Freesurfer December 2019

freesurfer@nmr.mgh.harvard.edu

85 participants
117 discussions

Problem with the segmentation of subregions
by Marina Fernández 17 Dec '19

17 Dec '19

External Email - Use Caution Hi experts, We are doing the segmentation of the subfields of the hippocampal and amygdala subfields, brainstem subestructures and thalamic nuclei. When we used segmentHA_T1.sh and segmentThalamicNuclei.sh, 'Command not found' appears. On the other hand, we used segmentBS.sh and we got the following message: 'Software requires 4 arguments: segmentSFnewT1.sh matlabRuntimeDirectory FShomeDirectory subjectName subjectDir'. But, when we typed 'segmentSFnewT1.sh' we got 'Command not found' again. Note: We have FS v6.0 and the runtime Matlab 2014b. Best wishes, Marina

1 0

Topological Defect
by Kong Chuizheng 16 Dec '19

16 Dec '19

External Email - Use Caution Hello Freesurfer Developers, We are trying to construct a 3D cortex model of a tumour subject. The recon-all.log showed that the program froze at Correction of the Topology with "XL defect detected." I followed the procedure of the online tutorial page ( https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/TopologicalDefect_free…) and found that there's lots of "holes" and sharp abnormal shapes in the lh/rh.smoothwm.nofix file. It will be very hard to fix this by hand, and we wonder if you can give us some advice. Sincerely, Chuizheng Kong Chuizheng Kong

2 9

PETsurfer RBV-PVC strange values for some ROIs
by Jonathan DuBois 16 Dec '19

16 Dec '19

External Email - Use Caution Hi Doug, I have been running mri_gtmpvc RBV-PVC and have noticed that in a few regions the RBV corrected values are negative. This occurs when using –no-rescale so I don’t think it is a reference region issue. I also tried using the –wm-annot option in mri_gtmseg and this resulted in slightly more regions with a negative value following RBV PVC. This suggested to me that the matrix may be ill-conditioned, but I was wondering what you think and if you have seen this before? I included a screen shot showing a couple regions with negative values. The raw counts are slightly lower in these regions but not significantly below the average. The data is from an HR+ scan (~6mm FWHM) from AD subject so the cortex is pretty thin and regions that this is occurring in are fairly small (e.g. left caudal anterior cingulate, right vessel, right accumbens, right transverse temporal). If you think it is the case that the matrix is ill-conditioned is there a way to reduce the number of regions by combining them automatically? I can generate a new parcellation and feed it into mri_gtmseg, but I wasn’t sure if you had something set up already. Best, Jon

1 0

(no subject)
by Marina Fernández 16 Dec '19

16 Dec '19

1 0

Create translucent brain volume and save as matlab figure file
by Sparsh Jain 15 Dec '19

15 Dec '19

External Email - Use Caution Hi! Can I create a brain volume with about 40% opacity and save it as a matlab figure? The volume may not be very accurate. Why I need this: I want to 3D plot the RAS coordinates of electrodes inside this brain volume in Matlab and assign different colors corresponding to power values. I have attached a sample brain volume that might work for me. Please ignore the electrode color (it was obtained using a different pipeline). Thanks! Sparsh

2 2

ROI analysis with manually created labels
by 尹圆圆 14 Dec '19

14 Dec '19

External Email - Use Caution Dear FreeSurfer developers, I used Freesurfer QDEC for group comparisons and identified clusters with significance cortical thickness between the Patient group and the Control group. Then I created several ROIs manually, saved them as labels, and mapped them onto each subject.  I have learned how to get the cortical thickness of certain label (e.g., lh.precentral.label) from one subject (e.g. N001) using this command "mris_anatomical_stats -l lh.precentral.label -t lh.thickness -b -f N001/stats/lh.precentral.stats N001 lh".  But using this command for each label and each subject (one by one) to extract cortical thickness is rather time-consuming, I am wondering if there this a batch processing command for this (for all subjects, do the aforementioned command automatically, so that I can extract the cortical thickness values of the lh.precentral.label of all subjects)? I've searched the freesurfer wiki and the question list but found no help. Excuse me for this basic question. Thanks in advance. Best regards, Yuanyuan Yin

2 1

Thickness problem
by Gwang-Won Kim 13 Dec '19

13 Dec '19

2 1

Study specific template
by Joshi, Nandita 13 Dec '19

13 Dec '19

External Email - Use Caution Hello, I am trying to make a study specific template based on the instructions at: https://surfer.nmr.mgh.harvard.edu/fswiki/SurfaceRegAndTemplates However, on running the first step: make_average_subject --out newtemplate --subjects subj1 subj2 subj3 ... I get an error while running the recon-all is trying to run the aparc to aseg step: #@# AParc-to-ASeg aparc Thu Dec 5 11:29:33 EST 2019 /Users/nandita/FreeSurfer_recon/newtemplate \n mri_aparc2aseg --s newtemplate --volmask --aseg aseg.presurf.hypos --relabel mri/norm.mgz mri/transforms/talairach.m3z /Applications/freesurfer/average/RB_all_2016-05-10.vc700.gca mri/aseg.auto_noCCseg.label_intensities.txt \n relabeling unlikely voxels interior to white matter surface: norm: mri/norm.mgz XFORM: mri/transforms/talairach.m3z GCA: /Applications/freesurfer/average/RB_all_2016-05-10.vc700.gca label intensities: mri/aseg.auto_noCCseg.label_intensities.txt SUBJECTS_DIR /Users/nandita/FreeSurfer_recon subject newtemplate outvol /Users/nandita/FreeSurfer_recon/newtemplate/mri/aparc+aseg.mgz useribbon 0 baseoffset 0 RipUnknown 0 Reading lh white surface /Users/nandita/FreeSurfer_recon/newtemplate/surf/lh.white Reading lh pial surface /Users/nandita/FreeSurfer_recon/newtemplate/surf/lh.pial Loading lh annotations from /Users/nandita/FreeSurfer_recon/newtemplate/label/lh.aparc.annot reading colortable from annotation file... colortable with 36 entries read (originally /Users/nandita/FreeSurfer_recon/newtemplate/label/tmpdir.annot2std.41916/seg.1.100110189.ctab) Reading rh white surface /Users/nandita/reeSurfer_recon/newtemplate/surf/rh.white Reading rh pial surface /Users/nandita/FreeSurfer_recon/newtemplate/surf/rh.pial Loading rh annotations from /Users/nandita/ADRC/FreeSurfer_recon/newtemplate/label/rh.aparc.annot reading colortable from annotation file... colortable with 36 entries read (originally /Users/nandita/FreeSurfer_recon/newtemplate/label/tmpdir.annot2std.47059/seg.1.100110189.ctab) Have color table for lh white annotation Have color table for rh white annotation Loading ribbon segmentation from /Users/nandita/FreeSurfer_recon/newtemplate/mri/ribbon.mgz Building hash of lh white Building hash of lh pial Building hash of rh white Building hash of rh pial Loading aseg from /Users/nandita/FreeSurfer_recon/newtemplate/mri/aseg.presurf.hypos.mgz ASeg Vox2RAS: ----------- -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; mghRead(mri/norm.mgz, -1): could not open file ------------------------- Labeling Slice relabeling unlikely voxels in interior of white matter Segmentation fault Darwin Nanditas-MacBook-Pro.local 17.7.0 Darwin Kernel Version 17.7.0: Sun Jun 2 20:31:42 PDT 2019; root:xnu-4570.71.46~1/RELEASE_X86_64 x86_64 recon-all -s newtemplate exited with ERRORS at Thu Dec 5 11:29:52 EST 2019 On inspecting the mri folder, it seems the norm.mgz file has not been created at all. Looking at the flowchart of steps for the recon-all command, norm.mgz is supposed to be created in the CA Normalize step, which requires the talairach.lta in the newtemplate/mri/transforms folder, which seems to be missing as well. The only transform created in the transform folder is talairach.xfm. The talairach.lta is created in the EM GCA registration step. Do I re-run the EM GCA registration, CA normalize, and then contonue from aparc to aseg? I am not sure if since I am creating my own template, the EM GCA step requires any modifications? Any help will be appreciated. Thanks, Nandita

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Segmentation fault mri_em_register
by Loucao, Ricardo 13 Dec '19

13 Dec '19

External Email - Use Caution Dear Freesurfer experts, I’m trying to run recon-all on "T1-like” data (it is actually a quantitative MRI map with a contrast very similar to T1). I managed to put it through recon-all’s autorecon1 stage without any major tricks or modifications (watershed is failing but I managed to work around it using FSL’s BET combined with mri_convert to create the brainmask.mgz file). Now I’m running into a segmentation fault error in mri_em_register. Here’s the relevant portion of the log file: #@# EM Registration Fri Dec 13 10:55:07 GMT 2019 /data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/mri mri_em_register -rusage /data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/touch/rusage.mri_em_register.dat -uns 3 -mask brainmask.mgz nu.mgz /data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_2016-05-10.vc700.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 '/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_2016-05-10.vc700.gca'... average std = 7.3 using min determinant for regularization = 5.3 0 singular and 841 ill-conditioned covariance matrices regularized reading 'nu.mgz'... INFO: MRImask() using MRImaskDifferentGeometry() INFO: MRImask() using MRImaskDifferentGeometry() INFO: MRImask() using MRImaskDifferentGeometry() INFO: MRImask() using MRImaskDifferentGeometry() INFO: MRImask() using MRImaskDifferentGeometry() freeing gibbs priors...done. accounting for voxel sizes in initial transform bounding unknown intensity as < 6.3 or > 503.7 total sample mean = 78.8 (1011 zeros) ************************************************ spacing=8, using 2830 sample points, tol=1.00e-05... ************************************************ register_mri: find_optimal_transform find_optimal_transform: nsamples 2830, passno 0, spacing 8 Segmentation fault (core dumped) Linux btupc09 4.4.0-154-generic #181-Ubuntu SMP Tue Jun 25 05:29:03 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux recon-all -s PM1 exited with ERRORS at Fri Dec 13 10:55:35 GMT 2019 I’m also sending you the log from the skull striping step, in case the two errors might be connected: #@# Skull Stripping Fri Dec 13 10:23:26 GMT 2019 /data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/mri mri_em_register -rusage /data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/touch/rusage.mri_em_register.skull.dat -skull nu.mgz /data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_withskull_2016-05-10.vc700.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 '/data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_withskull_2016-05-10.vc700.gca'... average std = 22.9 using min determinant for regularization = 52.6 0 singular and 9002 ill-conditioned covariance matrices regularized reading 'nu.mgz'... freeing gibbs priors...done. accounting for voxel sizes in initial transform bounding unknown intensity as < 8.7 or > 569.1 total sample mean = 77.6 (1399 zeros) ************************************************ spacing=8, using 3243 sample points, tol=1.00e-05... ************************************************ register_mri: find_optimal_transform find_optimal_transform: nsamples 3243, 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=25.1 skull bounding box = (67, 34, 13) --> (199, 162, 201) using (111, 77, 107) as brain centroid... mean wm in atlas = 108, using box (95,61,84) --> (127, 92,130) to find MRI wm before smoothing, mri peak at 106 robust fit to distribution - 107 +- 5.6 after smoothing, mri peak at 106, scaling input intensities by 1.019 scaling channel 0 by 1.01887 initial log_p = -4.775 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.469154 @ (-9.091, 27.273, 9.091) max log p = -4.310065 @ (4.545, -4.545, -4.545) max log p = -4.294819 @ (-2.273, 2.273, -2.273) max log p = -4.269301 @ (1.136, -1.136, -1.136) max log p = -4.255560 @ (-0.568, 0.568, 0.568) max log p = -4.255560 @ (0.000, 0.000, 0.000) Found translation: (-6.3, 24.4, 1.7): log p = -4.256 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.200, old_max_log_p =-4.256 (thresh=-4.3) 1.07500 0.00000 0.00000 -16.08441; 0.00000 1.05465 0.12074 4.81056; 0.00000 -0.15011 0.99144 20.31265; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.200, old_max_log_p =-4.200 (thresh=-4.2) 1.07500 0.00000 0.00000 -16.08441; 0.00000 1.05465 0.12074 4.81056; 0.00000 -0.15011 0.99144 20.31265; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 **************************************** Nine parameter search. iteration 2 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.137, old_max_log_p =-4.200 (thresh=-4.2) 1.09382 0.00671 -0.03027 -14.36137; -0.00004 1.06183 0.18668 -1.18705; 0.03573 -0.20423 0.92682 27.37608; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 3 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.137, old_max_log_p =-4.137 (thresh=-4.1) 1.09382 0.00671 -0.03027 -14.36137; -0.00004 1.06183 0.18668 -1.18705; 0.03573 -0.20423 0.92682 27.37608; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 **************************************** Nine parameter search. iteration 4 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.112, old_max_log_p =-4.137 (thresh=-4.1) 1.09811 0.01669 0.00322 -18.38501; -0.01727 1.06003 0.21032 -0.89724; 0.00002 -0.23036 0.92131 35.15163; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 5 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.109, old_max_log_p =-4.112 (thresh=-4.1) 1.09940 0.01671 0.00322 -18.55289; -0.01727 1.06003 0.21032 -0.89724; 0.00002 -0.23009 0.92023 35.24042; 0.00000 0.00000 0.00000 1.00000; min search scale 0.025000 reached *********************************************** Computing MAP estimate using 3243 samples... *********************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-05 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.09940 0.01671 0.00322 -18.55289; -0.01727 1.06003 0.21032 -0.89724; 0.00002 -0.23009 0.92023 35.24042; 0.00000 0.00000 0.00000 1.00000; nsamples 3243 Quasinewton: input matrix 1.09940 0.01671 0.00322 -18.55289; -0.01727 1.06003 0.21032 -0.89724; 0.00002 -0.23009 0.92023 35.24042; 0.00000 0.00000 0.00000 1.00000; outof QuasiNewtonEMA: 008: -log(p) = -0.0 tol 0.000010 Resulting transform: 1.09940 0.01671 0.00322 -18.55289; -0.01727 1.06003 0.21032 -0.89724; 0.00002 -0.23009 0.92023 35.24042; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -4.109 (old=-4.775) transform before final EM align: 1.09940 0.01671 0.00322 -18.55289; -0.01727 1.06003 0.21032 -0.89724; 0.00002 -0.23009 0.92023 35.24042; 0.00000 0.00000 0.00000 1.00000; ************************************************** EM alignment process ... Computing final MAP estimate using 364799 samples. ************************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-07 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.09940 0.01671 0.00322 -18.55289; -0.01727 1.06003 0.21032 -0.89724; 0.00002 -0.23009 0.92023 35.24042; 0.00000 0.00000 0.00000 1.00000; nsamples 364799 Quasinewton: input matrix 1.09940 0.01671 0.00322 -18.55289; -0.01727 1.06003 0.21032 -0.89724; 0.00002 -0.23009 0.92023 35.24042; 0.00000 0.00000 0.00000 1.00000; outof QuasiNewtonEMA: 010: -log(p) = 4.6 tol 0.000000 final transform: 1.09940 0.01671 0.00322 -18.55289; -0.01727 1.06003 0.21032 -0.89724; 0.00002 -0.23009 0.92023 35.24042; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach_with_skull.lta... mri_em_register utimesec 749.584000 mri_em_register stimesec 1.268000 mri_em_register ru_maxrss 612332 mri_em_register ru_ixrss 0 mri_em_register ru_idrss 0 mri_em_register ru_isrss 0 mri_em_register ru_minflt 157470 mri_em_register ru_majflt 0 mri_em_register ru_nswap 0 mri_em_register ru_inblock 148912 mri_em_register ru_oublock 24 mri_em_register ru_msgsnd 0 mri_em_register ru_msgrcv 0 mri_em_register ru_nsignals 0 mri_em_register ru_nvcsw 28 mri_em_register ru_nivcsw 480 registration took 12 minutes and 31 seconds. mri_watershed -rusage /data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/subjects/PM1/touch/rusage.mri_watershed.dat -T1 -brain_atlas /data/MR/mr_user/rloucao/Scriptery/freesurferCentOS/freesurfer/average/RB_all_withskull_2016-05-10.vc700.gca transforms/talairach_with_skull.lta T1.mgz brainmask.auto.mgz Mode: T1 normalized volume Mode: Use the information of atlas (default parms, --help for details) ********************************************************* The input file is T1.mgz The output file is brainmask.auto.mgz Weighting the input with atlas information before watershed *************************WATERSHED************************** Sorting... first estimation of the COG coord: x=129 y=88 z=109 r=61 first estimation of the main basin volume: 953211 voxels Looking for seedpoints 2 found in the cerebellum 18 found in the rest of the brain global maximum in x=114, y=83, z=83, Imax=255 CSF=11, WM_intensity=110, WM_VARIANCE=5 WM_MIN=110, WM_HALF_MIN=110, WM_HALF_MAX=110, WM_MAX=110 preflooding height equal to 10 percent done. Analyze... main basin size=9854728970 voxels, voxel volume =1.000 = 9854728970 mmm3 = 9854729.216 cm3 done. PostAnalyze...Basin Prior 47 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=134,y=125, z=130, r=123101 iterations ^^^^^^^^ couldn't find WM with original limits - expanding ^^^^^^ GLOBAL CSF_MIN=1, CSF_intensity=2, CSF_MAX=4 , nb = 45809 RIGHT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=4 , nb = -1021745081 LEFT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=4 , nb = 1107215422 RIGHT_BRAIN CSF_MIN=1, CSF_intensity=2, CSF_MAX=4 , nb = 1073632535 LEFT_BRAIN CSF_MIN=1, CSF_intensity=2, CSF_MAX=4 , nb = 1058569754 OTHER CSF_MIN=1, CSF_intensity=2, CSF_MAX=4 , nb = 1087488148 mri_watershed Error: GLOBAL region of the brain empty ! Linux btupc09 4.4.0-154-generic #181-Ubuntu SMP Tue Jun 25 05:29:03 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux recon-all -s PM1 exited with ERRORS at Fri Dec 13 10:36:04 GMT 2019 To report a problem, see http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting I’m not convinced that the problem is with mri_em_register per se, since the same command in the skull striping step didn’t evoke any problems. I also tried looking for nan’s or negative values in the nu.mgz and T1.mgz files and didn’t find any. Both these files are uchar(0), as shown by mri_info. I’m running recon-all from Freesurfer version 6.0.0, on an Ubuntu 16.04, with 64GB of RAM. Any help or pointers are highly appreciated! Thank you for your time, Best regards, Ricardo ﹎﹎﹎﹎﹎﹎﹎﹎﹎﹎﹎﹎﹎﹎﹎﹎﹎﹎ Ricardo Loução Institute of Neuroscience and Medicine INM-11 Forschungszentrum Jülich GmbH Germany E-mail: r.loucao(a)fz-juelich.de<mailto:r.loucao@fz-juelich.de> Tel: +49 2461 61 1907 ﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊ ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Volker Rieke Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, Prof. Dr. Sebastian M. Schmidt ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

2 1

flat maps in native space
by Nasiriavanaki, Zahra 12 Dec '19

12 Dec '19

Hi Freesurfer experts I was trying to make flat maps for individual subjects, following instruction in https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferOccipitalFlattenedPatch. For some of my subjects, the flat maps are distorted (attached image). Please let me know what you think. Thanks Mona [cid:5edcb33b-2248-4b84-938f-3f5afa365555] Zahra (Mona) Nasiriavanaki Postdoctoral Research Fellow Martinos Center for Biomedical Imaging Massachusetts General Hospital 149 13th Street, 149-2615 Charlestown, MA, USA, 02129

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Freesurfer December 2019