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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@fz-juelich.demailto:r.loucao@fz-juelich.de Tel: +49 2461 61 1907 ﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊﹊
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