormalize -renormalize_mean 0.500 -regularize 0.500 == Number of threads available to for OpenMP = 1 == reading 1 input volumes reading classifier array from /usr/local/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 7.19 Atlas used for the 3D morph was /usr/local/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 = 0.09218 (16) Left_Lateral_Ventricle (4): linear fit = 0.67 x + 0.0 (665 voxels, overlap=0.587) Left_Lateral_Ventricle (4): linear fit = 0.67 x + 0.0 (665 voxels, peak = 13), gca=13.3 gca peak = 0.20380 (13) mri peak = 0.10526 ( 5) Right_Lateral_Ventricle (43): linear fit = 0.31 x + 0.0 (422 voxels, overlap=0.447) Right_Lateral_Ventricle (43): linear fit = 0.40 x + 0.0 (422 voxels, peak = 4), gca=5.2 gca peak = 0.26283 (96) mri peak = 0.08565 (88) Right_Pallidum (52): linear fit = 0.90 x + 0.0 (432 voxels, overlap=1.008) Right_Pallidum (52): linear fit = 0.90 x + 0.0 (432 voxels, peak = 87), gca=86.9 gca peak = 0.15814 (97) mri peak = 0.14480 (98) Left_Pallidum (13): linear fit = 1.02 x + 0.0 (658 voxels, overlap=0.951) Left_Pallidum (13): linear fit = 1.02 x + 0.0 (658 voxels, peak = 99), gca=99.4 gca peak = 0.27624 (56) mri peak = 0.07824 (63) Right_Hippocampus (53): linear fit = 1.12 x + 0.0 (819 voxels, overlap=0.762) Right_Hippocampus (53): linear fit = 1.12 x + 0.0 (819 voxels, peak = 62), gca=62.4 gca peak = 0.28723 (59) mri peak = 0.08867 (68) Left_Hippocampus (17): linear fit = 1.13 x + 0.0 (806 voxels, overlap=0.770) Left_Hippocampus (17): linear fit = 1.13 x + 0.0 (806 voxels, peak = 67), gca=67.0 gca peak = 0.07623 (103) mri peak = 0.08265 (100) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (43243 voxels, overlap=0.799) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (43243 voxels, peak = 102), gca=102.5 gca peak = 0.07837 (105) mri peak = 0.08483 (103) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (40011 voxels, overlap=0.755) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (40011 voxels, peak = 104), gca=104.5 gca peak = 0.10165 (58) mri peak = 0.03753 (63) Left_Cerebral_Cortex (3): linear fit = 1.10 x + 0.0 (16148 voxels, overlap=0.803) Left_Cerebral_Cortex (3): linear fit = 1.10 x + 0.0 (16148 voxels, peak = 64), gca=63.5 gca peak = 0.11113 (58) mri peak = 0.03841 (62) Right_Cerebral_Cortex (42): linear fit = 1.04 x + 0.0 (14667 voxels, overlap=0.947) Right_Cerebral_Cortex (42): linear fit = 1.04 x + 0.0 (14667 voxels, peak = 61), gca=60.6 gca peak = 0.27796 (67) mri peak = 0.15590 (75) Right_Caudate (50): linear fit = 1.11 x + 0.0 (931 voxels, overlap=0.229) Right_Caudate (50): linear fit = 1.11 x + 0.0 (931 voxels, peak = 74), gca=74.0 gca peak = 0.14473 (69) mri peak = 0.14267 (79) Left_Caudate (11): linear fit = 1.09 x + 0.0 (749 voxels, overlap=0.568) Left_Caudate (11): linear fit = 1.09 x + 0.0 (749 voxels, peak = 75), gca=74.9 gca peak = 0.14301 (56) mri peak = 0.04490 (61) Left_Cerebellum_Cortex (8): linear fit = 1.04 x + 0.0 (9741 voxels, overlap=1.000) Left_Cerebellum_Cortex (8): linear fit = 1.04 x + 0.0 (9741 voxels, peak = 59), gca=58.5 gca peak = 0.14610 (55) mri peak = 0.04335 (59) Right_Cerebellum_Cortex (47): linear fit = 1.04 x + 0.0 (6705 voxels, overlap=0.997) Right_Cerebellum_Cortex (47): linear fit = 1.04 x + 0.0 (6705 voxels, peak = 57), gca=57.5 gca peak = 0.16309 (85) mri peak = 0.08590 (99) Left_Cerebellum_White_Matter (7): linear fit = 1.16 x + 0.0 (4387 voxels, overlap=0.003) Left_Cerebellum_White_Matter (7): linear fit = 1.16 x + 0.0 (4387 voxels, peak = 99), gca=99.0 gca peak = 0.15172 (84) mri peak = 0.06176 (92) Right_Cerebellum_White_Matter (46): linear fit = 1.13 x + 0.0 (9039 voxels, overlap=0.004) Right_Cerebellum_White_Matter (46): linear fit = 1.13 x + 0.0 (9039 voxels, peak = 95), gca=95.3 gca peak = 0.30461 (58) mri peak = 0.11372 (69) Left_Amygdala (18): linear fit = 1.18 x + 0.0 (404 voxels, overlap=0.056) Left_Amygdala (18): linear fit = 1.18 x + 0.0 (404 voxels, peak = 69), gca=68.7 gca peak = 0.32293 (57) mri peak = 0.09524 (63) Right_Amygdala (54): linear fit = 1.10 x + 0.0 (486 voxels, overlap=0.985) Right_Amygdala (54): linear fit = 1.10 x + 0.0 (486 voxels, peak = 62), gca=62.4 gca peak = 0.11083 (90) mri peak = 0.08065 (89) Left_Thalamus (10): linear fit = 0.99 x + 0.0 (5215 voxels, overlap=0.992) Left_Thalamus (10): linear fit = 0.99 x + 0.0 (5215 voxels, peak = 89), gca=88.7 gca peak = 0.11393 (83) mri peak = 0.08045 (86) Right_Thalamus (49): linear fit = 1.04 x + 0.0 (5583 voxels, overlap=0.865) Right_Thalamus (49): linear fit = 1.04 x + 0.0 (5583 voxels, peak = 87), gca=86.7 gca peak = 0.08575 (81) mri peak = 0.13056 (84) Left_Putamen (12): linear fit = 1.03 x + 0.0 (2474 voxels, overlap=0.652) Left_Putamen (12): linear fit = 1.03 x + 0.0 (2474 voxels, peak = 84), gca=83.8 gca peak = 0.08618 (78) mri peak = 0.11064 (82) Right_Putamen (51): linear fit = 1.03 x + 0.0 (1658 voxels, overlap=0.706) Right_Putamen (51): linear fit = 1.03 x + 0.0 (1658 voxels, peak = 81), gca=80.7 gca peak = 0.08005 (78) mri peak = 0.08766 (87) Brain_Stem (16): linear fit = 1.07 x + 0.0 (10064 voxels, overlap=0.491) Brain_Stem (16): linear fit = 1.07 x + 0.0 (10064 voxels, peak = 83), gca=83.1 gca peak = 0.12854 (88) mri peak = 0.09515 (92) Right_VentralDC (60): linear fit = 1.07 x + 0.0 (1690 voxels, overlap=0.779) Right_VentralDC (60): linear fit = 1.07 x + 0.0 (1690 voxels, peak = 94), gca=93.7 gca peak = 0.15703 (87) mri peak = 0.09195 (91) Left_VentralDC (28): linear fit = 1.03 x + 0.0 (1717 voxels, overlap=0.894) Left_VentralDC (28): linear fit = 1.03 x + 0.0 (1717 voxels, peak = 90), gca=90.0 gca peak = 0.17522 (25) mri peak = 0.14119 (17) gca peak = 0.17113 (14) mri peak = 0.11959 ( 4) Fourth_Ventricle (15): linear fit = 0.19 x + 0.0 (206 voxels, overlap=0.334) Fourth_Ventricle (15): linear fit = 0.19 x + 0.0 (206 voxels, peak = 3), gca=2.7 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.16627 (28) gca peak Third_Ventricle = 0.17522 (25) gca peak Fourth_Ventricle = 0.17113 (14) gca peak CSF = 0.20346 (36) gca peak Left_Accumbens_area = 0.70646 (62) gca peak Left_undetermined = 1.00000 (28) gca peak Left_vessel = 0.89917 (53) gca peak Left_choroid_plexus = 0.11689 (35) gca peak Right_Inf_Lat_Vent = 0.25504 (23) gca peak Right_Accumbens_area = 0.31650 (65) gca peak Right_vessel = 0.77268 (52) gca peak Right_choroid_plexus = 0.13275 (38) gca peak Fifth_Ventricle = 0.60973 (33) gca peak WM_hypointensities = 0.11013 (77) gca peak non_WM_hypointensities = 0.11354 (41) gca peak Optic_Chiasm = 0.51646 (76) not using caudate to estimate GM means estimating mean gm scale to be 1.11 x + 0.0 estimating mean wm scale to be 1.00 x + 0.0 estimating mean csf scale to be 0.53 x + 0.0 Left_Pallidum too bright - rescaling by 1.010 (from 1.025) to 100.4 (was 99.4) saving intensity scales to aseg.auto_noCCseg.label_intensities.txt renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.20709 (12) mri peak = 0.09218 (16) Left_Lateral_Ventricle (4): linear fit = 1.04 x + 0.0 (665 voxels, overlap=0.980) Left_Lateral_Ventricle (4): linear fit = 1.04 x + 0.0 (665 voxels, peak = 13), gca=12.5 gca peak = 0.29334 ( 5) mri peak = 0.10526 ( 5) Right_Lateral_Ventricle (43): linear fit = 0.88 x + 0.0 (422 voxels, overlap=0.990) Right_Lateral_Ventricle (43): linear fit = 0.88 x + 0.0 (422 voxels, peak = 4), gca=4.4 gca peak = 0.27659 (86) mri peak = 0.08565 (88) Right_Pallidum (52): linear fit = 0.98 x + 0.0 (432 voxels, overlap=1.008) Right_Pallidum (52): linear fit = 0.98 x + 0.0 (432 voxels, peak = 84), gca=83.8 gca peak = 0.17576 (98) mri peak = 0.14480 (98) Left_Pallidum (13): linear fit = 0.99 x + 0.0 (658 voxels, overlap=1.000) Left_Pallidum (13): linear fit = 0.99 x + 0.0 (658 voxels, peak = 97), gca=96.5 gca peak = 0.27572 (62) mri peak = 0.07824 (63) Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (819 voxels, overlap=1.006) Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (819 voxels, peak = 62), gca=62.0 gca peak = 0.32829 (64) mri peak = 0.08867 (68) Left_Hippocampus (17): linear fit = 0.99 x + 0.0 (806 voxels, overlap=1.006) Left_Hippocampus (17): linear fit = 0.99 x + 0.0 (806 voxels, peak = 63), gca=63.0 gca peak = 0.08109 (103) mri peak = 0.08265 (100) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (43243 voxels, overlap=0.781) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (43243 voxels, peak = 102), gca=102.5 gca peak = 0.08124 (104) mri peak = 0.08483 (103) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (40011 voxels, overlap=0.734) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (40011 voxels, peak = 103), gca=103.5 gca peak = 0.08785 (63) mri peak = 0.03753 (63) Left_Cerebral_Cortex (3): linear fit = 0.99 x + 0.0 (16148 voxels, overlap=0.942) Left_Cerebral_Cortex (3): linear fit = 0.99 x + 0.0 (16148 voxels, peak = 62), gca=62.1 gca peak = 0.10923 (61) mri peak = 0.03841 (62) Right_Cerebral_Cortex (42): linear fit = 1.02 x + 0.0 (14667 voxels, overlap=0.972) Right_Cerebral_Cortex (42): linear fit = 1.02 x + 0.0 (14667 voxels, peak = 63), gca=62.5 gca peak = 0.25483 (74) mri peak = 0.15590 (75) Right_Caudate (50): linear fit = 1.00 x + 0.0 (931 voxels, overlap=0.998) Right_Caudate (50): linear fit = 1.00 x + 0.0 (931 voxels, peak = 74), gca=74.0 gca peak = 0.12271 (74) mri peak = 0.14267 (79) Left_Caudate (11): linear fit = 0.99 x + 0.0 (749 voxels, overlap=0.847) Left_Caudate (11): linear fit = 0.99 x + 0.0 (749 voxels, peak = 73), gca=72.9 gca peak = 0.13218 (58) mri peak = 0.04490 (61) Left_Cerebellum_Cortex (8): linear fit = 1.02 x + 0.0 (9741 voxels, overlap=1.000) Left_Cerebellum_Cortex (8): linear fit = 1.02 x + 0.0 (9741 voxels, peak = 59), gca=59.4 gca peak = 0.15502 (58) mri peak = 0.04335 (59) Right_Cerebellum_Cortex (47): linear fit = 1.02 x + 0.0 (6705 voxels, overlap=1.000) Right_Cerebellum_Cortex (47): linear fit = 1.02 x + 0.0 (6705 voxels, peak = 59), gca=59.4 gca peak = 0.14856 (99) mri peak = 0.08590 (99) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (4387 voxels, overlap=0.955) Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (4387 voxels, peak = 99), gca=99.5 gca peak = 0.14519 (96) mri peak = 0.06176 (92) Right_Cerebellum_White_Matter (46): linear fit = 1.00 x + 0.0 (9039 voxels, overlap=0.896) Right_Cerebellum_White_Matter (46): linear fit = 1.00 x + 0.0 (9039 voxels, peak = 96), gca=95.5 gca peak = 0.26950 (69) mri peak = 0.11372 (69) Left_Amygdala (18): linear fit = 1.01 x + 0.0 (404 voxels, overlap=1.008) Left_Amygdala (18): linear fit = 1.01 x + 0.0 (404 voxels, peak = 70), gca=70.0 gca peak = 0.33149 (63) mri peak = 0.09524 (63) Right_Amygdala (54): linear fit = 0.99 x + 0.0 (486 voxels, overlap=1.025) Right_Amygdala (54): linear fit = 0.99 x + 0.0 (486 voxels, peak = 62), gca=62.1 gca peak = 0.10780 (87) mri peak = 0.08065 (89) Left_Thalamus (10): linear fit = 1.00 x + 0.0 (5215 voxels, overlap=0.997) Left_Thalamus (10): linear fit = 1.00 x + 0.0 (5215 voxels, peak = 87), gca=86.6 gca peak = 0.09468 (86) mri peak = 0.08045 (86) Right_Thalamus (49): linear fit = 0.99 x + 0.0 (5583 voxels, overlap=0.961) Right_Thalamus (49): linear fit = 0.99 x + 0.0 (5583 voxels, peak = 85), gca=84.7 gca peak = 0.08071 (86) mri peak = 0.13056 (84) Left_Putamen (12): linear fit = 1.01 x + 0.0 (2474 voxels, overlap=0.791) Left_Putamen (12): linear fit = 1.01 x + 0.0 (2474 voxels, peak = 87), gca=87.3 gca peak = 0.09160 (80) mri peak = 0.11064 (82) Right_Putamen (51): linear fit = 0.99 x + 0.0 (1658 voxels, overlap=0.791) Right_Putamen (51): linear fit = 0.99 x + 0.0 (1658 voxels, peak = 79), gca=78.8 gca peak = 0.07382 (86) mri peak = 0.08766 (87) Brain_Stem (16): linear fit = 1.02 x + 0.0 (10064 voxels, overlap=0.738) Brain_Stem (16): linear fit = 1.02 x + 0.0 (10064 voxels, peak = 88), gca=88.2 gca peak = 0.12240 (95) mri peak = 0.09515 (92) Right_VentralDC (60): linear fit = 0.98 x + 0.0 (1690 voxels, overlap=0.857) Right_VentralDC (60): linear fit = 0.98 x + 0.0 (1690 voxels, peak = 93), gca=92.6 gca peak = 0.15407 (90) mri peak = 0.09195 (91) Left_VentralDC (28): linear fit = 1.00 x + 0.0 (1717 voxels, overlap=0.945) Left_VentralDC (28): linear fit = 1.00 x + 0.0 (1717 voxels, peak = 90), gca=90.0 gca peak = 0.25593 (13) mri peak = 0.14119 (17) gca peak = 0.30855 ( 8) mri peak = 0.11959 ( 4) Fourth_Ventricle (15): linear fit = 0.38 x + 0.0 (206 voxels, overlap=0.699) Fourth_Ventricle (15): linear fit = 0.38 x + 0.0 (206 voxels, peak = 3), gca=3.1 gca peak Unknown = 0.94777 ( 0) gca peak Left_Inf_Lat_Vent = 0.20602 (32) gca peak Third_Ventricle = 0.25593 (13) gca peak Fourth_Ventricle = 0.30855 ( 8) gca peak CSF = 0.30162 (19) gca peak Left_Accumbens_area = 0.55446 (67) gca peak Left_undetermined = 1.00000 (28) gca peak Left_vessel = 0.89837 (53) gca peak Left_choroid_plexus = 0.11689 (35) gca peak Right_Inf_Lat_Vent = 0.25012 (26) gca peak Right_Accumbens_area = 0.39742 (71) gca peak Right_vessel = 0.77268 (52) gca peak Right_choroid_plexus = 0.13275 (38) gca peak Fifth_Ventricle = 0.91795 (17) gca peak WM_hypointensities = 0.11530 (76) gca peak non_WM_hypointensities = 0.08214 (41) gca peak Optic_Chiasm = 0.51674 (76) not using caudate to estimate GM means estimating mean gm scale to be 1.00 x + 0.0 estimating mean wm scale to be 1.00 x + 0.0 estimating mean csf scale to be 0.96 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 100265 voxels changed in iteration 0 of unlikely voxel relabeling 276 voxels changed in iteration 1 of unlikely voxel relabeling 23 voxels changed in iteration 2 of unlikely voxel relabeling 0 voxels changed in iteration 3 of unlikely voxel relabeling 64795 gm and wm labels changed (%20 to gray, %80 to white out of all changed labels) 535 hippocampal voxels changed. 0 amygdala voxels changed. Reclassifying using Gibbs Priors pass 1: 91129 changed. image ll: -2.148, PF=0.500 pass 2: 24559 changed. image ll: -2.147, PF=0.500 pass 3: 6719 changed. pass 4: 2340 changed. 67856 voxels changed in iteration 0 of unlikely voxel relabeling 449 voxels changed in iteration 1 of unlikely voxel relabeling 25 voxels changed in iteration 2 of unlikely voxel relabeling 11 voxels changed in iteration 3 of unlikely voxel relabeling 0 voxels changed in iteration 4 of unlikely voxel relabeling 8532 voxels changed in iteration 0 of unlikely voxel relabeling 166 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 6587 voxels changed in iteration 0 of unlikely voxel relabeling 89 voxels changed in iteration 1 of unlikely voxel relabeling 15 voxels changed in iteration 2 of unlikely voxel relabeling 0 voxels changed in iteration 3 of unlikely voxel relabeling 5954 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 5695 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 0 with volume 106 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 1 with volume 165 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 4 with volume 4204 above threshold 100 - not erasing !!!!!!!!!! !!!!!!!!! ventricle segment 1 with volume 145 above threshold 100 - not erasing !!!!!!!!!! writing labeled volume to aseg.auto_noCCseg.mgz mri_ca_label utimesec 4276.806673 mri_ca_label stimesec 168.258421 mri_ca_label ru_maxrss 2027956 mri_ca_label ru_ixrss 0 mri_ca_label ru_idrss 0 mri_ca_label ru_isrss 0 mri_ca_label ru_minflt 1502398 mri_ca_label ru_majflt 91 mri_ca_label ru_nswap 0 mri_ca_label ru_inblock 2760 mri_ca_label ru_oublock 712 mri_ca_label ru_msgsnd 0 mri_ca_label ru_msgrcv 0 mri_ca_label ru_nsignals 0 mri_ca_label ru_nvcsw 112 mri_ca_label ru_nivcsw 61186 mri_ca_label took 79 minutes and 33 seconds. mri_ca_label done @#@FSTIME 2026:03:26:20:35:10 mri_ca_label N 10 e 4773.40 S 168.62 U 4276.80 P 93% M 2027956 F 101 R 1502422 W 0 c 61198 w 120 I 2888 O 712 L 13.12 12.98 13.04 @#@FSLOADPOST 2026:03:26:21:54:44 mri_ca_label N 10 13.76 13.34 12.87 #-------------------------------------- #@# CC Seg 2026年 03月 26日星期四 21:54:44 CST mri_cc -aseg aseg.auto_noCCseg.mgz -o aseg.auto.mgz -lta /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/transforms/cc_up.lta LH2024_173 will read input aseg from aseg.auto_noCCseg.mgz writing aseg with cc labels to aseg.auto.mgz will write lta as /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/transforms/cc_up.lta reading aseg from /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/aseg.auto_noCCseg.mgz reading norm from /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/norm.mgz 35623 voxels in left wm, 48729 in right wm, xrange [123, 132] searching rotation angles z=[-5 9], y=[-8 6] searching scale 1 Z rot -4.8 searching scale 1 Z rot -4.5 searching scale 1 Z rot -4.3 searching scale 1 Z rot -4.0 searching scale 1 Z rot -3.8 searching scale 1 Z rot -3.5 searching scale 1 Z rot -3.3 searching scale 1 Z rot -3.0 searching scale 1 Z rot -2.8 searching scale 1 Z rot -2.5 searching scale 1 Z rot -2.3 searching scale 1 Z rot -2.0 searching scale 1 Z rot -1.8 searching scale 1 Z rot -1.5 searching scale 1 Z rot -1.3 searching scale 1 Z rot -1.0 searching scale 1 Z rot -0.8 searching scale 1 Z rot -0.5 searching scale 1 Z rot -0.3 searching scale 1 Z rot -0.0 searching scale 1 Z rot 0.2 searching scale 1 Z rot 0.5 searching scale 1 Z rot 0.7 searching scale 1 Z rot 1.0 searching scale 1 Z rot 1.2 searching scale 1 Z rot 1.5 searching scale 1 Z rot 1.7 searching scale 1 Z rot 2.0 searching scale 1 Z rot 2.2 searching scale 1 Z rot 2.5 searching scale 1 Z rot 2.7 searching scale 1 Z rot 3.0 searching scale 1 Z rot 3.2 searching scale 1 Z rot 3.5 searching scale 1 Z rot 3.7 searching scale 1 Z rot 4.0 searching scale 1 Z rot 4.2 searching scale 1 Z rot 4.5 searching scale 1 Z rot 4.7 searching scale 1 Z rot 5.0 searching scale 1 Z rot 5.2 searching scale 1 Z rot 5.5 searching scale 1 Z rot 5.7 searching scale 1 Z rot 6.0 searching scale 1 Z rot 6.2 searching scale 1 Z rot 6.5 searching scale 1 Z rot 6.7 searching scale 1 Z rot 7.0 searching scale 1 Z rot 7.2 searching scale 1 Z rot 7.5 searching scale 1 Z rot 7.7 searching scale 1 Z rot 8.0 searching scale 1 Z rot 8.2 searching scale 1 Z rot 8.5 searching scale 1 Z rot 8.7 searching scale 1 Z rot 9.0 global minimum found at slice 127.6, rotations (-1.35, 2.24) final transformation (x=127.6, yr=-1.350, zr=2.240): 0.99896 -0.03908 -0.02354 7.00301; 0.03907 0.99924 -0.00092 24.19247; 0.02356 0.00000 0.99972 16.02506; 0.00000 0.00000 0.00000 1.00000; updating x range to be [125, 130] in xformed coordinates best xformed slice 127 min_x_fornix = 146 min_x_fornix = 148 WARNING: min_x_fornix not set min_x_fornix = 159 min_x_fornix = 136 cc center is found at 127 99 109 eigenvectors: -0.00005 -0.00205 1.00000; -0.06568 -0.99784 -0.00205; 0.99784 -0.06568 -0.00008; writing aseg with callosum to /home/laihan/Desktop/7T_fs2025/LH2024_173/mri/aseg.auto.mgz... corpus callosum segmentation took 1.2 minutes #VMPC# mri_cc VmPeak 440400 mri_cc done @#@FSTIME 2026:03:26:21:54:44 mri_cc N 7 e 72.57 S 5.14 U 62.96 P 93% M 345584 F 0 R 277581 W 0 c 1329 w 9 I 2512 O 696 L 13.76 13.34 12.87 @#@FSLOADPOST 2026:03:26:21:55:57 mri_cc N 7 13.96 13.52 12.97 #-------------------------------------- #@# Merge ASeg 2026年 03月 26日星期四 21:55:57 CST cp aseg.auto.mgz aseg.presurf.mgz #-------------------------------------------- #@# Intensity Normalization2 2026年 03月 26日星期四 21:55:57 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/mri mri_normalize -seed 1234 -mprage -aseg aseg.presurf.mgz -mask brainmask.mgz norm.mgz brain.mgz setting seed for random number genererator to 1234 assuming input volume is MGH (Van der Kouwe) MP-RAGE using segmentation for initial intensity normalization using MR volume brainmask.mgz to mask input volume... reading mri_src from norm.mgz... Reading aseg aseg.presurf.mgz aseg read with width 256 (src width 256) ************** resampling aseg to account for mismatch with source image *************** normalizing image... NOT doing gentle normalization with control points/label processing with aseg removing outliers in the aseg WM... 1161 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 109 gm peak at 67 (67), valley at 20 (20) csf peak at 33, setting threshold to 55 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... --------------------------------- 3d normalization pass 2 of 2 white matter peak found at 110 white matter peak found at 110 gm peak at 66 (66), valley at 20 (20) csf peak at 34, setting threshold to 55 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Done iterating --------------------------------- writing output to brain.mgz 3D bias adjustment took 4 minutes and 8 seconds. @#@FSTIME 2026:03:26:21:55:59 mri_normalize N 9 e 259.73 S 11.98 U 235.18 P 95% M 1199624 F 0 R 664979 W 0 c 2686 w 2 I 2392 O 2304 L 13.96 13.52 12.97 @#@FSLOADPOST 2026:03:26:22:00:18 mri_normalize N 9 13.46 13.34 13.03 #-------------------------------------------- #@# Mask BFS 2026年 03月 26日星期四 22:00:19 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/mri mri_mask -T 5 brain.mgz brainmask.mgz brain.finalsurfs.mgz threshold mask volume at 5 DoAbs = 0 Found 1422919 voxels in mask (pct= 8.48) maskval=0, outval=0 Writing masked volume to brain.finalsurfs.mgz...done. @#@FSTIME 2026:03:26:22:00:19 mri_mask N 5 e 2.59 S 0.24 U 1.96 P 85% M 74192 F 0 R 17264 W 0 c 40 w 1 I 0 O 2296 L 13.46 13.34 13.03 @#@FSLOADPOST 2026:03:26:22:00:21 mri_mask N 5 13.19 13.28 13.01 cp brain.finalsurfs.mgz brain.finalsurfs.manedit.mgz #-------------------------------------------- #@# WM Segmentation 2026年 03月 26日星期四 22:00:22 CST AntsDenoiseImageFs -i brain.mgz -o antsdn.brain.mgz @#@FSTIME 2026:03:26:22:00:22 AntsDenoiseImageFs N 4 e 61.40 S 2.66 U 56.38 P 96% M 350900 F 0 R 86505 W 0 c 639 w 1 I 0 O 2328 L 13.19 13.28 13.01 @#@FSLOADPOST 2026:03:26:22:01:23 AntsDenoiseImageFs N 4 13.11 13.23 13.01 mri_segment -wsizemm 13 -mprage antsdn.brain.mgz wm.seg.mgz wsizemm = 13, voxres = 1, wsize = 13 Widening wm low from 89 to 79 assuming input volume is MGH (Van der Kouwe) MP-RAGE wm mean: 110 wsize: 13 wm low: 79 wm hi: 125 gray low: 30 gray hi: 99 Doing initial trinary intensity segmentation MRIintensitySegmentation() wm_low=79, wm_hi=125, gray_hi=99 white = 381498, nonwhite = 16086170, ambig = 309548, nmask = 0 Using local statistics to label ambiguous voxels Autodetecting stats Computing class statistics for intensity windows... CCS WM (103.0): 103.7 +- 5.3 [79.0 --> 125.0] CCS GM (70.0) : 68.6 +- 10.8 [30.0 --> 95.0] white_mean 103.666 white_sigma 5.31348 gray_mean 68.5826 gray_sigma 10.84 setting bottom of white matter range wm_low to 79.4 setting top of gray matter range gray_hi to 90.3 wm_low 79.4226 wm_hi 125 gray_low 30 gray_hi 90.2626 Redoing initial intensity segmentation... MRIintensitySegmentation() wm_low=79.4226, wm_hi=125, gray_hi=90.2626 white = 511826, nonwhite = 16102795, ambig = 162595, nmask = 0 Recomputing local statistics to label ambiguous voxels... wm_low 79.4226 wm_hi 125 gray_low 30 gray_hi 90.2626 using local geometry to label remaining ambiguous voxels... polvwsize = 5, polvlen = 3, gray_hi = 90.2626, wm_low = 79.4226 MRIcpolvMedianCurveSegment(): wsize=5, len=3, gmhi=90.2626, wmlow=79.4226 157212 voxels processed (0.94%) 69479 voxels white (0.41%) 87733 voxels non-white (0.52%) Reclassifying voxels using Gaussian border classifier niter=1 MRIreclassify(): wm_low=74.4226, gray_hi=90.2626, wsize=13 251493 voxels tested (1.50%) 64498 voxels changed (0.38%) 57403 multi-scale searches (0.34%) Recovering bright white MRIrecoverBrightWhite() wm_low 79.4226 wm_hi 125 slack 5.31348 pct_thresh 0.33 intensity_thresh 130.313 nvox_thresh 8.58 604 voxels tested (0.00%) 434 voxels changed (0.00%) removing voxels with positive offset direction... MRIremoveWrongDirection() wsize=3, lowthr=74.4226, hithr=90.2626 smoothing input volume with sigma = 0.250 107970 voxels tested (0.64%) 22494 voxels changed (0.13%) thicken = 1 removing 1-dimensional structures... MRIremove1dStructures(): max_iter=10000, thresh=2, WM_MIN_VAL=5 7771 sparsely connected voxels removed in 1 iterations thickening thin strands.... thickness 4 nsegments 20 wm_hi 125 3168 diagonally connected voxels added... MRIthickenThinWMStrands(): thickness=4, nsegments=20 20 segments, 5547 filled MRIfindBrightNonWM(): 1731 bright non-wm voxels segmented. MRIfilterMorphology() WM_MIN_VAL=5, DIAGONAL_FILL=230 white matter segmentation took 2.2 minutes writing output to wm.seg.mgz... @#@FSTIME 2026:03:26:22:01:24 mri_segment N 5 e 130.88 S 4.47 U 120.09 P 95% M 149052 F 0 R 290148 W 0 c 1395 w 1 I 0 O 968 L 13.11 13.23 13.01 @#@FSLOADPOST 2026:03:26:22:03:35 mri_segment N 5 12.12 12.81 12.89 mri_edit_wm_with_aseg -keep-in wm.seg.mgz brain.mgz aseg.presurf.mgz wm.asegedit.mgz 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 0.83 minutes reading wm segmentation from wm.seg.mgz... 0 voxels added to wm to prevent paths from MTL structures to cortex 2704 additional wm voxels added 0 additional wm voxels added SEG EDIT: 33000 voxels turned on, 34624 voxels turned off. propagating editing to output volume from wm.seg.mgz writing edited volume to wm.asegedit.mgz.... @#@FSTIME 2026:03:26:22:03:35 mri_edit_wm_with_aseg N 5 e 49.84 S 2.41 U 44.75 P 94% M 462260 F 0 R 317280 W 0 c 559 w 1 I 0 O 912 L 12.12 12.81 12.89 @#@FSLOADPOST 2026:03:26:22:04:25 mri_edit_wm_with_aseg N 5 12.26 12.74 12.85 mri_pretess wm.asegedit.mgz wm norm.mgz wm.mgz Iteration Number : 1 pass 1 (xy+): 26 found - 26 modified | TOTAL: 26 pass 2 (xy+): 0 found - 26 modified | TOTAL: 26 pass 1 (xy-): 32 found - 32 modified | TOTAL: 58 pass 2 (xy-): 0 found - 32 modified | TOTAL: 58 pass 1 (yz+): 37 found - 37 modified | TOTAL: 95 pass 2 (yz+): 0 found - 37 modified | TOTAL: 95 pass 1 (yz-): 28 found - 28 modified | TOTAL: 123 pass 2 (yz-): 0 found - 28 modified | TOTAL: 123 pass 1 (xz+): 33 found - 33 modified | TOTAL: 156 pass 2 (xz+): 0 found - 33 modified | TOTAL: 156 pass 1 (xz-): 21 found - 21 modified | TOTAL: 177 pass 2 (xz-): 0 found - 21 modified | TOTAL: 177 Iteration Number : 1 pass 1 (+++): 50 found - 50 modified | TOTAL: 50 pass 2 (+++): 0 found - 50 modified | TOTAL: 50 pass 1 (+++): 39 found - 39 modified | TOTAL: 89 pass 2 (+++): 0 found - 39 modified | TOTAL: 89 pass 1 (+++): 57 found - 57 modified | TOTAL: 146 pass 2 (+++): 0 found - 57 modified | TOTAL: 146 pass 1 (+++): 46 found - 46 modified | TOTAL: 192 pass 2 (+++): 0 found - 46 modified | TOTAL: 192 Iteration Number : 1 pass 1 (++): 76 found - 76 modified | TOTAL: 76 pass 2 (++): 0 found - 76 modified | TOTAL: 76 pass 1 (+-): 119 found - 119 modified | TOTAL: 195 pass 2 (+-): 0 found - 119 modified | TOTAL: 195 pass 1 (--): 150 found - 150 modified | TOTAL: 345 pass 2 (--): 0 found - 150 modified | TOTAL: 345 pass 1 (-+): 155 found - 155 modified | TOTAL: 500 pass 2 (-+): 0 found - 155 modified | TOTAL: 500 Iteration Number : 2 pass 1 (xy+): 12 found - 12 modified | TOTAL: 12 pass 2 (xy+): 0 found - 12 modified | TOTAL: 12 pass 1 (xy-): 13 found - 13 modified | TOTAL: 25 pass 2 (xy-): 0 found - 13 modified | TOTAL: 25 pass 1 (yz+): 11 found - 11 modified | TOTAL: 36 pass 2 (yz+): 0 found - 11 modified | TOTAL: 36 pass 1 (yz-): 13 found - 13 modified | TOTAL: 49 pass 2 (yz-): 0 found - 13 modified | TOTAL: 49 pass 1 (xz+): 11 found - 11 modified | TOTAL: 60 pass 2 (xz+): 0 found - 11 modified | TOTAL: 60 pass 1 (xz-): 11 found - 11 modified | TOTAL: 71 pass 2 (xz-): 0 found - 11 modified | TOTAL: 71 Iteration Number : 2 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 7 found - 7 modified | TOTAL: 7 pass 2 (+++): 0 found - 7 modified | TOTAL: 7 pass 1 (+++): 8 found - 8 modified | TOTAL: 15 pass 2 (+++): 0 found - 8 modified | TOTAL: 15 Iteration Number : 2 pass 1 (++): 4 found - 4 modified | TOTAL: 4 pass 2 (++): 0 found - 4 modified | TOTAL: 4 pass 1 (+-): 7 found - 7 modified | TOTAL: 11 pass 2 (+-): 0 found - 7 modified | TOTAL: 11 pass 1 (--): 4 found - 4 modified | TOTAL: 15 pass 2 (--): 0 found - 4 modified | TOTAL: 15 pass 1 (-+): 3 found - 3 modified | TOTAL: 18 pass 2 (-+): 0 found - 3 modified | TOTAL: 18 Iteration Number : 3 pass 1 (xy+): 1 found - 1 modified | TOTAL: 1 pass 2 (xy+): 0 found - 1 modified | TOTAL: 1 pass 1 (xy-): 0 found - 0 modified | TOTAL: 1 pass 1 (yz+): 0 found - 0 modified | TOTAL: 1 pass 1 (yz-): 0 found - 0 modified | TOTAL: 1 pass 1 (xz+): 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 (+++): 2 found - 2 modified | TOTAL: 2 pass 2 (+++): 0 found - 2 modified | TOTAL: 2 pass 1 (+++): 0 found - 0 modified | TOTAL: 2 pass 1 (+++): 0 found - 0 modified | TOTAL: 2 Iteration Number : 3 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 Iteration Number : 4 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 0 found - 0 modified | TOTAL: 0 pass 1 (yz+): 0 found - 0 modified | TOTAL: 0 pass 1 (yz-): 0 found - 0 modified | TOTAL: 0 pass 1 (xz+): 1 found - 1 modified | TOTAL: 1 pass 2 (xz+): 0 found - 1 modified | TOTAL: 1 pass 1 (xz-): 0 found - 0 modified | TOTAL: 1 Iteration Number : 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 (--): 1 found - 1 modified | TOTAL: 1 pass 2 (--): 0 found - 1 modified | TOTAL: 1 pass 1 (-+): 0 found - 0 modified | TOTAL: 1 Iteration Number : 5 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 : 5 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 : 5 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 = 978 (out of 588105: 0.166297) binarizing input wm segmentation... Ambiguous edge configurations... mri_pretess done @#@FSTIME 2026:03:26:22:04:25 mri_pretess N 4 e 8.83 S 0.58 U 8.09 P 98% M 56700 F 0 R 12959 W 0 c 62 w 1 I 0 O 912 L 12.26 12.74 12.85 @#@FSLOADPOST 2026:03:26:22:04:34 mri_pretess N 4 11.84 12.64 12.82 #-------------------------------------------- #@# Fill 2026年 03月 26日星期四 22:04:34 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/mri mri_fill -a ../scripts/ponscc.cut.log -xform transforms/talairach.lta -segmentation aseg.presurf.mgz -ctab /usr/local/freesurfer/SubCorticalMassLUT.txt wm.mgz filled.mgz logging cutting plane coordinates to ../scripts/ponscc.cut.log... INFO: Using transforms/talairach.lta and its offset for Talairach volume ... using segmentation aseg.presurf.mgz... done. searching for cutting planes...voxel to talairach voxel transform 1.01466 -0.09464 -0.02429 11.43251; 0.08558 0.98211 0.24558 -36.03990; 0.00052 -0.26125 1.04145 14.73981; 0.00000 0.00000 0.00000 1.00000; reading input volume... wm.mgzvoxel to talairach voxel transform 1.01466 -0.09464 -0.02429 11.43251; 0.08558 0.98211 0.24558 -36.03990; 0.00052 -0.26125 1.04145 14.73981; 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] = 2457 (min = 350, max = 1400), aspect = 1.01 (min = 0.10, max = 0.75) need search nearby using seed (128, 117, 151), TAL = (0.0, 23.0, 11.0) talairach voxel to voxel transform 0.97760 0.09435 0.00055 -7.78426; -0.08005 0.95039 -0.22597 38.49796; -0.02057 0.23836 0.90351 -4.49181; 0.00000 0.00000 0.00000 1.00000; segmentation indicates cc at (128, 117, 151) --> (0.0, 23.0, 11.0) done. filling took 2.9 minutes talairach cc position changed to (0.00, 23.00, 11.00) Erasing brainstem...done. seed_search_size = 9, min_neighbors = 5 search rh wm seed point around talairach space:(18.00, 23.00, 11.00) SRC: (110.87, 106.77, 157.56) search lh wm seed point around talairach space (-18.00, 23.00, 11.00), SRC: (146.07, 103.89, 156.82) compute mri_fill using aseg Erasing Brain Stem and Cerebellum ... Define left and right masks using aseg: Building Voronoi diagram ... Using the Voronoi diagram for separating WM into two hemispheres ... Find the largest connected component for each hemisphere ... Embedding colortable mri_fill done, writing output to filled.mgz... @#@FSTIME 2026:03:26:22:04:34 mri_fill N 10 e 172.44 S 9.66 U 152.65 P 94% M 973764 F 0 R 510440 W 0 c 1971 w 2 I 8 O 296 L 12.02 12.66 12.83 @#@FSLOADPOST 2026:03:26:22:07:27 mri_fill N 10 12.28 12.39 12.68 cp filled.mgz filled.auto.mgz #-------------------------------------------- #@# Tessellate lh 2026年 03月 26日星期四 22:07:27 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/scripts mri_pretess ../mri/filled.mgz 255 ../mri/norm.mgz ../mri/filled-pretess255.mgz Iteration Number : 1 pass 1 (xy+): 4 found - 4 modified | TOTAL: 4 pass 2 (xy+): 0 found - 4 modified | TOTAL: 4 pass 1 (xy-): 10 found - 10 modified | TOTAL: 14 pass 2 (xy-): 0 found - 10 modified | TOTAL: 14 pass 1 (yz+): 13 found - 13 modified | TOTAL: 27 pass 2 (yz+): 0 found - 13 modified | TOTAL: 27 pass 1 (yz-): 11 found - 11 modified | TOTAL: 38 pass 2 (yz-): 0 found - 11 modified | TOTAL: 38 pass 1 (xz+): 4 found - 4 modified | TOTAL: 42 pass 2 (xz+): 0 found - 4 modified | TOTAL: 42 pass 1 (xz-): 4 found - 4 modified | TOTAL: 46 pass 2 (xz-): 0 found - 4 modified | TOTAL: 46 Iteration Number : 1 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 2 found - 2 modified | TOTAL: 2 pass 2 (+++): 0 found - 2 modified | TOTAL: 2 pass 1 (+++): 2 found - 2 modified | TOTAL: 4 pass 2 (+++): 0 found - 2 modified | TOTAL: 4 pass 1 (+++): 0 found - 0 modified | TOTAL: 4 Iteration Number : 1 pass 1 (++): 2 found - 2 modified | TOTAL: 2 pass 2 (++): 0 found - 2 modified | TOTAL: 2 pass 1 (+-): 3 found - 3 modified | TOTAL: 5 pass 2 (+-): 0 found - 3 modified | TOTAL: 5 pass 1 (--): 3 found - 3 modified | TOTAL: 8 pass 2 (--): 0 found - 3 modified | TOTAL: 8 pass 1 (-+): 0 found - 0 modified | TOTAL: 8 Iteration Number : 2 pass 1 (xy+): 1 found - 1 modified | TOTAL: 1 pass 2 (xy+): 0 found - 1 modified | TOTAL: 1 pass 1 (xy-): 0 found - 0 modified | TOTAL: 1 pass 1 (yz+): 2 found - 2 modified | TOTAL: 3 pass 2 (yz+): 0 found - 2 modified | TOTAL: 3 pass 1 (yz-): 0 found - 0 modified | TOTAL: 3 pass 1 (xz+): 0 found - 0 modified | TOTAL: 3 pass 1 (xz-): 0 found - 0 modified | TOTAL: 3 Iteration Number : 2 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 2 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 0 found - 0 modified | TOTAL: 0 pass 1 (yz+): 0 found - 0 modified | TOTAL: 0 pass 1 (yz-): 0 found - 0 modified | TOTAL: 0 pass 1 (xz+): 0 found - 0 modified | TOTAL: 0 pass 1 (xz-): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 Total Number of Modified Voxels = 61 (out of 287128: 0.021245) Ambiguous edge configurations... mri_pretess done @#@FSTIME 2026:03:26:22:07:28 mri_pretess N 4 e 6.67 S 0.64 U 4.69 P 79% M 40332 F 0 R 8801 W 0 c 165 w 1 I 0 O 288 L 12.28 12.39 12.68 @#@FSLOADPOST 2026:03:26:22:07:35 mri_pretess N 4 12.78 12.49 12.71 mri_tessellate ../mri/filled-pretess255.mgz 255 ../surf/lh.orig.nofix 7.4.1 7.4.1 slice 50: 3137 vertices, 3325 faces slice 60: 13017 vertices, 13430 faces slice 70: 27075 vertices, 27617 faces slice 80: 42095 vertices, 42705 faces slice 90: 56992 vertices, 57507 faces slice 100: 70536 vertices, 71075 faces slice 110: 84069 vertices, 84688 faces slice 120: 97271 vertices, 97855 faces slice 130: 109511 vertices, 110107 faces slice 140: 123196 vertices, 123828 faces slice 150: 134890 vertices, 135415 faces slice 160: 143697 vertices, 144195 faces slice 170: 152369 vertices, 152826 faces slice 180: 158759 vertices, 159147 faces slice 190: 162900 vertices, 163216 faces slice 200: 164931 vertices, 165141 faces slice 210: 164942 vertices, 165146 faces slice 220: 164942 vertices, 165146 faces slice 230: 164942 vertices, 165146 faces slice 240: 164942 vertices, 165146 faces slice 250: 164942 vertices, 165146 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 2026:03:26:22:07:36 mri_tessellate N 3 e 3.38 S 0.25 U 2.66 P 86% M 40028 F 0 R 9012 W 0 c 54 w 2 I 8 O 7744 L 12.78 12.49 12.71 @#@FSLOADPOST 2026:03:26:22:07:39 mri_tessellate N 3 12.78 12.49 12.71 rm -f ../mri/filled-pretess255.mgz mris_extract_main_component ../surf/lh.orig.nofix ../surf/lh.orig.nofix counting number of connected components... 164942 voxel in cpt #1: X=-204 [v=164942,e=495438,f=330292] located at (-30.501661, -17.609755, 21.066769) For the whole surface: X=-204 [v=164942,e=495438,f=330292] One single component has been found nothing to do done @#@FSTIME 2026:03:26:22:07:39 mris_extract_main_component N 2 e 2.70 S 0.59 U 2.03 P 97% M 327164 F 0 R 86750 W 0 c 16 w 3 I 8 O 11616 L 12.79 12.50 12.71 @#@FSLOADPOST 2026:03:26:22:07:42 mris_extract_main_component N 2 12.79 12.50 12.71 #-------------------------------------------- #@# Tessellate rh 2026年 03月 26日星期四 22:07:42 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/scripts mri_pretess ../mri/filled.mgz 127 ../mri/norm.mgz ../mri/filled-pretess127.mgz Iteration Number : 1 pass 1 (xy+): 1 found - 1 modified | TOTAL: 1 pass 2 (xy+): 0 found - 1 modified | TOTAL: 1 pass 1 (xy-): 0 found - 0 modified | TOTAL: 1 pass 1 (yz+): 13 found - 13 modified | TOTAL: 14 pass 2 (yz+): 0 found - 13 modified | TOTAL: 14 pass 1 (yz-): 12 found - 12 modified | TOTAL: 26 pass 2 (yz-): 0 found - 12 modified | TOTAL: 26 pass 1 (xz+): 2 found - 2 modified | TOTAL: 28 pass 2 (xz+): 0 found - 2 modified | TOTAL: 28 pass 1 (xz-): 0 found - 0 modified | TOTAL: 28 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 (++): 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 : 2 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 0 found - 0 modified | TOTAL: 0 pass 1 (yz+): 3 found - 3 modified | TOTAL: 3 pass 2 (yz+): 0 found - 3 modified | TOTAL: 3 pass 1 (yz-): 0 found - 0 modified | TOTAL: 3 pass 1 (xz+): 0 found - 0 modified | TOTAL: 3 pass 1 (xz-): 0 found - 0 modified | TOTAL: 3 Iteration Number : 2 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 2 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (xy+): 0 found - 0 modified | TOTAL: 0 pass 1 (xy-): 0 found - 0 modified | TOTAL: 0 pass 1 (yz+): 0 found - 0 modified | TOTAL: 0 pass 1 (yz-): 0 found - 0 modified | TOTAL: 0 pass 1 (xz+): 0 found - 0 modified | TOTAL: 0 pass 1 (xz-): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 pass 1 (+++): 0 found - 0 modified | TOTAL: 0 Iteration Number : 3 pass 1 (++): 0 found - 0 modified | TOTAL: 0 pass 1 (+-): 0 found - 0 modified | TOTAL: 0 pass 1 (--): 0 found - 0 modified | TOTAL: 0 pass 1 (-+): 0 found - 0 modified | TOTAL: 0 Total Number of Modified Voxels = 32 (out of 279352: 0.011455) Ambiguous edge configurations... mri_pretess done @#@FSTIME 2026:03:26:22:07:42 mri_pretess N 4 e 5.08 S 0.30 U 4.09 P 86% M 40320 F 0 R 8799 W 0 c 131 w 1 I 0 O 288 L 12.79 12.50 12.71 @#@FSLOADPOST 2026:03:26:22:07:47 mri_pretess N 4 12.41 12.42 12.69 mri_tessellate ../mri/filled-pretess127.mgz 127 ../surf/rh.orig.nofix 7.4.1 7.4.1 slice 50: 3168 vertices, 3340 faces slice 60: 11088 vertices, 11429 faces slice 70: 21454 vertices, 21879 faces slice 80: 33744 vertices, 34239 faces slice 90: 47115 vertices, 47541 faces slice 100: 59156 vertices, 59598 faces slice 110: 71874 vertices, 72314 faces slice 120: 85143 vertices, 85654 faces slice 130: 98185 vertices, 98673 faces slice 140: 110852 vertices, 111352 faces slice 150: 122403 vertices, 122848 faces slice 160: 131243 vertices, 131642 faces slice 170: 139295 vertices, 139655 faces slice 180: 145729 vertices, 146050 faces slice 190: 150669 vertices, 150942 faces slice 200: 153039 vertices, 153158 faces slice 210: 153060 vertices, 153170 faces slice 220: 153060 vertices, 153170 faces slice 230: 153060 vertices, 153170 faces slice 240: 153060 vertices, 153170 faces slice 250: 153060 vertices, 153170 faces using the conformed surface RAS to save vertex points... writing ../surf/rh.orig.nofix using vox2ras matrix: -1.00000 0.00000 0.00000 128.00000; 0.00000 0.00000 1.00000 -128.00000; 0.00000 -1.00000 0.00000 128.00000; 0.00000 0.00000 0.00000 1.00000; @#@FSTIME 2026:03:26:22:07:48 mri_tessellate N 3 e 2.94 S 0.15 U 2.69 P 96% M 39016 F 0 R 8750 W 0 c 81 w 1 I 0 O 7184 L 12.41 12.42 12.69 @#@FSLOADPOST 2026:03:26:22:07:51 mri_tessellate N 3 11.90 12.32 12.65 rm -f ../mri/filled-pretess127.mgz mris_extract_main_component ../surf/rh.orig.nofix ../surf/rh.orig.nofix counting number of connected components... 153060 voxel in cpt #1: X=-110 [v=153060,e=459510,f=306340] located at (28.906324, -14.437338, 17.912582) For the whole surface: X=-110 [v=153060,e=459510,f=306340] One single component has been found nothing to do done @#@FSTIME 2026:03:26:22:07:52 mris_extract_main_component N 2 e 2.79 S 0.55 U 1.89 P 87% M 303864 F 0 R 80449 W 0 c 38 w 4 I 16 O 10776 L 11.90 12.32 12.65 @#@FSLOADPOST 2026:03:26:22:07:55 mris_extract_main_component N 2 11.99 12.33 12.65 #-------------------------------------------- #@# Smooth1 lh 2026年 03月 26日星期四 22:07:56 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/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 2026:03:26:22:07:56 mris_smooth N 5 e 9.63 S 0.86 U 7.02 P 81% M 260584 F 0 R 78519 W 0 c 200 w 1 I 0 O 11616 L 11.99 12.33 12.65 @#@FSLOADPOST 2026:03:26:22:08:06 mris_smooth N 5 12.61 12.45 12.69 #-------------------------------------------- #@# Smooth1 rh 2026年 03月 26日星期四 22:08:06 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/scripts mris_smooth -nw -seed 1234 ../surf/rh.orig.nofix ../surf/rh.smoothwm.nofix setting seed for random number generator to 1234 smoothing surface tessellation for 10 iterations... smoothing complete - recomputing first and second fundamental forms... @#@FSTIME 2026:03:26:22:08:06 mris_smooth N 5 e 7.33 S 0.85 U 6.14 P 95% M 241992 F 0 R 72790 W 0 c 84 w 1 I 0 O 10776 L 12.61 12.45 12.69 @#@FSLOADPOST 2026:03:26:22:08:14 mris_smooth N 5 12.72 12.48 12.70 #-------------------------------------------- #@# Inflation1 lh 2026年 03月 26日星期四 22:08:14 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/scripts mris_inflate -no-save-sulc ../surf/lh.smoothwm.nofix ../surf/lh.inflated.nofix Not saving sulc Reading ../surf/lh.smoothwm.nofix avg radius = 47.1 mm, total surface area = 86243 mm^2 step 000: RMS=0.166 (target=0.015) step 005: RMS=0.130 (target=0.015) step 010: RMS=0.103 (target=0.015) step 015: RMS=0.091 (target=0.015) step 020: RMS=0.084 (target=0.015) step 025: RMS=0.080 (target=0.015) step 030: RMS=0.077 (target=0.015) step 035: RMS=0.074 (target=0.015) step 040: RMS=0.072 (target=0.015) step 045: RMS=0.072 (target=0.015) step 050: RMS=0.070 (target=0.015) step 055: RMS=0.071 (target=0.015) step 060: RMS=0.071 (target=0.015) writing inflated surface to ../surf/lh.inflated.nofix inflation took 0.9 minutes inflation complete. Not saving sulc mris_inflate utimesec 43.767454 mris_inflate stimesec 5.872484 mris_inflate ru_maxrss 261000 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 691143 mris_inflate ru_majflt 0 mris_inflate ru_nswap 0 mris_inflate ru_inblock 0 mris_inflate ru_oublock 11616 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 0 mris_inflate ru_nivcsw 892 @#@FSTIME 2026:03:26:22:08:14 mris_inflate N 3 e 55.74 S 5.96 U 43.76 P 89% M 261000 F 0 R 691148 W 0 c 898 w 1 I 0 O 11616 L 12.90 12.52 12.71 @#@FSLOADPOST 2026:03:26:22:09:10 mris_inflate N 3 13.39 12.71 12.77 #-------------------------------------------- #@# Inflation1 rh 2026年 03月 26日星期四 22:09:10 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/scripts mris_inflate -no-save-sulc ../surf/rh.smoothwm.nofix ../surf/rh.inflated.nofix Not saving sulc Reading ../surf/rh.smoothwm.nofix avg radius = 46.3 mm, total surface area = 79899 mm^2 step 000: RMS=0.163 (target=0.015) step 005: RMS=0.124 (target=0.015) step 010: RMS=0.099 (target=0.015) step 015: RMS=0.088 (target=0.015) step 020: RMS=0.081 (target=0.015) step 025: RMS=0.077 (target=0.015) step 030: RMS=0.072 (target=0.015) step 035: RMS=0.068 (target=0.015) step 040: RMS=0.067 (target=0.015) step 045: RMS=0.066 (target=0.015) step 050: RMS=0.065 (target=0.015) step 055: RMS=0.065 (target=0.015) step 060: RMS=0.066 (target=0.015) writing inflated surface to ../surf/rh.inflated.nofix inflation took 0.9 minutes inflation complete. Not saving sulc mris_inflate utimesec 40.425749 mris_inflate stimesec 5.762602 mris_inflate ru_maxrss 242448 mris_inflate ru_ixrss 0 mris_inflate ru_idrss 0 mris_inflate ru_isrss 0 mris_inflate ru_minflt 650145 mris_inflate ru_majflt 0 mris_inflate ru_nswap 0 mris_inflate ru_inblock 0 mris_inflate ru_oublock 10776 mris_inflate ru_msgsnd 0 mris_inflate ru_msgrcv 0 mris_inflate ru_nsignals 0 mris_inflate ru_nvcsw 0 mris_inflate ru_nivcsw 973 @#@FSTIME 2026:03:26:22:09:10 mris_inflate N 3 e 53.38 S 5.76 U 40.45 P 86% M 242448 F 0 R 650150 W 0 c 975 w 1 I 0 O 10776 L 13.39 12.71 12.77 @#@FSLOADPOST 2026:03:26:22:10:04 mris_inflate N 3 12.30 12.51 12.69 #-------------------------------------------- #@# QSphere lh 2026年 03月 26日星期四 22:10:05 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/scripts mris_sphere -q -p 6 -a 128 -seed 1234 ../surf/lh.inflated.nofix ../surf/lh.qsphere.nofix doing quick spherical unfolding. limitting unfolding to 6 passes using n_averages = 128 setting seed for random number genererator to 1234 version: 7.4.1 available threads: 1 scaling brain by 0.308... inflating... projecting onto sphere... surface projected - minimizing metric distortion... vertex spacing 0.94 +- 0.57 (0.00-->7.76) (max @ vno 67346 --> 68759) face area 0.02 +- 0.03 (-0.16-->0.87) Entering MRISinflateToSphere() inflating to sphere (rms error < 2.00) 000: dt: 0.0000, rms radial error=175.733, avgs=0 005/300: dt: 0.9000, rms radial error=175.479, avgs=0 010/300: dt: 0.9000, rms radial error=174.934, avgs=0 015/300: dt: 0.9000, rms radial error=174.218, avgs=0 020/300: dt: 0.9000, rms radial error=173.402, avgs=0 025/300: dt: 0.9000, rms radial error=172.528, avgs=0 030/300: dt: 0.9000, rms radial error=171.620, avgs=0 035/300: dt: 0.9000, rms radial error=170.695, avgs=0 040/300: dt: 0.9000, rms radial error=169.760, avgs=0 045/300: dt: 0.9000, rms radial error=168.822, avgs=0 050/300: dt: 0.9000, rms radial error=167.884, avgs=0 055/300: dt: 0.9000, rms radial error=166.949, avgs=0 060/300: dt: 0.9000, rms radial error=166.017, avgs=0 065/300: dt: 0.9000, rms radial error=165.089, avgs=0 070/300: dt: 0.9000, rms radial error=164.165, avgs=0 075/300: dt: 0.9000, rms radial error=163.246, avgs=0 080/300: dt: 0.9000, rms radial error=162.332, avgs=0 085/300: dt: 0.9000, rms radial error=161.423, avgs=0 090/300: dt: 0.9000, rms radial error=160.518, avgs=0 095/300: dt: 0.9000, rms radial error=159.618, avgs=0 100/300: dt: 0.9000, rms radial error=158.722, avgs=0 105/300: dt: 0.9000, rms radial error=157.831, avgs=0 110/300: dt: 0.9000, rms radial error=156.945, avgs=0 115/300: dt: 0.9000, rms radial error=156.064, avgs=0 120/300: dt: 0.9000, rms radial error=155.187, avgs=0 125/300: dt: 0.9000, rms radial error=154.315, avgs=0 130/300: dt: 0.9000, rms radial error=153.448, avgs=0 135/300: dt: 0.9000, rms radial error=152.585, avgs=0 140/300: dt: 0.9000, rms radial error=151.726, avgs=0 145/300: dt: 0.9000, rms radial error=150.873, avgs=0 150/300: dt: 0.9000, rms radial error=150.025, avgs=0 155/300: dt: 0.9000, rms radial error=149.181, avgs=0 160/300: dt: 0.9000, rms radial error=148.341, avgs=0 165/300: dt: 0.9000, rms radial error=147.506, avgs=0 170/300: dt: 0.9000, rms radial error=146.676, avgs=0 175/300: dt: 0.9000, rms radial error=145.850, avgs=0 180/300: dt: 0.9000, rms radial error=145.029, avgs=0 185/300: dt: 0.9000, rms radial error=144.212, avgs=0 190/300: dt: 0.9000, rms radial error=143.399, avgs=0 195/300: dt: 0.9000, rms radial error=142.591, avgs=0 200/300: dt: 0.9000, rms radial error=141.788, avgs=0 205/300: dt: 0.9000, rms radial error=140.989, avgs=0 210/300: dt: 0.9000, rms radial error=140.194, avgs=0 215/300: dt: 0.9000, rms radial error=139.404, avgs=0 220/300: dt: 0.9000, rms radial error=138.618, avgs=0 225/300: dt: 0.9000, rms radial error=137.837, avgs=0 230/300: dt: 0.9000, rms radial error=137.059, avgs=0 235/300: dt: 0.9000, rms radial error=136.286, avgs=0 240/300: dt: 0.9000, rms radial error=135.518, avgs=0 245/300: dt: 0.9000, rms radial error=134.753, avgs=0 250/300: dt: 0.9000, rms radial error=133.993, avgs=0 255/300: dt: 0.9000, rms radial error=133.237, avgs=0 260/300: dt: 0.9000, rms radial error=132.486, avgs=0 265/300: dt: 0.9000, rms radial error=131.738, avgs=0 270/300: dt: 0.9000, rms radial error=130.995, avgs=0 275/300: dt: 0.9000, rms radial error=130.256, avgs=0 280/300: dt: 0.9000, rms radial error=129.521, avgs=0 285/300: dt: 0.9000, rms radial error=128.790, avgs=0 290/300: dt: 0.9000, rms radial error=128.063, avgs=0 295/300: dt: 0.9000, rms radial error=127.341, avgs=0 300/300: dt: 0.9000, rms radial error=126.623, avgs=0 spherical inflation complete. epoch 1 (K=10.0), pass 1, starting sse = 19454.40 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00030 epoch 2 (K=40.0), pass 1, starting sse = 3337.43 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00009 epoch 3 (K=160.0), pass 1, starting sse = 438.26 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.04/13 = 0.00323 epoch 4 (K=640.0), pass 1, starting sse = 78.87 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.13/21 = 0.00634 final distance error %100000.00 writing spherical brain to ../surf/lh.qsphere.nofix spherical transformation took 0.0999 hours FSRUNTIME@ mris_sphere 0.0999 hours 1 threads #VMPC# mris_sphere VmPeak 526556 mris_sphere done @#@FSTIME 2026:03:26:22:10:05 mris_sphere N 9 e 359.57 S 35.50 U 294.64 P 91% M 267140 F 0 R 4119287 W 0 c 4093 w 1 I 0 O 11616 L 11.80 12.40 12.66 @#@FSLOADPOST 2026:03:26:22:16:05 mris_sphere N 9 13.17 12.51 12.56 #-------------------------------------------- #@# QSphere rh 2026年 03月 26日星期四 22:16:05 CST /home/laihan/Desktop/7T_fs2025/LH2024_173/scripts mris_sphere -q -p 6 -a 128 -seed 1234 ../surf/rh.inflated.nofix ../surf/rh.qsphere.nofix doing quick spherical unfolding. limitting unfolding to 6 passes using n_averages = 128 setting seed for random number genererator to 1234 version: 7.4.1 available threads: 1 scaling brain by 0.303... inflating... projecting onto sphere... surface projected - minimizing metric distortion... vertex spacing 0.95 +- 0.58 (0.00-->10.38) (max @ vno 107702 --> 107715) face area 0.02 +- 0.03 (-0.29-->0.59) Entering MRISinflateToSphere() inflating to sphere (rms error < 2.00) 000: dt: 0.0000, rms radial error=176.773, avgs=0 005/300: dt: 0.9000, rms radial error=176.511, avgs=0 010/300: dt: 0.9000, rms radial error=175.953, avgs=0 015/300: dt: 0.9000, rms radial error=175.223, avgs=0 020/300: dt: 0.9000, rms radial error=174.391, avgs=0 025/300: dt: 0.9000, rms radial error=173.501, avgs=0 030/300: dt: 0.9000, rms radial error=172.580, avgs=0 035/300: dt: 0.9000, rms radial error=171.642, avgs=0 040/300: dt: 0.9000, rms radial error=170.695, avgs=0 045/300: dt: 0.9000, rms radial error=169.747, avgs=0 050/300: dt: 0.9000, rms radial error=168.799, avgs=0 055/300: dt: 0.9000, rms radial error=167.853, avgs=0 060/300: dt: 0.9000, rms radial error=166.912, avgs=0 065/300: dt: 0.9000, rms radial error=165.975, avgs=0 070/300: dt: 0.9000, rms radial error=165.044, avgs=0 075/300: dt: 0.9000, rms radial error=164.117, avgs=0 080/300: dt: 0.9000, rms radial error=163.196, avgs=0 085/300: dt: 0.9000, rms radial error=162.280, avgs=0 090/300: dt: 0.9000, rms radial error=161.368, avgs=0 095/300: dt: 0.9000, rms radial error=160.461, avgs=0 100/300: dt: 0.9000, rms radial error=159.559, avgs=0 105/300: dt: 0.9000, rms radial error=158.662, avgs=0 110/300: dt: 0.9000, rms radial error=157.770, avgs=0 115/300: dt: 0.9000, rms radial error=156.882, avgs=0 120/300: dt: 0.9000, rms radial error=155.999, avgs=0 125/300: dt: 0.9000, rms radial error=155.122, avgs=0 130/300: dt: 0.9000, rms radial error=154.248, avgs=0 135/300: dt: 0.9000, rms radial error=153.380, avgs=0 140/300: dt: 0.9000, rms radial error=152.516, avgs=0 145/300: dt: 0.9000, rms radial error=151.657, avgs=0 150/300: dt: 0.9000, rms radial error=150.803, avgs=0 155/300: dt: 0.9000, rms radial error=149.953, avgs=0 160/300: dt: 0.9000, rms radial error=149.108, avgs=0 165/300: dt: 0.9000, rms radial error=148.267, avgs=0 170/300: dt: 0.9000, rms radial error=147.431, avgs=0 175/300: dt: 0.9000, rms radial error=146.599, avgs=0 180/300: dt: 0.9000, rms radial error=145.772, avgs=0 185/300: dt: 0.9000, rms radial error=144.950, avgs=0 190/300: dt: 0.9000, rms radial error=144.131, avgs=0 195/300: dt: 0.9000, rms radial error=143.318, avgs=0 200/300: dt: 0.9000, rms radial error=142.508, avgs=0 205/300: dt: 0.9000, rms radial error=141.704, avgs=0 210/300: dt: 0.9000, rms radial error=140.904, avgs=0 215/300: dt: 0.9000, rms radial error=140.108, avgs=0 220/300: dt: 0.9000, rms radial error=139.316, avgs=0 225/300: dt: 0.9000, rms radial error=138.529, avgs=0 230/300: dt: 0.9000, rms radial error=137.747, avgs=0 235/300: dt: 0.9000, rms radial error=136.969, avgs=0 240/300: dt: 0.9000, rms radial error=136.195, avgs=0 245/300: dt: 0.9000, rms radial error=135.426, avgs=0 250/300: dt: 0.9000, rms radial error=134.663, avgs=0 255/300: dt: 0.9000, rms radial error=133.903, avgs=0 260/300: dt: 0.9000, rms radial error=133.147, avgs=0 265/300: dt: 0.9000, rms radial error=132.395, avgs=0 270/300: dt: 0.9000, rms radial error=131.648, avgs=0 275/300: dt: 0.9000, rms radial error=130.905, avgs=0 280/300: dt: 0.9000, rms radial error=130.166, avgs=0 285/300: dt: 0.9000, rms radial error=129.431, avgs=0 290/300: dt: 0.9000, rms radial error=128.700, avgs=0 295/300: dt: 0.9000, rms radial error=127.973, avgs=0 300/300: dt: 0.9000, rms radial error=127.251, avgs=0 spherical inflation complete. epoch 1 (K=10.0), pass 1, starting sse = 18076.13 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00023 epoch 2 (K=40.0), pass 1, starting sse = 3057.02 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.00/13 = 0.00029 epoch 3 (K=160.0), pass 1, starting sse = 340.30 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.03/13 = 0.00207 epoch 4 (K=640.0), pass 1, starting sse = 42.61 taking momentum steps... taking momentum steps... taking momentum steps... taking momentum steps... pass 1 complete, delta sse/iter = 0.04/14 = 0.00312 final distance error %100000.00 writing spherical brain to ../surf/rh.qsphere.nofix spherical transformation took 0.0852 hours FSRUNTIME@ mris_sphere 0.0852 hours 1 threads #VMPC# mris_sphere VmPeak 507424 mris_sphere done @#@FSTIME 2026:03:26:22:16:05 mris_sphere N 9 e 306.63 S 31.08 U 249.31 P 91% M 248040 F 0 R 3478274 W 0 c 4555 w 1 I 0 O 10776 L 13.17 12.51 12.56 @#@FSLOADPOST 2026:03:26:22:21:12 mris_sphere N 9 13.72 13.38 12.94 #@# Fix Topology lh 2026年 03月 26日星期四 22:21:12 CST mris_fix_topology -mgz -sphere qsphere.nofix -inflated inflated.nofix -orig orig.nofix -out orig.premesh -ga -seed 1234 LH2024_173 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' 7.4.1 7.4.1 before topology correction, eno=-204 (nv=164942, nf=330292, ne=495438, g=103) 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 10 iterations marking ambiguous vertices... 50185 ambiguous faces found in tessellation segmenting defects... 40 defects found, arbitrating ambiguous regions... analyzing neighboring defects... -merging segment 3 into 1 -merging segment 23 into 20 -merging segment 24 into 22 37 defects to be corrected 0 vertices coincident reading input surface /home/laihan/Desktop/7T_fs2025/LH2024_173/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: -9.4698 (-4.7349) -vertex loglikelihood: -6.5300 (-3.2650) -normal dot loglikelihood: -3.6197 (-3.6197) -quad curv loglikelihood: -6.1855 (-3.0928) Total Loglikelihood : -25.8051 CORRECTING DEFECT 0 (vertices=656, convex hull=240, v0=0) After retessellation of defect 0 (v0=0), euler #=-32 (138218,411248,272998) : difference with theory (-34) = -2 CORRECTING DEFECT 1 (vertices=20175, convex hull=3658, v0=20) *** stack smashing detected ***: terminated Command terminated by signal 6 @#@FSTIME 2026:03:26:22:21:13 mris_fix_topology N 14 e 20823.59 S 408.99 U 20031.39 P 98% M 1770840 F 614 R 593742 W 0 c 146638 w 788 I 84928 O 3888 L 13.72 13.38 12.94 @#@FSLOADPOST 2026:03:27:04:08:16 mris_fix_topology N 14 8.16 8.18 8.11 Linux laihan-VirtualBox 5.15.0-139-generic #149~20.04.1-Ubuntu SMP Wed Apr 16 08:29:56 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux recon-all -s LH2024_173 exited with ERRORS To report a problem, see http://secure-web.cisco.com/1kXlCp4ff9-3jFVcIL0ZMKu8jjqAxQ7whmkHhtilGrLlkJY…

1 0

recon-all-clinical (FS 8.1): MEG/EEG coregistration issue - inverted BEM normals from norm.mgz geometry
by Noam Peled 26 Mar '26

26 Mar '26

External Email - Use Caution Dear FreeSurfer team, I'm writing to report a coordinate-frame issue when using recon-all-clinical (FreeSurfer 8.1) with downstream MEG/EEG coregistration and BEM workflows (MNE-Python), along with findings from testing across multiple subjects. I have a MEG processing pipeline that has worked reliably using standard recon-all (FreeSurfer 7.3). After switching to recon-all-clinical (FS 8.1) on the same data, the EEG/MEG sensors appear completely displaced from the scalp surface during coregistration. I tested the watershed BEM generation across 8 subjects using recon-all-clinical output and found the following: - Using norm.mgz (the SynthSR-derived volume from recon-all-clinical) for watershed BEM consistently produces surfaces with inverted normals. The norm.mgz has a non-standard geometry compared to what the standard pipeline produces. - Using a separately conformed T1 (via mri_convert --conform on the original input) for watershed BEM produces correct topology and passes all validation checks. - The cortical surfaces from recon-all-clinical live in norm.mgz space. However, when using the conformed T1 watershed for BEM, 7 out of 8 subjects showed 99-100% source retention, indicating the coordinate frames are close enough for practical use. - Simply symlinking T1.mgz to norm.mgz fixes the coordinate alignment but introduces the inverted normals problem, so this is not a viable workaround. 1. Is this a known issue? 2. Is there a more principled approach than the workaround above for reconciling the coordinate frames between the clinical pipeline surfaces and the conformed BEM surfaces? Thanks! Noam Peled

2 1

[8.2.0] [Ubuntu 24] Issue with gtmseg with FreeSurfer 8.2.0 on Ubuntu 24
by Valentin Demeusy 26 Mar '26

26 Mar '26

External Email - Use Caution Hello developpers, I have an issue when I try to run `gtmseg` on Ubuntu 24 in a docker image. I previously ran recon-all with the same FreeSurfer version and same OS flawlessly. But when I run gtmseg (gtmseg --s $subjectName). I get the following error: Traceback (most recent call last): File "/usr/local/freesurfer/8.2.0/python/scripts/run_samseg", line 5, in <module> from samseg.cli.run_samseg import main File "/usr/local/freesurfer/8.2.0/python/lib/python3.8/site-packages/samseg/__init__.py", line 5, in <module> from .SamsegUtility import * File "/usr/local/freesurfer/8.2.0/python/lib/python3.8/site-packages/samseg/SamsegUtility.py", line 8, in <module> from samseg import gems File "/usr/local/freesurfer/8.2.0/python/lib/python3.8/site-packages/samseg/gems/__init__.py", line 1, in <module> from .gemsbindings import * ModuleNotFoundError: No module named 'samseg.gems.gemsbindings' You will find the complete log at the end of this mail. Thank you for your help Valentin Demeusy Here is the complete log of the command: Mon Mar 23 02:43:20 UTC 2026 setenv SUBJECTS_DIR /mnt/subjectDir cd /mnt/subjectDir/fs /usr/local/freesurfer/8.2.0/bin/gtmseg --s fs freesurfer-linux-ubuntu24_x86_64-8.2.0-20260314-d932c45 gtmseg 8.2.0 Linux 1587b43e469e 6.17.0-1008-azure #8~24.04.1-Ubuntu SMP Mon Jan 26 18:35:40 UTC 2026 x86_64 x86_64 x86_64 GNU/Linux xcerebralseg --s fs --threads 1 --o apas+head.samseg.mgz Mon Mar 23 02:43:20 UTC 2026 setenv SUBJECTS_DIR /mnt/subjectDir cd /mnt/subjectDir/fs /usr/local/freesurfer/8.2.0/bin/xcerebralseg --s fs --threads 1 --o apas+head.samseg.mgz freesurfer-linux-ubuntu24_x86_64-8.2.0-20260314-d932c45 xcerebralseg 8.2.0 Linux 1587b43e469e 6.17.0-1008-azure #8~24.04.1-Ubuntu SMP Mon Jan 26 18:35:40 UTC 2026 x86_64 x86_64 x86_64 GNU/Linux fs_time samseg --i /mnt/subjectDir/fs/mri/nu.mgz --o /mnt/subjectDir/fs/mri/xcerebral.samseg --threads 1 --cpvcw /mnt/subjectDir/fs/mri/xcerebral.samseg /mnt/subjectDir/fs /mnt/subjectDir/fs Mon Mar 23 02:43:21 UTC 2026 setenv SUBJECTS_DIR /mnt/subjectDir cd /mnt/subjectDir/fs /usr/local/freesurfer/8.2.0/bin/samseg --i /mnt/subjectDir/fs/mri/nu.mgz --o /mnt/subjectDir/fs/mri/xcerebral.samseg --threads 1 --cpvcw freesurfer-linux-ubuntu24_x86_64-8.2.0-20260314-d932c45 samseg 8.2.0 Linux 1587b43e469e 6.17.0-1008-azure #8~24.04.1-Ubuntu SMP Mon Jan 26 18:35:40 UTC 2026 x86_64 x86_64 x86_64 GNU/Linux Using Block Coordinate Descent NOT using Log Domain Cost And Gradient Calculator run_samseg -o /mnt/subjectDir/fs/mri/xcerebral.samseg --threads 1 --atlas /usr/local/freesurfer/8.2.0/average/samseg/samseg+cc+pons+verm+charm+wmcrowns --save-warp -i /mnt/subjectDir/fs/mri/nu.mgz Traceback (most recent call last): File "/usr/local/freesurfer/8.2.0/python/scripts/run_samseg", line 5, in <module> from samseg.cli.run_samseg import main File "/usr/local/freesurfer/8.2.0/python/lib/python3.8/site-packages/samseg/__init__.py", line 5, in <module> from .SamsegUtility import * File "/usr/local/freesurfer/8.2.0/python/lib/python3.8/site-packages/samseg/SamsegUtility.py", line 8, in <module> from samseg import gems File "/usr/local/freesurfer/8.2.0/python/lib/python3.8/site-packages/samseg/gems/__init__.py", line 1, in <module> from .gemsbindings import * ModuleNotFoundError: No module named 'samseg.gems.gemsbindings' run_samseg complete Mon Mar 23 02:43:22 UTC 2026 lta_convert --inlta /mnt/subjectDir/fs/mri/xcerebral.samseg/samseg.talairach.lta --outmni /mnt/subjectDir/fs/mri/xcerebral.samseg/samseg.talairach.xfm 8.2.0 --inlta: /mnt/subjectDir/fs/mri/xcerebral.samseg/samseg.talairach.lta input LTA transform. --outmni: /mnt/subjectDir/fs/mri/xcerebral.samseg/samseg.talairach.xfm output MNI/XFM matrix. ERROR: lta_convert --inlta /mnt/subjectDir/fs/mri/xcerebral.samseg/samseg.talairach.lta --outmni /mnt/subjectDir/fs/mri/xcerebral.samseg/samseg.talairach.xfm ERROR: ERROR: xcerebralseg --s fs --threads 1 --o apas+head.samseg.mgz gtmseg exited with errors

2 7

Updates/bug fixes available for the 8.2.0 release
by fsbuild 25 Mar '26

25 Mar '26

External Email - Use Caution Some updates/bug fixes are available for an existing installation of a Freesurfer 8.2.0 release.   These include fixes for recon-all expert options and an update on Ubuntu24 only for gems bindings.  Please see the instructions at, https://secure-web.cisco.com/1M0Q8P56ELrNIwesSF0kx9ENclG7FIKiEl5oCxnbWJTr3b… - R.

1 0

Re: use of a custom annot for mri_segstats
by Simone Cauzzo 23 Mar '26

23 Mar '26

External Email - Use Caution Thank you very much I updated my code with an mri_surf2surf call that projects the annot to subject space. It is working now. Nonetheless, I'm having troubles in masking out some vertices. Since the QSM map had to be eroded in some points, the pial surface might meet voxels where the QSM map is null. These vertices are getting a zero value, and this is influencing my statistics. I would like to mask out the zero vertices from the summary statistics computed by mri_segstats. This is my current call mri_segstats --annot sub hemi ....annot \ --i []/surf/${hemi}.Xpara.${proF}.mgh \ --sum []/stats/${hemi}.Xpara.${proF}.${annotFL}.stats I tried using --maskthresh 0.0001 , and still I obtain in my statistics 0 as minimum for some ROIs, for example # ColHeaders Index SegId NVertices Area_mm2 StructName Mean StdDev Min Max Range 201 200 1388 949.8 17networks_RH_VisCent_Striate 0.0324 0.0318 0.0000 0.2042 0.2042 So it looks like the zero vertices are not being excluded. Is there a way to exclude zero vertices? Thanks Simone Cauzzo

2 1

FreeSurfer 8.2.0 release
by Douglas N. Greve 23 Mar '26

23 Mar '26

FreeSurfer 8.2.0 is officially released. The release notes are here: https://surfer.nmr.mgh.harvard.edu/fswiki/ReleaseNotes, which I have replicated below doug Stable 8.2.0 Release (Mar 18 2026) FreeSurfer <https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurfer> version 8.2.0 can be found here <https://surfer.nmr.mgh.harvard.edu/fswiki/rel7downloads>. This is a minor release and will give the same output of recon-all when it is run in the same way. We have stopped supporting CentOS7 and have added support for Rocky 9 Linux, Ubuntu 24 Linux, and MacOS Tahoe (26.1). Note that different combinations of build and execution operating systems can give different results (see "Control of operating system" below). * New features: o Claustrum segmentation (mri_claustrum_seg) o Pituitary and pineal glands segementation (mri_pglands_seg) o Control of operating system when running recon-all. Create a file called fs-allowed-os.txt with the allowable operating systems. If recon-all is not run from one of these operating systems, then it will exit with error. See fs-check-os --help for how to use. This, along with FS build version specification (fs-allowed-versions.txt) can go along way towards assuring repeatability. o fs-eddy and fs-topup frontend to run FSL eddy and topup corrections for diffusion analysis * Bug fixes: o Fixes xhemireg (cross-hemisphere surface registration); o Fixes gtmseg (now creates segementation with sinus segmentation, arteries, vermis, pons) o There was an occasional error in license reading when running mris_volmask that would cause recon-all to crash for no apparent reason when multiple threads were used.

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FreeSurfer 8.2.0 expert options bug in recon-all / rca-surfreg
by Sofoklis Goulis 22 Mar '26

22 Mar '26

External Email - Use Caution Hello, I encountered two reproducible bugs in the latest release, Stable 8.2.0 (March 18 2026), when running recon-all with an expert options file on a 0.7 mm 7T MP2RAGE dataset. First, the run crashes after mris_sphere because recon-all evaluates if($XOptsFile), which expands to the expert-options path and triggers tcsh’s "if: Expression Syntax." error. A suggested fix would be to replace if($XOptsFile) with if($#XOptsFile). After patching this locally, recon-all proceeds further but then passes --expert to rca-surfreg, which currently recognizes -expert but not --expert, causing a second crash with "ERROR: Flag --expert unrecognized". A suggested fix would be to add support for --expert in rca-surfreg, for example: case "--expert": case "-expert": These issues were reproduced with an expert options file containing only mris_inflate -n 60. I have also registered the bug on GitHub and am emailing here for completeness. Best regards, Sophocles Goulis Postdoctoral Research Fellow Max Planck UCL Centre for Computational Psychiatry and Ageing Department of Imaging Neuroscience UCL Queen Square Institute of Neurology University College London, London, UK

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NextBrain atlas at different resolution images
by Gabriella Monopoli 20 Mar '26

20 Mar '26

External Email - Use Caution I would like to ask for your advice regarding the use of NextBrain with MRI datasets acquired at different resolutions and coverage. Specifically, I have: • A 3D T1-weighted image covering the whole brain with 1×1×1 mm voxel size • A second T1-weighted acquisition focused only on the brainstem, with higher resolution (0.5×0.5×0.5 mm) Would it be possible (and methodologically sound) to combine these two datasets within the NextBrain pipeline? In particular, I am interested in whether the high-resolution brainstem scan can be integrated with the whole-brain image to improve segmentation or analysis in that region. If this approach is feasible, I would appreciate any recommendations on preprocessing steps (e.g., registration, resampling, or intensity normalization) to ensure compatibility between the two images. Thank you very much for your help. Best regards, Gabriella Monopoli

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