============================== ==> mri2mesh started Tue Jul 26 10:37:25 UTC 2022 ... ============================== ============================== ==> --brain/--brainf: Preparing GM and WM surfaces ... ============================== ============================== ==> running FreeSurfer recon-all on T1fs! ... ============================== mri_convert.bin -cs 1 /home/gianrocco/m2m_ernie/tmpImg.nii.gz /home/gianrocco/m2m_ernie/tmpImg.nii.gz $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from /home/gianrocco/m2m_ernie/tmpImg.nii.gz... TR=1000000.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0, -1) k_ras = (0, 1, 0) changing data type from float to uchar (noscale = 0)... MRIchangeType: Building histogram Reslicing using trilinear interpolation writing to /home/gianrocco/m2m_ernie/tmpImg.nii.gz... mri_convert.bin -cm /home/gianrocco/m2m_ernie/tmpImg.nii.gz /home/gianrocco/m2m_ernie/tmpImg.nii.gz $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from /home/gianrocco/m2m_ernie/tmpImg.nii.gz... TR=1000000.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0, -1) k_ras = (0, 1, 0) writing to /home/gianrocco/m2m_ernie/tmpImg.nii.gz... mri_convert.bin -nc -cs 1 -oni 256 -onj 256 -onk 256 -rt cubic /home/gianrocco/m2m_ernie/tmpImg.nii.gz /home/gianrocco/m2m_ernie/T1fs_resamp.nii.gz $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from /home/gianrocco/m2m_ernie/tmpImg.nii.gz... TR=1000000.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0, -1) k_ras = (0, 1, 0) Reslicing using MRItoBSpline degree 3 writing to /home/gianrocco/m2m_ernie/T1fs_resamp.nii.gz... Final result: 0.966349 -0.032293 -0.008358 47.245705 0.014539 1.022636 -0.097479 7.284630 0.012117 0.148925 0.915146 70.913880 0.000000 0.000000 0.000000 1.000000 Subject Stamp: freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c Current Stamp: freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c INFO: SUBJECTS_DIR is /home/gianrocco Actual FREESURFER_HOME /usr/local/freesurfer Linux gpu-ubuntu 5.13.0-1031-azure #37~20.04.1-Ubuntu SMP Mon Jun 13 22:51:01 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux '/usr/local/freesurfer/bin/recon-all' -> '/home/gianrocco/fs_ernie/scripts/recon-all.local-copy' /home/gianrocco/fs_ernie mri_convert /home/gianrocco/m2m_ernie/tmp/T1fs_roi_FS.nii.gz /home/gianrocco/fs_ernie/mri/orig/001.mgz mri_convert.bin /home/gianrocco/m2m_ernie/tmp/T1fs_roi_FS.nii.gz /home/gianrocco/fs_ernie/mri/orig/001.mgz $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from /home/gianrocco/m2m_ernie/tmp/T1fs_roi_FS.nii.gz... TR=1000000.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0, -1) k_ras = (0, 1, 0) writing to /home/gianrocco/fs_ernie/mri/orig/001.mgz... Started at Tue Jul 26 10:38:50 UTC 2022 Ended at Tue Jul 26 10:38:54 UTC 2022 #@#%# recon-all-run-time-hours 0.001 recon-all -s fs_ernie finished without error at Tue Jul 26 10:38:54 UTC 2022 done INFO: all volumes are conformed to the min voxel size Subject Stamp: freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c Current Stamp: freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0-2beb96c INFO: SUBJECTS_DIR is /home/gianrocco Actual FREESURFER_HOME /usr/local/freesurfer -rw-rw-r-- 1 gianrocco gianrocco 17116 Jul 26 10:38 /home/gianrocco/fs_ernie/scripts/recon-all.log Linux gpu-ubuntu 5.13.0-1031-azure #37~20.04.1-Ubuntu SMP Mon Jun 13 22:51:01 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux '/usr/local/freesurfer/bin/recon-all' -> '/home/gianrocco/fs_ernie/scripts/recon-all.local-copy' #-------------------------------------------- #@# MotionCor Tue Jul 26 10:38:55 UTC 2022 Found 1 runs /home/gianrocco/fs_ernie/mri/orig/001.mgz Checking for (invalid) multi-frame inputs... WARNING: only one run found. This is OK, but motion correction cannot be performed on one run, so I'll copy the run to rawavg and continue. cp /home/gianrocco/fs_ernie/mri/orig/001.mgz /home/gianrocco/fs_ernie/mri/rawavg.mgz /home/gianrocco/fs_ernie mri_convert /home/gianrocco/fs_ernie/mri/rawavg.mgz /home/gianrocco/fs_ernie/mri/orig.mgz -rt cubic --conform_min mri_convert.bin /home/gianrocco/fs_ernie/mri/rawavg.mgz /home/gianrocco/fs_ernie/mri/orig.mgz -rt cubic --conform_min $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from /home/gianrocco/fs_ernie/mri/rawavg.mgz... TR=1000000.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0, -1) k_ras = (0, 1, 0) changing data type from float to uchar (noscale = 0)... MRIchangeType: Building histogram writing to /home/gianrocco/fs_ernie/mri/orig.mgz... mri_add_xform_to_header -c /home/gianrocco/fs_ernie/mri/transforms/talairach.xfm /home/gianrocco/fs_ernie/mri/orig.mgz /home/gianrocco/fs_ernie/mri/orig.mgz INFO: extension is mgz #-------------------------------------------- #@# Talairach Tue Jul 26 10:39:03 UTC 2022 /home/gianrocco/fs_ernie/mri mri_nu_correct.mni --no-rescale --i orig.mgz --o orig_nu.mgz --n 1 --proto-iters 1000 --distance 50 /home/gianrocco/fs_ernie/mri /usr/local/freesurfer/bin/mri_nu_correct.mni --no-rescale --i orig.mgz --o orig_nu.mgz --n 1 --proto-iters 1000 --distance 50 nIters 1 $Id: mri_nu_correct.mni,v 1.27 2016/02/26 16:19:49 mreuter Exp $ Linux gpu-ubuntu 5.13.0-1031-azure #37~20.04.1-Ubuntu SMP Mon Jun 13 22:51:01 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux Tue Jul 26 10:39:03 UTC 2022 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 /usr/bin/bc tmpdir is ./tmp.mri_nu_correct.mni.74379 /home/gianrocco/fs_ernie/mri mri_convert orig.mgz ./tmp.mri_nu_correct.mni.74379/nu0.mnc -odt float mri_convert.bin orig.mgz ./tmp.mri_nu_correct.mni.74379/nu0.mnc -odt float $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from orig.mgz... TR=1000000.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0, -1) k_ras = (0, 1, 0) changing data type from uchar to float (noscale = 0)... writing to ./tmp.mri_nu_correct.mni.74379/nu0.mnc... -------------------------------------------------------- Iteration 1 Tue Jul 26 10:39:05 UTC 2022 nu_correct -clobber ./tmp.mri_nu_correct.mni.74379/nu0.mnc ./tmp.mri_nu_correct.mni.74379/nu1.mnc -tmpdir ./tmp.mri_nu_correct.mni.74379/0/ -iterations 1000 -distance 50 [gianrocco@gpu-ubuntu:/home/gianrocco/fs_ernie/mri/] [2022-07-26 10:39:05] running: /usr/local/freesurfer/mni/bin/nu_estimate_np_and_em -parzen -log -sharpen 0.15 0.01 -iterations 1000 -stop 0.001 -shrink 4 -auto_mask -nonotify -b_spline 1.0e-7 -distance 50 -quiet -execute -clobber -nokeeptmp -tmpdir ./tmp.mri_nu_correct.mni.74379/0/ ./tmp.mri_nu_correct.mni.74379/nu0.mnc ./tmp.mri_nu_correct.mni.74379/nu1.imp Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Number of iterations: 45 CV of field change: 0.000955505 mri_convert ./tmp.mri_nu_correct.mni.74379/nu1.mnc orig_nu.mgz --like orig.mgz --conform mri_convert.bin ./tmp.mri_nu_correct.mni.74379/nu1.mnc orig_nu.mgz --like orig.mgz --conform $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from ./tmp.mri_nu_correct.mni.74379/nu1.mnc... TR=0.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0, -1) k_ras = (0, 1, 0) INFO: transform src into the like-volume: orig.mgz changing data type from float to uchar (noscale = 0)... MRIchangeType: Building histogram writing to orig_nu.mgz... Tue Jul 26 10:39:54 UTC 2022 mri_nu_correct.mni done talairach_avi --i orig_nu.mgz --xfm transforms/talairach.auto.xfm --atlas 3T18yoSchwartzReactN32_as_orig talairach_avi log file is transforms/talairach_avi.log... Started at Tue Jul 26 10:39:54 UTC 2022 Ended at Tue Jul 26 10:40:16 UTC 2022 talairach_avi done cp transforms/talairach.auto.xfm transforms/talairach.xfm #-------------------------------------------- #@# Talairach Failure Detection Tue Jul 26 10:40:18 UTC 2022 /home/gianrocco/fs_ernie/mri talairach_afd -T 0.005 -xfm transforms/talairach.xfm talairach_afd: Talairach Transform: transforms/talairach.xfm OK (p=0.3371, pval=0.0406 >= threshold=0.0050) awk -f /usr/local/freesurfer/bin/extract_talairach_avi_QA.awk /home/gianrocco/fs_ernie/mri/transforms/talairach_avi.log tal_QC_AZS /home/gianrocco/fs_ernie/mri/transforms/talairach_avi.log TalAviQA: 0.93990 z-score: -8 #-------------------------------------------- #@# Nu Intensity Correction Tue Jul 26 10:40:18 UTC 2022 mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --cm --proto-iters 1000 --distance 50 --n 1 /home/gianrocco/fs_ernie/mri /usr/local/freesurfer/bin/mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --cm --proto-iters 1000 --distance 50 --n 1 nIters 1 $Id: mri_nu_correct.mni,v 1.27 2016/02/26 16:19:49 mreuter Exp $ Linux gpu-ubuntu 5.13.0-1031-azure #37~20.04.1-Ubuntu SMP Mon Jun 13 22:51:01 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux Tue Jul 26 10:40:18 UTC 2022 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 /usr/bin/bc tmpdir is ./tmp.mri_nu_correct.mni.75264 /home/gianrocco/fs_ernie/mri mri_convert -cm orig.mgz ./tmp.mri_nu_correct.mni.75264/nu0.mnc -odt float mri_convert.bin -cm orig.mgz ./tmp.mri_nu_correct.mni.75264/nu0.mnc -odt float $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from orig.mgz... TR=1000000.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0, -1) k_ras = (0, 1, 0) changing data type from uchar to float (noscale = 0)... writing to ./tmp.mri_nu_correct.mni.75264/nu0.mnc... -------------------------------------------------------- Iteration 1 Tue Jul 26 10:40:20 UTC 2022 nu_correct -clobber ./tmp.mri_nu_correct.mni.75264/nu0.mnc ./tmp.mri_nu_correct.mni.75264/nu1.mnc -tmpdir ./tmp.mri_nu_correct.mni.75264/0/ -iterations 1000 -distance 50 [gianrocco@gpu-ubuntu:/home/gianrocco/fs_ernie/mri/] [2022-07-26 10:40:20] running: /usr/local/freesurfer/mni/bin/nu_estimate_np_and_em -parzen -log -sharpen 0.15 0.01 -iterations 1000 -stop 0.001 -shrink 4 -auto_mask -nonotify -b_spline 1.0e-7 -distance 50 -quiet -execute -clobber -nokeeptmp -tmpdir ./tmp.mri_nu_correct.mni.75264/0/ ./tmp.mri_nu_correct.mni.75264/nu0.mnc ./tmp.mri_nu_correct.mni.75264/nu1.imp Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Processing:.................................................................Done Number of iterations: 45 CV of field change: 0.000955505 mri_binarize --i ./tmp.mri_nu_correct.mni.75264/nu1.mnc --min -1 --o ./tmp.mri_nu_correct.mni.75264/ones.mgz $Id: mri_binarize.c,v 1.43 2016/06/09 20:46:21 greve Exp $ cwd /home/gianrocco/fs_ernie/mri cmdline mri_binarize.bin --i ./tmp.mri_nu_correct.mni.75264/nu1.mnc --min -1 --o ./tmp.mri_nu_correct.mni.75264/ones.mgz sysname Linux hostname gpu-ubuntu machine x86_64 user gianrocco input ./tmp.mri_nu_correct.mni.75264/nu1.mnc frame 0 nErode3d 0 nErode2d 0 output ./tmp.mri_nu_correct.mni.75264/ones.mgz Binarizing based on threshold min -1 max +infinity binval 1 binvalnot 0 fstart = 0, fend = 0, nframes = 1 Found 16777216 values in range Counting number of voxels in first frame Found 16777216 voxels in final mask Count: 16777216 16777216.000000 16777216 100.000000 mri_binarize done mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.75264/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.75264/sum.junk --avgwf ./tmp.mri_nu_correct.mni.75264/input.mean.dat $Id: mri_segstats.c,v 1.121 2016/05/31 17:27:11 greve Exp $ cwd cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.75264/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.75264/sum.junk --avgwf ./tmp.mri_nu_correct.mni.75264/input.mean.dat sysname Linux hostname gpu-ubuntu machine x86_64 user gianrocco UseRobust 0 Loading ./tmp.mri_nu_correct.mni.75264/ones.mgz Loading orig.mgz Voxel Volume is 1 mm^3 Generating list of segmentation ids Found 1 segmentations Computing statistics for each segmentation Reporting on 1 segmentations Using PrintSegStat Computing spatial average of each frame 0 Writing to ./tmp.mri_nu_correct.mni.75264/input.mean.dat mri_segstats done mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.75264/ones.mgz --i ./tmp.mri_nu_correct.mni.75264/nu1.mnc --sum ./tmp.mri_nu_correct.mni.75264/sum.junk --avgwf ./tmp.mri_nu_correct.mni.75264/output.mean.dat $Id: mri_segstats.c,v 1.121 2016/05/31 17:27:11 greve Exp $ cwd cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.75264/ones.mgz --i ./tmp.mri_nu_correct.mni.75264/nu1.mnc --sum ./tmp.mri_nu_correct.mni.75264/sum.junk --avgwf ./tmp.mri_nu_correct.mni.75264/output.mean.dat sysname Linux hostname gpu-ubuntu machine x86_64 user gianrocco UseRobust 0 Loading ./tmp.mri_nu_correct.mni.75264/ones.mgz Loading ./tmp.mri_nu_correct.mni.75264/nu1.mnc Voxel Volume is 1 mm^3 Generating list of segmentation ids Found 1 segmentations Computing statistics for each segmentation Reporting on 1 segmentations Using PrintSegStat Computing spatial average of each frame 0 Writing to ./tmp.mri_nu_correct.mni.75264/output.mean.dat mri_segstats done mris_calc -o ./tmp.mri_nu_correct.mni.75264/nu1.mnc ./tmp.mri_nu_correct.mni.75264/nu1.mnc mul .94925460011558096822 Saving result to './tmp.mri_nu_correct.mni.75264/nu1.mnc' (type = MINC ) [ ok ] mri_convert ./tmp.mri_nu_correct.mni.75264/nu1.mnc nu.mgz --like orig.mgz mri_convert.bin ./tmp.mri_nu_correct.mni.75264/nu1.mnc nu.mgz --like orig.mgz $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from ./tmp.mri_nu_correct.mni.75264/nu1.mnc... TR=0.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 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 6 seconds. mapping (31, 125) to ( 3, 110) Tue Jul 26 10:41:31 UTC 2022 mri_nu_correct.mni done mri_add_xform_to_header -c /home/gianrocco/fs_ernie/mri/transforms/talairach.xfm nu.mgz nu.mgz INFO: extension is mgz #-------------------------------------------- #@# Intensity Normalization Tue Jul 26 10:41:32 UTC 2022 /home/gianrocco/fs_ernie/mri mri_normalize -g 1 -mprage -noconform nu.mgz T1.mgz using max gradient = 1.000 assuming input volume is MGH (Van der Kouwe) MP-RAGE not interpolating and embedding volume to be 256^3... reading from nu.mgz... normalizing image... talairach transform 0.82517 -0.00810 -0.05140 6.18815; -0.04078 0.78272 0.09512 -13.91792; -0.00784 -0.05674 0.89239 -32.30025; 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 = 22 Starting OpenSpline(): npoints = 22 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Iterating 2 times --------------------------------- 3d normalization pass 1 of 2 white matter peak found at 110 white matter peak found at 90 gm peak at 67 (67), valley at 35 (35) csf peak at 16, setting threshold to 50 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 93 gm peak at 65 (65), valley at 34 (34) csf peak at 15, setting threshold to 48 building Voronoi diagram... performing soap bubble smoothing, sigma = 8... Done iterating --------------------------------- writing output to T1.mgz 3D bias adjustment took 1 minutes and 44 seconds. #-------------------------------------------- #@# Skull Stripping Tue Jul 26 10:43:16 UTC 2022 /home/gianrocco/fs_ernie/mri mri_em_register -rusage /home/gianrocco/fs_ernie/touch/rusage.mri_em_register.skull.dat -skull nu.mgz /usr/local/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 '/usr/local/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=18.0 skull bounding box = (35, 14, 0) --> (219, 184, 221) using (96, 71, 111) as brain centroid... mean wm in atlas = 108, using box (73,50,84) --> (118, 91,138) to find MRI wm before smoothing, mri peak at 55 robust fit to distribution - 57 +- 12.4 distribution too broad for accurate scaling - disabling after smoothing, mri peak at 108, scaling input intensities by 1.000 scaling channel 0 by 1 initial log_p = -4.588 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.488641 @ (9.091, 27.273, -9.091) max log p = -4.479380 @ (4.545, 4.545, -4.545) max log p = -4.455728 @ (2.273, 2.273, 2.273) max log p = -4.436715 @ (-1.136, -5.682, 5.682) max log p = -4.420221 @ (1.705, 1.705, 1.705) max log p = -4.420221 @ (0.000, 0.000, 0.000) Found translation: (16.5, 30.1, -4.0): log p = -4.420 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.338, old_max_log_p =-4.420 (thresh=-4.4) 1.13041 -0.18351 -0.10490 33.01730; 0.15998 1.17846 -0.33757 31.28614; 0.16136 0.31723 1.18391 -69.66171; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.292, old_max_log_p =-4.338 (thresh=-4.3) 1.23083 -0.14439 0.07924 -0.06225; 0.19778 1.45687 -0.41732 12.58883; 0.03370 0.35672 1.26128 -47.18365; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 2 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.267, old_max_log_p =-4.292 (thresh=-4.3) 1.09401 -0.33088 -0.05408 46.01988; 0.38477 1.56600 -0.27839 -39.07138; 0.18080 0.15401 1.40195 -63.14937; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 3 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.267, old_max_log_p =-4.267 (thresh=-4.3) 1.09401 -0.33088 -0.05408 46.01988; 0.38477 1.56600 -0.27839 -39.07138; 0.18080 0.15401 1.40195 -63.14937; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 **************************************** Nine parameter search. iteration 4 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.250, old_max_log_p =-4.267 (thresh=-4.3) 1.10511 -0.21555 0.02534 24.72059; 0.33723 1.64921 -0.19864 -50.58752; 0.08242 0.06895 1.41834 -44.97516; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 5 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.250, old_max_log_p =-4.250 (thresh=-4.2) 1.10511 -0.21555 0.02534 24.72059; 0.33723 1.64921 -0.19864 -50.58752; 0.08242 0.06895 1.41834 -44.97516; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 **************************************** Nine parameter search. iteration 6 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.239, old_max_log_p =-4.250 (thresh=-4.2) 1.10252 -0.21505 0.02528 24.95533; 0.33802 1.65308 -0.19910 -51.03930; 0.08262 0.06912 1.42166 -45.34055; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 7 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.239, old_max_log_p =-4.239 (thresh=-4.2) 1.10252 -0.21505 0.02528 24.95533; 0.33802 1.65308 -0.19910 -51.03930; 0.08262 0.06912 1.42166 -45.34055; 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 1.10252 -0.21505 0.02528 24.95533; 0.33802 1.65308 -0.19910 -51.03930; 0.08262 0.06912 1.42166 -45.34055; 0.00000 0.00000 0.00000 1.00000; nsamples 3243 Quasinewton: input matrix 1.10252 -0.21505 0.02528 24.95533; 0.33802 1.65308 -0.19910 -51.03930; 0.08262 0.06912 1.42166 -45.34055; 0.00000 0.00000 0.00000 1.00000; outof QuasiNewtonEMA: 010: -log(p) = -0.0 tol 0.000010 Resulting transform: 1.10252 -0.21505 0.02528 24.95533; 0.33802 1.65308 -0.19910 -51.03930; 0.08262 0.06912 1.42166 -45.34055; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -4.239 (old=-4.588) transform before final EM align: 1.10252 -0.21505 0.02528 24.95533; 0.33802 1.65308 -0.19910 -51.03930; 0.08262 0.06912 1.42166 -45.34055; 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 1.10252 -0.21505 0.02528 24.95533; 0.33802 1.65308 -0.19910 -51.03930; 0.08262 0.06912 1.42166 -45.34055; 0.00000 0.00000 0.00000 1.00000; nsamples 364799 Quasinewton: input matrix 1.10252 -0.21505 0.02528 24.95533; 0.33802 1.65308 -0.19910 -51.03930; 0.08262 0.06912 1.42166 -45.34055; 0.00000 0.00000 0.00000 1.00000; outof QuasiNewtonEMA: 012: -log(p) = 4.6 tol 0.000000 final transform: 1.10252 -0.21505 0.02528 24.95533; 0.33802 1.65308 -0.19910 -51.03930; 0.08262 0.06912 1.42166 -45.34055; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach_with_skull.lta... mri_em_register utimesec 691.195437 mri_em_register stimesec 1.708948 mri_em_register ru_maxrss 615160 mri_em_register ru_ixrss 0 mri_em_register ru_idrss 0 mri_em_register ru_isrss 0 mri_em_register ru_minflt 160652 mri_em_register ru_majflt 0 mri_em_register ru_nswap 0 mri_em_register ru_inblock 0 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 0 mri_em_register ru_nivcsw 2618 registration took 11 minutes and 33 seconds. mri_watershed -rusage /home/gianrocco/fs_ernie/touch/rusage.mri_watershed.dat -T1 -brain_atlas /usr/local/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=123 y=90 z=117 r=80 first estimation of the main basin volume: 2177635 voxels Looking for seedpoints 2 found in the cerebellum 6 found in the rest of the brain global maximum in x=120, y=65, z=89, Imax=255 CSF=18, 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=308206956 voxels, voxel volume =1.000 = 308206956 mmm3 = 308206.944 cm3 done. PostAnalyze...Basin Prior 602 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=111,y=93, z=99, r=8772 iterations ^^^^^^^^ couldn't find WM with original limits - expanding ^^^^^^ GLOBAL CSF_MIN=0, CSF_intensity=50, CSF_MAX=61 , nb = 23694 RIGHT_CER CSF_MIN=1, CSF_intensity=8, CSF_MAX=21 , nb = 1027652428 LEFT_CER CSF_MIN=34, CSF_intensity=48, CSF_MAX=64 , nb = -1040752847 RIGHT_BRAIN CSF_MIN=0, CSF_intensity=6, CSF_MAX=243 , nb = 1100841350 LEFT_BRAIN CSF_MIN=35, CSF_intensity=50, CSF_MAX=59 , nb = 1067844861 OTHER CSF_MIN=7, CSF_intensity=47, CSF_MAX=54 , nb = 1070009480 Problem with the least square interpolation in GM_MIN calculation. CSF_MAX TRANSITION GM_MIN GM GLOBAL before analyzing : 61, 77, 87, 93 after analyzing : 61, 83, 87, 85 RIGHT_CER before analyzing : 21, 66, 87, 93 after analyzing : 21, 80, 87, 83 LEFT_CER before analyzing : 64, 80, 87, 93 after analyzing : 64, 84, 87, 86 RIGHT_BRAIN before analyzing : 243, 90, 87, 93 after analyzing : 69, 90, 90, 90 LEFT_BRAIN before analyzing : 59, 75, 87, 93 after analyzing : 59, 83, 87, 85 OTHER before analyzing : 54, 71, 87, 93 after analyzing : 54, 81, 87, 84 mri_strip_skull: done peeling brain highly tesselated surface with 10242 vertices matching...67 iterations *********************VALIDATION********************* curvature mean = -0.018, std = 0.021 curvature mean = 49.374, std = 6.206 No Rigid alignment: -atlas Mode Off (basic atlas / no registration) before rotation: sse = 20.97, sigma = 53.63 after rotation: sse = 20.97, sigma = 53.63 Localization of inacurate regions: Erosion-Dilation steps the sse mean is 25.28, its var is 44.68 before Erosion-Dilatation 26.18% of inacurate vertices after Erosion-Dilatation 38.26% of inacurate vertices 40.24% of 'positive' inacurate vertices 59.76% of 'negative' inacurate vertices The surface validation has detected a possible Error If the final segmentation is not valid, try using the option '-atlas' Scaling of atlas fields onto current surface fields ********FINAL ITERATIVE TEMPLATE DEFORMATION******** Compute Local values csf/gray Fine Segmentation...50 iterations mri_strip_skull: done peeling brain Brain Size = 532452 voxels, voxel volume = 1.000 mm3 = 532452 mmm3 = 532.452 cm3 ****************************** Saving brainmask.auto.mgz done mri_watershed utimesec 15.868180 mri_watershed stimesec 0.308081 mri_watershed ru_maxrss 847572 mri_watershed ru_ixrss 0 mri_watershed ru_idrss 0 mri_watershed ru_isrss 0 mri_watershed ru_minflt 210092 mri_watershed ru_majflt 0 mri_watershed ru_nswap 0 mri_watershed ru_inblock 0 mri_watershed ru_oublock 1024 mri_watershed ru_msgsnd 0 mri_watershed ru_msgrcv 0 mri_watershed ru_nsignals 0 mri_watershed ru_nvcsw 0 mri_watershed ru_nivcsw 87 mri_watershed done cp brainmask.auto.mgz brainmask.mgz #------------------------------------- #@# EM Registration Tue Jul 26 10:55:06 UTC 2022 /home/gianrocco/fs_ernie/mri mri_em_register -rusage /home/gianrocco/fs_ernie/touch/rusage.mri_em_register.dat -uns 3 -mask brainmask.mgz nu.mgz /usr/local/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 '/usr/local/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'... 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 resetting wm mean[0]: 98 --> 107 resetting gm mean[0]: 61 --> 61 input volume #1 is the most T1-like using real data threshold=37.1 skull bounding box = (63, 51, 45) --> (165, 129, 149) using (97, 77, 97) as brain centroid... mean wm in atlas = 107, using box (85,68,84) --> (109, 86,109) to find MRI wm before smoothing, mri peak at 58 robust fit to distribution - 58 +- 6.5 distribution too broad for accurate scaling - disabling after smoothing, mri peak at 107, scaling input intensities by 1.000 scaling channel 0 by 1 initial log_p = -4.686 ************************************************ First Search limited to translation only. ************************************************ max log p = -4.574648 @ (9.091, 27.273, -9.091) max log p = -4.545949 @ (13.636, 13.636, -4.545) max log p = -4.520437 @ (6.818, 2.273, 2.273) max log p = -4.517220 @ (-3.409, -3.409, -5.682) max log p = -4.515133 @ (0.568, 2.841, 5.114) max log p = -4.509790 @ (0.852, -0.852, -0.284) Found translation: (27.6, 41.8, -12.2): log p = -4.510 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.339, old_max_log_p =-4.510 (thresh=-4.5) 1.15906 -0.39459 -0.17083 51.36142; 0.32241 1.12253 -0.40532 24.89108; 0.28449 0.33546 1.15536 -82.68553; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.266, old_max_log_p =-4.339 (thresh=-4.3) 1.20628 -0.31873 0.00555 27.46876; 0.46969 1.38681 -0.38666 -17.74169; 0.05341 0.27125 1.20836 -49.19301; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 2 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.238, old_max_log_p =-4.266 (thresh=-4.3) 1.11751 -0.52942 -0.10045 67.05777; 0.66743 1.42876 -0.43346 -37.73641; 0.22619 0.24437 1.28866 -73.29755; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 3 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-4.236, old_max_log_p =-4.238 (thresh=-4.2) 1.11751 -0.52942 -0.10045 67.05777; 0.61738 1.32160 -0.40095 -25.95943; 0.22619 0.24437 1.28866 -73.29755; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.2500 **************************************** Nine parameter search. iteration 4 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.212, old_max_log_p =-4.236 (thresh=-4.2) 1.08398 -0.47979 -0.15586 71.76229; 0.57141 1.33014 -0.43909 -17.94983; 0.29489 0.28471 1.31695 -85.30508; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 5 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.206, old_max_log_p =-4.212 (thresh=-4.2) 1.02411 -0.55570 -0.12721 82.11201; 0.66260 1.32895 -0.41270 -30.15651; 0.27086 0.23652 1.30566 -77.22214; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 6 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-4.206, old_max_log_p =-4.206 (thresh=-4.2) 1.02411 -0.55570 -0.12721 82.11201; 0.66260 1.32895 -0.41270 -30.15651; 0.27086 0.23652 1.30566 -77.22214; 0.00000 0.00000 0.00000 1.00000; reducing scale to 0.0625 **************************************** Nine parameter search. iteration 7 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.194, old_max_log_p =-4.206 (thresh=-4.2) 1.02808 -0.53600 -0.15469 81.74413; 0.65169 1.34476 -0.38971 -31.84111; 0.27573 0.20490 1.30538 -74.82301; 0.00000 0.00000 0.00000 1.00000; **************************************** Nine parameter search. iteration 8 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-4.194, old_max_log_p =-4.194 (thresh=-4.2) 1.02808 -0.53600 -0.15469 81.74413; 0.65169 1.34476 -0.38971 -31.84111; 0.27573 0.20490 1.30538 -74.82301; 0.00000 0.00000 0.00000 1.00000; min search scale 0.025000 reached *********************************************** Computing MAP estimate using 2830 samples... *********************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-05 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.02808 -0.53600 -0.15469 81.74413; 0.65169 1.34476 -0.38971 -31.84111; 0.27573 0.20490 1.30538 -74.82301; 0.00000 0.00000 0.00000 1.00000; nsamples 2830 Quasinewton: input matrix 1.02808 -0.53600 -0.15469 81.74413; 0.65169 1.34476 -0.38971 -31.84111; 0.27573 0.20490 1.30538 -74.82301; 0.00000 0.00000 0.00000 1.00000; outof QuasiNewtonEMA: 011: -log(p) = -0.0 tol 0.000010 Resulting transform: 1.02808 -0.53600 -0.15469 81.74413; 0.65169 1.34476 -0.38971 -31.84111; 0.27573 0.20490 1.30538 -74.82301; 0.00000 0.00000 0.00000 1.00000; pass 1, spacing 8: log(p) = -4.194 (old=-4.686) transform before final EM align: 1.02808 -0.53600 -0.15469 81.74413; 0.65169 1.34476 -0.38971 -31.84111; 0.27573 0.20490 1.30538 -74.82301; 0.00000 0.00000 0.00000 1.00000; ************************************************** EM alignment process ... Computing final MAP estimate using 315557 samples. ************************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-07 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.02808 -0.53600 -0.15469 81.74413; 0.65169 1.34476 -0.38971 -31.84111; 0.27573 0.20490 1.30538 -74.82301; 0.00000 0.00000 0.00000 1.00000; nsamples 315557 Quasinewton: input matrix 1.02808 -0.53600 -0.15469 81.74413; 0.65169 1.34476 -0.38971 -31.84111; 0.27573 0.20490 1.30538 -74.82301; 0.00000 0.00000 0.00000 1.00000; outof QuasiNewtonEMA: 013: -log(p) = 4.4 tol 0.000000 final transform: 1.02808 -0.53600 -0.15469 81.74413; 0.65169 1.34476 -0.38971 -31.84111; 0.27573 0.20490 1.30538 -74.82301; 0.00000 0.00000 0.00000 1.00000; writing output transformation to transforms/talairach.lta... mri_em_register utimesec 668.609193 mri_em_register stimesec 2.100079 mri_em_register ru_maxrss 604476 mri_em_register ru_ixrss 0 mri_em_register ru_idrss 0 mri_em_register ru_isrss 0 mri_em_register ru_minflt 161915 mri_em_register ru_majflt 0 mri_em_register ru_nswap 0 mri_em_register ru_inblock 0 mri_em_register ru_oublock 16 mri_em_register ru_msgsnd 0 mri_em_register ru_msgrcv 0 mri_em_register ru_nsignals 0 mri_em_register ru_nvcsw 13 mri_em_register ru_nivcsw 2541 registration took 11 minutes and 11 seconds. #-------------------------------------- #@# CA Normalize Tue Jul 26 11:06:17 UTC 2022 /home/gianrocco/fs_ernie/mri mri_ca_normalize -c ctrl_pts.mgz -mask brainmask.mgz nu.mgz /usr/local/freesurfer/average/RB_all_2016-05-10.vc700.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_2016-05-10.vc700.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=33.1 skull bounding box = (63, 50, 44) --> (166, 129, 149) using (97, 76, 97) as brain centroid... mean wm in atlas = 107, using box (84,66,84) --> (109, 85,109) to find MRI wm before smoothing, mri peak at 60 robust fit to distribution - 58 +- 6.3 distribution too broad for accurate scaling - disabling after smoothing, mri peak at 107, scaling input intensities by 1.000 scaling channel 0 by 1 using 246344 sample points... INFO: compute sample coordinates transform 1.02808 -0.53600 -0.15469 81.74413; 0.65169 1.34476 -0.38971 -31.84111; 0.27573 0.20490 1.30538 -74.82301; 0.00000 0.00000 0.00000 1.00000; INFO: transform used finding control points in Left_Cerebral_White_Matter.... found 39915 control points for structure... bounding box (89, 38, 41) --> (162, 117, 163) Left_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 28 of 28 (100.0%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 39557 control points for structure... bounding box (49, 54, 46) --> (124, 139, 167) Right_Cerebral_White_Matter: limiting intensities to 88.0 --> 132.0 8 of 8 (100.0%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3059 control points for structure... bounding box (107, 93, 52) --> (152, 118, 92) Left_Cerebellum_White_Matter: limiting intensities to 155.0 --> 132.0 33 of 33 (100.0%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2705 control points for structure... bounding box (79, 97, 53) --> (114, 135, 96) Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0 8 of 8 (100.0%) samples deleted finding control points in Brain_Stem.... found 3518 control points for structure... bounding box (99, 93, 74) --> (132, 135, 104) Brain_Stem: limiting intensities to 153.0 --> 132.0 25 of 25 (100.0%) samples deleted using 102 total control points for intensity normalization... 0 of 0 control points discarded finding control points in Left_Cerebral_White_Matter.... found 39915 control points for structure... bounding box (89, 38, 41) --> (162, 117, 163) finding control points in Right_Cerebral_White_Matter.... found 39557 control points for structure... bounding box (49, 54, 46) --> (124, 139, 167) finding control points in Left_Cerebellum_White_Matter.... found 3059 control points for structure... bounding box (107, 93, 52) --> (152, 118, 92) finding control points in Right_Cerebellum_White_Matter.... found 2705 control points for structure... bounding box (79, 97, 53) --> (114, 135, 96) finding control points in Brain_Stem.... found 3518 control points for structure... bounding box (99, 93, 74) --> (132, 135, 104) skipping region 2 with no control points detected finding control points in Left_Cerebral_White_Matter.... found 39915 control points for structure... bounding box (89, 38, 41) --> (162, 117, 163) finding control points in Right_Cerebral_White_Matter.... found 39557 control points for structure... bounding box (49, 54, 46) --> (124, 139, 167) finding control points in Left_Cerebellum_White_Matter.... found 3059 control points for structure... bounding box (107, 93, 52) --> (152, 118, 92) finding control points in Right_Cerebellum_White_Matter.... found 2705 control points for structure... bounding box (79, 97, 53) --> (114, 135, 96) finding control points in Brain_Stem.... found 3518 control points for structure... bounding box (99, 93, 74) --> (132, 135, 104) skipping region 3 with no control points detected writing normalized volume to norm.mgz... writing control points to ctrl_pts.mgz freeing GCA...done. normalization took 0 minutes and 23 seconds. #-------------------------------------- #@# CA Reg Tue Jul 26 11:06:40 UTC 2022 /home/gianrocco/fs_ernie/mri mri_ca_register -rusage /home/gianrocco/fs_ernie/touch/rusage.mri_ca_register.dat -nobigventricles -T transforms/talairach.lta -align-after -mask brainmask.mgz norm.mgz /usr/local/freesurfer/average/RB_all_2016-05-10.vc700.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_2016-05-10.vc700.gca'... label assignment complete, 0 changed (0.00%) det(m_affine) = 2.44 (predicted orig area = 3.3) label assignment complete, 0 changed (0.00%) freeing gibbs priors...done. average std[0] = 5.0 **************** pass 1 of 1 ************************ enabling zero nodes setting smoothness coefficient to 0.039 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0001: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0002: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0003: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0004: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 0.154 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0005: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0006: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0007: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0008: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 0.588 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0009: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0010: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0011: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0012: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 2.000 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0013: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0014: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0015: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0016: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 5.000 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0017: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0018: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0019: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0020: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 resetting metric properties... setting smoothness coefficient to 10.000 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0021: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0022: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0023: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0024: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.10027 (20) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.15565 (16) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.26829 (96) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.20183 (93) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.21683 (55) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.30730 (58) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.11430 (101) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.12076 (102) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.14995 (59) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.15082 (58) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.14161 (67) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.15243 (71) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.13336 (57) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.13252 (56) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.18181 (84) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.20573 (83) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.21969 (57) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.39313 (56) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.14181 (85) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.11978 (83) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.13399 (79) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.14159 (79) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.10025 (80) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.13281 (86) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.12801 (89) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.20494 (23) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.15061 (21) uniform distribution in MR - rejecting arbitrary fit gca peak Unknown = 0.94835 ( 0) gca peak Left_Cerebral_White_Matter = 0.12076 (102) gca peak Left_Cerebral_Cortex = 0.14995 (59) gca peak Left_Lateral_Ventricle = 0.10027 (20) gca peak Left_Inf_Lat_Vent = 0.18056 (32) gca peak Left_Cerebellum_White_Matter = 0.18181 (84) gca peak Left_Cerebellum_Cortex = 0.13336 (57) gca peak Left_Thalamus = 0.64095 (94) gca peak Left_Thalamus_Proper = 0.14181 (85) gca peak Left_Caudate = 0.15243 (71) gca peak Left_Putamen = 0.13399 (79) gca peak Left_Pallidum = 0.20183 (93) gca peak Third_Ventricle = 0.20494 (23) gca peak Fourth_Ventricle = 0.15061 (21) gca peak Brain_Stem = 0.10025 (80) gca peak Left_Hippocampus = 0.30730 (58) gca peak Left_Amygdala = 0.21969 (57) gca peak CSF = 0.20999 (34) gca peak Left_Accumbens_area = 0.39030 (62) gca peak Left_VentralDC = 0.12801 (89) gca peak Left_undetermined = 0.95280 (25) gca peak Left_vessel = 0.67734 (53) gca peak Left_choroid_plexus = 0.09433 (44) gca peak Right_Cerebral_White_Matter = 0.11430 (101) gca peak Right_Cerebral_Cortex = 0.15082 (58) gca peak Right_Lateral_Ventricle = 0.15565 (16) gca peak Right_Inf_Lat_Vent = 0.23544 (26) gca peak Right_Cerebellum_White_Matter = 0.20573 (83) gca peak Right_Cerebellum_Cortex = 0.13252 (56) gca peak Right_Thalamus_Proper = 0.11978 (83) gca peak Right_Caudate = 0.14161 (67) gca peak Right_Putamen = 0.14159 (79) gca peak Right_Pallidum = 0.26829 (96) gca peak Right_Hippocampus = 0.21683 (55) gca peak Right_Amygdala = 0.39313 (56) gca peak Right_Accumbens_area = 0.30312 (64) gca peak Right_VentralDC = 0.13281 (86) gca peak Right_vessel = 0.46315 (51) gca peak Right_choroid_plexus = 0.14086 (44) gca peak Fifth_Ventricle = 0.51669 (36) gca peak WM_hypointensities = 0.09722 (76) gca peak non_WM_hypointensities = 0.11899 (47) gca peak Optic_Chiasm = 0.39033 (72) label assignment complete, 0 changed (0.00%) 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 1.00 x + 0.0 setting left cbm cortex = 1.00 x + 0.00 setting right cbm cortex = 1.00 x + 0.00 saving intensity scales to talairach.label_intensities.txt **************** pass 1 of 1 ************************ enabling zero nodes setting smoothness coefficient to 0.008 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0025: dt=0.005645, rms=2.177 (0.184%), neg=0, invalid=762 0026: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0027: dt=0.850000, rms=2.177 (-0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0028: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0029: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0030: dt=0.850000, rms=2.177 (-0.000%), neg=0, invalid=762 setting smoothness coefficient to 0.031 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0031: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0032: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0033: dt=0.450000, rms=2.177 (-0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0034: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0035: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0036: dt=0.450000, rms=2.177 (-0.000%), neg=0, invalid=762 setting smoothness coefficient to 0.118 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0037: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0038: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0039: dt=0.250000, rms=2.177 (-0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0040: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0041: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0042: dt=0.250000, rms=2.177 (-0.000%), neg=0, invalid=762 setting smoothness coefficient to 0.400 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0043: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0044: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0045: dt=0.150000, rms=2.177 (-0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0046: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0047: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0048: dt=0.150000, rms=2.177 (-0.000%), neg=0, invalid=762 setting smoothness coefficient to 1.000 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0049: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0050: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0051: dt=0.100000, rms=2.177 (-0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0052: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0053: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0054: dt=0.100000, rms=2.177 (-0.000%), neg=0, invalid=762 resetting metric properties... setting smoothness coefficient to 2.000 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0055: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0056: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0057: dt=0.050000, rms=2.177 (-0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0058: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0059: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 0060: dt=0.050000, rms=2.177 (-0.000%), neg=0, invalid=762 label assignment complete, 0 changed (0.00%) ********************* ALLOWING NEGATIVE NODES IN DEFORMATION******************************** **************** pass 1 of 1 ************************ enabling zero nodes setting smoothness coefficient to 0.008 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0061: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0062: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0063: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0064: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 0.031 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0065: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0066: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0067: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0068: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 0.118 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0069: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0070: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0071: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0072: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 0.400 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0073: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0074: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0075: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0076: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 1.000 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0077: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0078: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0079: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0080: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 resetting metric properties... setting smoothness coefficient to 2.000 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0081: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0082: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0083: dt=0.000000, rms=2.177 (0.184%), neg=0, invalid=762 0084: dt=0.000000, rms=2.177 (0.000%), neg=0, invalid=762 label assignment complete, 0 changed (0.00%) label assignment complete, 0 changed (0.00%) ***************** morphing with label term set to 0 ******************************* **************** pass 1 of 1 ************************ enabling zero nodes setting smoothness coefficient to 0.008 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0085: dt=4734.976000, rms=2.181 (0.000%), neg=0, invalid=762 0086: dt=4734.976000, rms=2.181 (-0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0087: dt=0.000000, rms=2.181 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 0.031 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0088: dt=0.000000, rms=2.181 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0089: dt=0.000000, rms=2.181 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 0.118 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0090: dt=716.800000, rms=2.181 (0.000%), neg=0, invalid=762 0091: dt=716.800000, rms=2.181 (0.000%), neg=0, invalid=762 0092: dt=716.800000, rms=2.181 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0093: dt=0.000000, rms=2.181 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 0.400 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0094: dt=46.080000, rms=2.181 (0.000%), neg=0, invalid=762 0095: dt=46.080000, rms=2.181 (0.000%), neg=0, invalid=762 0096: dt=46.080000, rms=2.181 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0097: dt=46.080000, rms=2.181 (0.000%), neg=0, invalid=762 0098: dt=46.080000, rms=2.181 (0.000%), neg=0, invalid=762 0099: dt=46.080000, rms=2.181 (0.000%), neg=0, invalid=762 setting smoothness coefficient to 1.000 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0100: dt=6.144000, rms=2.181 (0.000%), neg=0, invalid=762 0101: dt=6.144000, rms=2.181 (0.000%), neg=0, invalid=762 0102: dt=6.144000, rms=2.181 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0103: dt=7.168000, rms=2.181 (0.000%), neg=0, invalid=762 0104: dt=7.168000, rms=2.181 (0.000%), neg=0, invalid=762 0105: dt=7.168000, rms=2.181 (0.000%), neg=0, invalid=762 resetting metric properties... setting smoothness coefficient to 2.000 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0106: dt=0.000000, rms=2.181 (0.000%), neg=0, invalid=762 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=2.181, neg=0, invalid=762 0107: dt=0.000000, rms=2.181 (0.000%), neg=0, invalid=762 writing output transformation to transforms/talairach.m3z... GCAMwrite mri_ca_register took 0 hours, 32 minutes and 37 seconds. mri_ca_register utimesec 1955.343353 mri_ca_register stimesec 1.812429 mri_ca_register ru_maxrss 1303620 mri_ca_register ru_ixrss 0 mri_ca_register ru_idrss 0 mri_ca_register ru_isrss 0 mri_ca_register ru_minflt 901440 mri_ca_register ru_majflt 0 mri_ca_register ru_nswap 0 mri_ca_register ru_inblock 0 mri_ca_register ru_oublock 58176 mri_ca_register ru_msgsnd 0 mri_ca_register ru_msgrcv 0 mri_ca_register ru_nsignals 0 mri_ca_register ru_nvcsw 17 mri_ca_register ru_nivcsw 9528 FSRUNTIME@ mri_ca_register 0.5437 hours 1 threads #-------------------------------------- #@# SubCort Seg Tue Jul 26 11:39:17 UTC 2022 mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /usr/local/freesurfer/average/RB_all_2016-05-10.vc700.gca aseg.auto_noCCseg.mgz sysname Linux hostname gpu-ubuntu machine x86_64 setenv SUBJECTS_DIR /home/gianrocco cd /home/gianrocco/fs_ernie/mri mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /usr/local/freesurfer/average/RB_all_2016-05-10.vc700.gca aseg.auto_noCCseg.mgz == Number of threads available to mri_ca_label for OpenMP = 1 == 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 reading 1 input volumes reading classifier array from /usr/local/freesurfer/average/RB_all_2016-05-10.vc700.gca reading input volume from norm.mgz average std[0] = 7.3 reading transform from transforms/talairach.m3z setting orig areas to linear transform determinant scaled 3.28 Atlas used for the 3D morph was /usr/local/freesurfer/average/RB_all_2016-05-10.vc700.gca average std = 7.3 using min determinant for regularization = 5.3 0 singular and 0 ill-conditioned covariance matrices regularized labeling volume... renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.16259 (20) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.17677 (13) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.28129 (95) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.16930 (96) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.24553 (55) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.30264 (59) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.07580 (103) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.07714 (104) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.09712 (58) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.11620 (58) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.30970 (66) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.15280 (69) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.13902 (56) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.14777 (55) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.16765 (84) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.18739 (84) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.29869 (57) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.33601 (57) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.11131 (90) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.11793 (83) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.08324 (81) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.10360 (77) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.08424 (78) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.12631 (89) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.14500 (87) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.14975 (24) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.19357 (14) uniform distribution in MR - rejecting arbitrary fit gca peak Unknown = 0.94835 ( 0) gca peak Left_Cerebral_White_Matter = 0.07714 (104) gca peak Left_Cerebral_Cortex = 0.09712 (58) gca peak Left_Lateral_Ventricle = 0.16259 (20) gca peak Left_Inf_Lat_Vent = 0.16825 (27) gca peak Left_Cerebellum_White_Matter = 0.16765 (84) gca peak Left_Cerebellum_Cortex = 0.13902 (56) gca peak Left_Thalamus = 1.00000 (94) gca peak Left_Thalamus_Proper = 0.11131 (90) gca peak Left_Caudate = 0.15280 (69) gca peak Left_Putamen = 0.08324 (81) gca peak Left_Pallidum = 0.16930 (96) gca peak Third_Ventricle = 0.14975 (24) gca peak Fourth_Ventricle = 0.19357 (14) gca peak Brain_Stem = 0.08424 (78) gca peak Left_Hippocampus = 0.30264 (59) gca peak Left_Amygdala = 0.29869 (57) gca peak CSF = 0.23379 (36) gca peak Left_Accumbens_area = 0.70037 (62) gca peak Left_VentralDC = 0.14500 (87) gca peak Left_undetermined = 1.00000 (26) gca peak Left_vessel = 0.75997 (52) gca peak Left_choroid_plexus = 0.12089 (35) gca peak Right_Cerebral_White_Matter = 0.07580 (103) gca peak Right_Cerebral_Cortex = 0.11620 (58) gca peak Right_Lateral_Ventricle = 0.17677 (13) gca peak Right_Inf_Lat_Vent = 0.24655 (23) gca peak Right_Cerebellum_White_Matter = 0.18739 (84) gca peak Right_Cerebellum_Cortex = 0.14777 (55) gca peak Right_Thalamus_Proper = 0.11793 (83) gca peak Right_Caudate = 0.30970 (66) gca peak Right_Putamen = 0.10360 (77) gca peak Right_Pallidum = 0.28129 (95) gca peak Right_Hippocampus = 0.24553 (55) gca peak Right_Amygdala = 0.33601 (57) gca peak Right_Accumbens_area = 0.45042 (65) gca peak Right_VentralDC = 0.12631 (89) gca peak Right_vessel = 0.82168 (52) gca peak Right_choroid_plexus = 0.14516 (37) gca peak Fifth_Ventricle = 0.65475 (32) gca peak WM_hypointensities = 0.07854 (76) gca peak non_WM_hypointensities = 0.08491 (43) gca peak Optic_Chiasm = 0.71127 (75) 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 1.00 x + 0.0 setting left cbm cortex = 1.00 x + 0.00 setting right cbm cortex = 1.00 x + 0.00 saving intensity scales to aseg.auto_noCCseg.label_intensities.txt renormalizing by structure alignment.... renormalizing input #0 gca peak = 0.16259 (20) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.17677 (13) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.28129 (95) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.16930 (96) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.24553 (55) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.30264 (59) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.07580 (103) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.07714 (104) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.09712 (58) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.11620 (58) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.30970 (66) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.15280 (69) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.13902 (56) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.14777 (55) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.16765 (84) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.18739 (84) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.29869 (57) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.33601 (57) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.11131 (90) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.11793 (83) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.08324 (81) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.10360 (77) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.08424 (78) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.12631 (89) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.14500 (87) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.14975 (24) uniform distribution in MR - rejecting arbitrary fit gca peak = 0.19357 (14) uniform distribution in MR - rejecting arbitrary fit gca peak Unknown = 0.94835 ( 0) gca peak Left_Cerebral_White_Matter = 0.07714 (104) gca peak Left_Cerebral_Cortex = 0.09712 (58) gca peak Left_Lateral_Ventricle = 0.16259 (20) gca peak Left_Inf_Lat_Vent = 0.16825 (27) gca peak Left_Cerebellum_White_Matter = 0.16765 (84) gca peak Left_Cerebellum_Cortex = 0.13902 (56) gca peak Left_Thalamus = 1.00000 (94) gca peak Left_Thalamus_Proper = 0.11131 (90) gca peak Left_Caudate = 0.15280 (69) gca peak Left_Putamen = 0.08324 (81) gca peak Left_Pallidum = 0.16930 (96) gca peak Third_Ventricle = 0.14975 (24) gca peak Fourth_Ventricle = 0.19357 (14) gca peak Brain_Stem = 0.08424 (78) gca peak Left_Hippocampus = 0.30264 (59) gca peak Left_Amygdala = 0.29869 (57) gca peak CSF = 0.23379 (36) gca peak Left_Accumbens_area = 0.70037 (62) gca peak Left_VentralDC = 0.14500 (87) gca peak Left_undetermined = 1.00000 (26) gca peak Left_vessel = 0.75997 (52) gca peak Left_choroid_plexus = 0.12089 (35) gca peak Right_Cerebral_White_Matter = 0.07580 (103) gca peak Right_Cerebral_Cortex = 0.11620 (58) gca peak Right_Lateral_Ventricle = 0.17677 (13) gca peak Right_Inf_Lat_Vent = 0.24655 (23) gca peak Right_Cerebellum_White_Matter = 0.18739 (84) gca peak Right_Cerebellum_Cortex = 0.14777 (55) gca peak Right_Thalamus_Proper = 0.11793 (83) gca peak Right_Caudate = 0.30970 (66) gca peak Right_Putamen = 0.10360 (77) gca peak Right_Pallidum = 0.28129 (95) gca peak Right_Hippocampus = 0.24553 (55) gca peak Right_Amygdala = 0.33601 (57) gca peak Right_Accumbens_area = 0.45042 (65) gca peak Right_VentralDC = 0.12631 (89) gca peak Right_vessel = 0.82168 (52) gca peak Right_choroid_plexus = 0.14516 (37) gca peak Fifth_Ventricle = 0.65475 (32) gca peak WM_hypointensities = 0.07854 (76) gca peak non_WM_hypointensities = 0.08491 (43) gca peak Optic_Chiasm = 0.71127 (75) 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 1.00 x + 0.0 setting left cbm cortex = 1.00 x + 0.00 setting right cbm cortex = 1.00 x + 0.00 saving intensity scales to aseg.auto_noCCseg.label_intensities.txt saving sequentially combined intensity scales to aseg.auto_noCCseg.label_intensities.txt 9502 voxels changed in iteration 0 of unlikely voxel relabeling 52 voxels changed in iteration 1 of unlikely voxel relabeling 0 voxels changed in iteration 2 of unlikely voxel relabeling 71 gm and wm labels changed (%100 to gray, % 0 to white out of all changed labels) 177 hippocampal voxels changed. 0 amygdala voxels changed. pass 1: 15607 changed. image ll: -3.085, PF=0.500 pass 2: 2292 changed. 6938 voxels changed in iteration 0 of unlikely voxel relabeling 0 voxels changed in iteration 1 of unlikely voxel relabeling 3333 voxels changed in iteration 0 of unlikely voxel relabeling 1 voxels changed in iteration 1 of unlikely voxel relabeling 0 voxels changed in iteration 2 of unlikely voxel relabeling 2554 voxels changed in iteration 0 of unlikely voxel relabeling 23 voxels changed in iteration 1 of unlikely voxel relabeling 0 voxels changed in iteration 2 of unlikely voxel relabeling 4655 voxels changed in iteration 0 of unlikely voxel relabeling 11 voxels changed in iteration 1 of unlikely voxel relabeling 0 voxels changed in iteration 2 of unlikely voxel relabeling MRItoUCHAR: min=0, max=80 MRItoUCHAR: converting to UCHAR writing labeled volume to aseg.auto_noCCseg.mgz mri_ca_label utimesec 2011.692338 mri_ca_label stimesec 1.032203 mri_ca_label ru_maxrss 2123832 mri_ca_label ru_ixrss 0 mri_ca_label ru_idrss 0 mri_ca_label ru_isrss 0 mri_ca_label ru_minflt 513480 mri_ca_label ru_majflt 0 mri_ca_label ru_nswap 0 mri_ca_label ru_inblock 0 mri_ca_label ru_oublock 264 mri_ca_label ru_msgsnd 0 mri_ca_label ru_msgrcv 0 mri_ca_label ru_nsignals 0 mri_ca_label ru_nvcsw 79 mri_ca_label ru_nivcsw 8113 auto-labeling took 33 minutes and 33 seconds. mri_cc -aseg aseg.auto_noCCseg.mgz -o aseg.auto.mgz -lta /home/gianrocco/fs_ernie/mri/transforms/cc_up.lta fs_ernie will read input aseg from aseg.auto_noCCseg.mgz writing aseg with cc labels to aseg.auto.mgz will write lta as /home/gianrocco/fs_ernie/mri/transforms/cc_up.lta reading aseg from /home/gianrocco/fs_ernie/mri/aseg.auto_noCCseg.mgz reading norm from /home/gianrocco/fs_ernie/mri/norm.mgz 36164 voxels in left wm, 28854 in right wm, xrange [90, 126] searching rotation angles z=[20 34], y=[-14 0] searching scale 1 Z rot 20.5 searching scale 1 Z rot 20.7 searching scale 1 Z rot 21.0 searching scale 1 Z rot 21.2 searching scale 1 Z rot 21.5 searching scale 1 Z rot 21.7 searching scale 1 Z rot 22.0 searching scale 1 Z rot 22.2 searching scale 1 Z rot 22.5 searching scale 1 Z rot 22.7 searching scale 1 Z rot 23.0 searching scale 1 Z rot 23.2 searching scale 1 Z rot 23.5 searching scale 1 Z rot 23.7 searching scale 1 Z rot 24.0 searching scale 1 Z rot 24.2 searching scale 1 Z rot 24.5 searching scale 1 Z rot 24.7 searching scale 1 Z rot 25.0 searching scale 1 Z rot 25.2 searching scale 1 Z rot 25.5 searching scale 1 Z rot 25.7 searching scale 1 Z rot 26.0 searching scale 1 Z rot 26.2 searching scale 1 Z rot 26.5 searching scale 1 Z rot 26.7 searching scale 1 Z rot 27.0 searching scale 1 Z rot 27.2 searching scale 1 Z rot 27.5 searching scale 1 Z rot 27.7 searching scale 1 Z rot 28.0 searching scale 1 Z rot 28.2 searching scale 1 Z rot 28.5 searching scale 1 Z rot 28.7 searching scale 1 Z rot 29.0 searching scale 1 Z rot 29.2 searching scale 1 Z rot 29.5 searching scale 1 Z rot 29.7 searching scale 1 Z rot 30.0 searching scale 1 Z rot 30.2 searching scale 1 Z rot 30.5 searching scale 1 Z rot 30.7 searching scale 1 Z rot 31.0 searching scale 1 Z rot 31.2 global minimum found at slice 109.0, rotations (-9.36, 24.21) final transformation (x=109.0, yr=-9.364, zr=24.211): 0.89989 -0.41010 -0.14839 83.85104; 0.40464 0.91204 -0.06672 8.37192; 0.16270 0.00000 0.98668 -0.24192; 0.00000 0.00000 0.00000 1.00000; mri_cc: no WM voxels found with norm > 40 -- check skull stripping No such file or directory Linux gpu-ubuntu 5.13.0-1031-azure #37~20.04.1-Ubuntu SMP Mon Jun 13 22:51:01 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux recon-all -s fs_ernie exited with ERRORS at Tue Jul 26 12:13:39 UTC 2022 For more details, see the log file /home/gianrocco/fs_ernie/scripts/recon-all.log To report a problem, see http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting exiting mri2mesh after error