Hello FreeSurfer Developers,
I'm attempting to run recon-all after unpacking data. It is exiting with errors
- mri_cc: no WM voxels found with norm > 40 -- check skull stripping
I've attached the recon-all.log in case it's of any use.
1) FreeSurfer version:
freesurfer-Linux-centos7_x86_64-stable-v6-20161229-80ac5eb
2) Platform: centos7_x86_64
3) uname -a: Linux lemmiwinks.nmr.mgh.harvard.edu 3.10.0-1062.4.3.el7.x86_64 #1
SMP Wed Nov 13 23:58:53 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
4) recon-all.log: see attached
Thank you!
Mon Jan 13 10:46:26 EST 2020
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6
/usr/local/freesurfer/stable6/bin/recon-all
-s FS6 -i raw/MPRAGE/004/mprage.mgz -all
subjid FS6
setenv SUBJECTS_DIR /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons
FREESURFER_HOME /usr/local/freesurfer/stable6
Actual FREESURFER_HOME /autofs/cluster/freesurfer/centos7_x86_64/stable6
build-stamp.txt: freesurfer-Linux-centos7_x86_64-stable-v6-20161229-80ac5eb
Linux lemmiwinks.nmr.mgh.harvard.edu 3.10.0-1062.4.3.el7.x86_64 #1 SMP Wed Nov 13 23:58:53 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
cputime unlimited
filesize unlimited
datasize unlimited
stacksize unlimited
coredumpsize 0 kbytes
memoryuse unlimited
vmemoryuse unlimited
descriptors 65535
memorylocked 64 kbytes
maxproc 240728
maxlocks unlimited
maxsignal 240728
maxmessage 819200
maxnice 0
maxrtprio 0
maxrttime unlimited
total used free shared buff/cache available
Mem: 61668864 1925908 53876912 309808 5866044 58918992
Swap: 25165820 0 25165820
########################################
program versions used
$Id: recon-all,v 1.580.2.15 2016/12/08 22:02:41 zkaufman Exp $
$Id: mri_motion_correct.fsl,v 1.15 2016/02/16 17:17:20 zkaufman Exp $
mri_convert.bin -all-info
ProgramName: mri_convert.bin ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:26-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
FLIRT version 5.5
$Id: talairach_avi,v 1.13 2015/12/23 04:25:17 greve Exp $
mri_convert.bin --version
stable6
ProgramName: tkregister2_cmdl ProgramArguments: --all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:26-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: tkregister2.c,v 1.132.2.1 2016/08/02 21:17:29 greve Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
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
ProgramName: mri_make_uchar ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:26-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_make_uchar.c,v 1.4 2011/03/02 00:04:14 nicks Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_normalize ProgramArguments: -all-info ProgramVersion: $Name: $ TimeStamp: 2020/01/13-15:46:26-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_normalize.c,v 1.88.2.3 2016/12/27 16:47:13 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_watershed ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:26-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_watershed.cpp,v 1.103 2016/06/17 18:00:49 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_gcut ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:26-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_gcut.cpp,v 1.14 2011/03/02 00:04:16 nicks Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_segment ProgramArguments: -all-info ProgramVersion: $Name: $ TimeStamp: 2020/01/13-15:46:26-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_segment.c,v 1.43.2.1 2016/10/27 22:24:52 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_label2label.bin ProgramArguments: -all-info ProgramVersion: $Name: $ TimeStamp: 2020/01/13-15:46:26-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_label2label.c,v 1.48.2.2 2016/12/12 14:15:26 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_em_register ProgramArguments: -all-info ProgramVersion: $Name: $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_em_register.c,v 1.105.2.1 2016/10/27 22:25:10 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_ca_normalize ProgramArguments: -all-info ProgramVersion: $Name: $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_ca_normalize.c,v 1.67.2.2 2016/10/27 22:25:09 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_ca_register ProgramArguments: -all-info ProgramVersion: $Name: $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_ca_register.c,v 1.96.2.3 2016/10/27 22:25:10 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_ca_label ProgramArguments: -all-info ProgramVersion: $Name: $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_ca_label.c,v 1.113.2.2 2016/10/27 22:25:10 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_pretess ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_pretess.c,v 1.22 2013/08/30 18:12:25 mreuter Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_fill ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_fill.c,v 1.119 2011/10/25 14:09:58 fischl Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_tessellate ProgramArguments: -all-info ProgramVersion: $Name: $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_tessellate.c,v 1.38.2.1 2016/07/26 18:46:38 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_concatenate_lta.bin ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_concatenate_lta.c,v 1.16 2015/11/21 00:06:20 greve Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_normalize_tp2 ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_normalize_tp2.c,v 1.8 2011/03/02 00:04:23 nicks Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_smooth ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_smooth.c,v 1.30 2014/01/21 18:48:21 fischl Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_inflate ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_inflate.c,v 1.45 2016/01/20 23:42:15 greve Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_curvature ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_curvature.c,v 1.31 2011/03/02 00:04:30 nicks Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_sphere ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_sphere.c,v 1.61 2016/01/20 23:42:15 greve Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_fix_topology ProgramArguments: -all-info ProgramVersion: $Name: $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_fix_topology.c,v 1.50.2.1 2016/10/27 22:25:58 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_topo_fixer ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_topo_fixer.cpp,v 1.29 2011/03/02 00:04:34 nicks Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_ca_label ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:27-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_ca_label.c,v 1.37 2014/02/04 17:46:42 fischl Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_euler_number ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_euler_number.c,v 1.10 2013/01/14 22:39:14 greve Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_make_surfaces ProgramArguments: -all-info ProgramVersion: $Name: $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_make_surfaces.c,v 1.164.2.4 2016/12/13 22:26:32 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_register ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_register.c,v 1.63 2016/01/20 23:43:04 greve Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_volmask ProgramArguments: --all-info ProgramVersion: $Name: $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_volmask.cpp,v 1.26.2.2 2016/11/18 20:05:18 zkaufman Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_anatomical_stats ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_anatomical_stats.c,v 1.79 2016/03/14 15:15:34 greve Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mrisp_paint ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mrisp_paint.c,v 1.12 2016/03/22 14:47:57 fischl Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_curvature_stats ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_curvature_stats.c,v 1.65 2015/06/04 20:50:51 nicks Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mris_calc ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mris_calc.c,v 1.54.2.1 2016/09/27 18:51:28 greve Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
$Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $
ProgramName: mri_robust_register.bin ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
$Id: mri_robust_template.cpp,v 1.54 2016/05/05 21:17:08 mreuter Exp $
ProgramName: mri_robust_template ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_robust_template.cpp,v 1.54 2016/05/05 21:17:08 mreuter Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_and ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_and.c,v 1.4 2011/03/02 00:04:13 nicks Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_or ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_or.c,v 1.5 2013/03/20 15:03:29 lzollei Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_fuse_segmentations ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_fuse_segmentations.c,v 1.8 2011/03/02 00:04:15 nicks Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_segstats ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_segstats.c,v 1.121 2016/05/31 17:27:11 greve Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
ProgramName: mri_relabel_hypointensities ProgramArguments: -all-info ProgramVersion: $Name: stable6 $ TimeStamp: 2020/01/13-15:46:28-GMT BuildTimeStamp: Dec 29 2016 04:02:37 CVS: $Id: mri_relabel_hypointensities.c,v 1.13 2015/05/15 18:44:10 nicks Exp $ User: td744 Machine: lemmiwinks.nmr.mgh.harvard.edu Platform: Linux PlatformVersion: 3.10.0-1062.4.3.el7.x86_64 CompilerName: GCC CompilerVersion: 40800
#######################################
GCADIR /usr/local/freesurfer/stable6/average
GCA RB_all_2016-05-10.vc700.gca
GCASkull RB_all_withskull_2016-05-10.vc700.gca
AvgCurvTif folding.atlas.acfb40.noaparc.i12.2016-08-02.tif
GCSDIR /usr/local/freesurfer/stable6/average
GCS DKaparc.atlas.acfb40.noaparc.i12.2016-08-02.gcs
#######################################
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6
mri_convert /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/raw/MPRAGE/004/mprage.mgz /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/orig/001.mgz
mri_convert.bin /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/raw/MPRAGE/004/mprage.mgz /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/orig/001.mgz
$Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $
reading from /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/raw/MPRAGE/004/mprage.mgz...
TR=2300.00, TE=2.96, TI=900.00, flip angle=9.00
i_ras = (-0, -1, 0)
j_ras = (-0, -0, -1)
k_ras = (-1, -0, 0)
writing to /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/orig/001.mgz...
#--------------------------------------------
#@# MotionCor Mon Jan 13 10:46:32 EST 2020
Found 1 runs
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/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 /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/orig/001.mgz /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/rawavg.mgz
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6
mri_convert /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/rawavg.mgz /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/orig.mgz --conform
mri_convert.bin /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/rawavg.mgz /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/orig.mgz --conform
$Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $
reading from /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/rawavg.mgz...
TR=2300.00, TE=2.96, TI=900.00, flip angle=9.00
i_ras = (-0, -1, 0)
j_ras = (-0, -0, -1)
k_ras = (-1, -0, 0)
changing data type from short to uchar (noscale = 0)...
MRIchangeType: Building histogram
Reslicing using trilinear interpolation
writing to /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/orig.mgz...
mri_add_xform_to_header -c /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/transforms/talairach.xfm /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/orig.mgz /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/orig.mgz
INFO: extension is mgz
#--------------------------------------------
#@# Talairach Mon Jan 13 10:46:44 EST 2020
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
mri_nu_correct.mni --no-rescale --i orig.mgz --o orig_nu.mgz --n 1 --proto-iters 1000 --distance 50
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
/usr/local/freesurfer/stable6/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 lemmiwinks.nmr.mgh.harvard.edu 3.10.0-1062.4.3.el7.x86_64 #1 SMP Wed Nov 13 23:58:53 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
Mon Jan 13 10:46:44 EST 2020
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.18442
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
mri_convert orig.mgz ./tmp.mri_nu_correct.mni.18442/nu0.mnc -odt float
mri_convert.bin orig.mgz ./tmp.mri_nu_correct.mni.18442/nu0.mnc -odt float
$Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $
reading from orig.mgz...
TR=2300.00, TE=2.96, TI=900.00, flip angle=9.00
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.18442/nu0.mnc...
--------------------------------------------------------
Iteration 1 Mon Jan 13 10:46:47 EST 2020
nu_correct -clobber ./tmp.mri_nu_correct.mni.18442/nu0.mnc ./tmp.mri_nu_correct.mni.18442/nu1.mnc -tmpdir ./tmp.mri_nu_correct.mni.18442/0/ -iterations 1000 -distance 50
[td...@lemmiwinks.nmr.mgh.harvard.edu:/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/] [2020-01-13 10:46:47] running:
/usr/local/freesurfer/stable6/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.18442/0/ ./tmp.mri_nu_correct.mni.18442/nu0.mnc ./tmp.mri_nu_correct.mni.18442/nu1.imp
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Number of iterations: 44
CV of field change: 0.000992581
mri_convert ./tmp.mri_nu_correct.mni.18442/nu1.mnc orig_nu.mgz --like orig.mgz --conform
mri_convert.bin ./tmp.mri_nu_correct.mni.18442/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.18442/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...
Mon Jan 13 10:48:10 EST 2020
mri_nu_correct.mni done
talairach_avi --i orig_nu.mgz --xfm transforms/talairach.auto.xfm
talairach_avi log file is transforms/talairach_avi.log...
Started at Mon Jan 13 10:48:10 EST 2020
Ended at Mon Jan 13 10:48:50 EST 2020
talairach_avi done
cp transforms/talairach.auto.xfm transforms/talairach.xfm
#--------------------------------------------
#@# Talairach Failure Detection Mon Jan 13 10:48:52 EST 2020
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
talairach_afd -T 0.005 -xfm transforms/talairach.xfm
talairach_afd: Talairach Transform: transforms/talairach.xfm OK (p=0.7698, pval=0.6675 >= threshold=0.0050)
awk -f /usr/local/freesurfer/stable6/bin/extract_talairach_avi_QA.awk /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/transforms/talairach_avi.log
tal_QC_AZS /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/transforms/talairach_avi.log
TalAviQA: 0.97889
z-score: 0
#--------------------------------------------
#@# Nu Intensity Correction Mon Jan 13 10:48:52 EST 2020
mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --n 2
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
/usr/local/freesurfer/stable6/bin/mri_nu_correct.mni
--i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --n 2
nIters 2
$Id: mri_nu_correct.mni,v 1.27 2016/02/26 16:19:49 mreuter Exp $
Linux lemmiwinks.nmr.mgh.harvard.edu 3.10.0-1062.4.3.el7.x86_64 #1 SMP Wed Nov 13 23:58:53 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
Mon Jan 13 10:48:52 EST 2020
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.19352
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
mri_convert orig.mgz ./tmp.mri_nu_correct.mni.19352/nu0.mnc -odt float
mri_convert.bin orig.mgz ./tmp.mri_nu_correct.mni.19352/nu0.mnc -odt float
$Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $
reading from orig.mgz...
TR=2300.00, TE=2.96, TI=900.00, flip angle=9.00
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.19352/nu0.mnc...
--------------------------------------------------------
Iteration 1 Mon Jan 13 10:48:55 EST 2020
nu_correct -clobber ./tmp.mri_nu_correct.mni.19352/nu0.mnc ./tmp.mri_nu_correct.mni.19352/nu1.mnc -tmpdir ./tmp.mri_nu_correct.mni.19352/0/
[td...@lemmiwinks.nmr.mgh.harvard.edu:/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/] [2020-01-13 10:48:55] running:
/usr/local/freesurfer/stable6/mni/bin/nu_estimate_np_and_em -parzen -log -sharpen 0.15 0.01 -iterations 50 -stop 0.001 -shrink 4 -auto_mask -nonotify -b_spline 1.0e-7 -distance 200 -quiet -execute -clobber -nokeeptmp -tmpdir ./tmp.mri_nu_correct.mni.19352/0/ ./tmp.mri_nu_correct.mni.19352/nu0.mnc ./tmp.mri_nu_correct.mni.19352/nu1.imp
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Number of iterations: 46
CV of field change: 0.000981903
--------------------------------------------------------
Iteration 2 Mon Jan 13 10:49:57 EST 2020
nu_correct -clobber ./tmp.mri_nu_correct.mni.19352/nu1.mnc ./tmp.mri_nu_correct.mni.19352/nu2.mnc -tmpdir ./tmp.mri_nu_correct.mni.19352/1/
[td...@lemmiwinks.nmr.mgh.harvard.edu:/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/] [2020-01-13 10:49:57] running:
/usr/local/freesurfer/stable6/mni/bin/nu_estimate_np_and_em -parzen -log -sharpen 0.15 0.01 -iterations 50 -stop 0.001 -shrink 4 -auto_mask -nonotify -b_spline 1.0e-7 -distance 200 -quiet -execute -clobber -nokeeptmp -tmpdir ./tmp.mri_nu_correct.mni.19352/1/ ./tmp.mri_nu_correct.mni.19352/nu1.mnc ./tmp.mri_nu_correct.mni.19352/nu2.imp
Processing:.................................................................Done
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Number of iterations: 19
CV of field change: 0.000982866
mri_binarize --i ./tmp.mri_nu_correct.mni.19352/nu2.mnc --min -1 --o ./tmp.mri_nu_correct.mni.19352/ones.mgz
$Id: mri_binarize.c,v 1.43 2016/06/09 20:46:21 greve Exp $
cwd /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
cmdline mri_binarize.bin --i ./tmp.mri_nu_correct.mni.19352/nu2.mnc --min -1 --o ./tmp.mri_nu_correct.mni.19352/ones.mgz
sysname Linux
hostname lemmiwinks.nmr.mgh.harvard.edu
machine x86_64
user td744
input ./tmp.mri_nu_correct.mni.19352/nu2.mnc
frame 0
nErode3d 0
nErode2d 0
output ./tmp.mri_nu_correct.mni.19352/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.19352/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.19352/sum.junk --avgwf ./tmp.mri_nu_correct.mni.19352/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.19352/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.19352/sum.junk --avgwf ./tmp.mri_nu_correct.mni.19352/input.mean.dat
sysname Linux
hostname lemmiwinks.nmr.mgh.harvard.edu
machine x86_64
user td744
UseRobust 0
Loading ./tmp.mri_nu_correct.mni.19352/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.19352/input.mean.dat
mri_segstats done
mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.19352/ones.mgz --i ./tmp.mri_nu_correct.mni.19352/nu2.mnc --sum ./tmp.mri_nu_correct.mni.19352/sum.junk --avgwf ./tmp.mri_nu_correct.mni.19352/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.19352/ones.mgz --i ./tmp.mri_nu_correct.mni.19352/nu2.mnc --sum ./tmp.mri_nu_correct.mni.19352/sum.junk --avgwf ./tmp.mri_nu_correct.mni.19352/output.mean.dat
sysname Linux
hostname lemmiwinks.nmr.mgh.harvard.edu
machine x86_64
user td744
UseRobust 0
Loading ./tmp.mri_nu_correct.mni.19352/ones.mgz
Loading ./tmp.mri_nu_correct.mni.19352/nu2.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.19352/output.mean.dat
mri_segstats done
mris_calc -o ./tmp.mri_nu_correct.mni.19352/nu2.mnc ./tmp.mri_nu_correct.mni.19352/nu2.mnc mul 1.00611939938923528661
Saving result to './tmp.mri_nu_correct.mni.19352/nu2.mnc' (type = MINC ) [ ok ]
mri_convert ./tmp.mri_nu_correct.mni.19352/nu2.mnc nu.mgz --like orig.mgz
mri_convert.bin ./tmp.mri_nu_correct.mni.19352/nu2.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.19352/nu2.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 11 seconds.
mapping ( 8, 142) to ( 3, 110)
Mon Jan 13 10:51:25 EST 2020
mri_nu_correct.mni done
mri_add_xform_to_header -c /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/transforms/talairach.xfm nu.mgz nu.mgz
INFO: extension is mgz
#--------------------------------------------
#@# Intensity Normalization Mon Jan 13 10:51:26 EST 2020
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
mri_normalize -g 1 -mprage nu.mgz T1.mgz
using max gradient = 1.000
assuming input volume is MGH (Van der Kouwe) MP-RAGE
reading from nu.mgz...
normalizing image...
talairach transform
1.02774 0.06281 0.02630 -1.89609;
-0.04769 1.08921 0.11363 -39.57262;
-0.04738 -0.07843 1.17599 -12.67232;
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 = 19
Starting OpenSpline(): npoints = 19
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 105
gm peak at 66 (66), valley at 32 (32)
csf peak at 33, setting threshold to 55
building Voronoi diagram...
performing soap bubble smoothing, sigma = 8...
---------------------------------
3d normalization pass 2 of 2
white matter peak found at 110
white matter peak found at 110
gm peak at 64 (64), valley at 31 (31)
csf peak at 32, setting threshold to 53
building Voronoi diagram...
performing soap bubble smoothing, sigma = 8...
Done iterating ---------------------------------
writing output to T1.mgz
3D bias adjustment took 2 minutes and 56 seconds.
#--------------------------------------------
#@# Skull Stripping Mon Jan 13 10:54:23 EST 2020
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
mri_em_register -rusage /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/touch/rusage.mri_em_register.skull.dat -skull nu.mgz /usr/local/freesurfer/stable6/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/stable6/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=5.0
skull bounding box = (43, 101, 26) --> (211, 255, 220)
using (99, 152, 123) as brain centroid...
mean wm in atlas = 108, using box (78,133,99) --> (119, 170,146) to find MRI wm
before smoothing, mri peak at 102
robust fit to distribution - 102 +- 7.3
after smoothing, mri peak at 102, scaling input intensities by 1.059
scaling channel 0 by 1.05882
initial log_p = -5.037
************************************************
First Search limited to translation only.
************************************************
max log p = -4.580850 @ (-9.091, -45.455, -9.091)
max log p = -4.419268 @ (4.545, -4.545, -4.545)
max log p = -4.362124 @ (2.273, -2.273, -2.273)
max log p = -4.348638 @ (1.136, -1.136, 1.136)
max log p = -4.313070 @ (-0.568, 0.568, -0.568)
max log p = -4.313070 @ (0.000, 0.000, 0.000)
Found translation: (-1.7, -52.8, -15.3): log p = -4.313
****************************************
Nine parameter search. iteration 0 nscales = 0 ...
****************************************
Result so far: scale 1.000: max_log_p=-3.988, old_max_log_p =-4.313 (thresh=-4.3)
1.06375 0.00000 0.00000 -9.77401;
0.00000 1.22567 0.16136 -117.22506;
0.00000 -0.15011 1.14016 3.73448;
0.00000 0.00000 0.00000 1.00000;
****************************************
Nine parameter search. iteration 1 nscales = 0 ...
****************************************
Result so far: scale 1.000: max_log_p=-3.939, old_max_log_p =-3.988 (thresh=-4.0)
1.05465 0.15998 0.02106 -42.48047;
-0.14926 1.30633 0.17198 -115.40096;
0.00000 -0.13885 1.05465 11.77363;
0.00000 0.00000 0.00000 1.00000;
****************************************
Nine parameter search. iteration 2 nscales = 0 ...
****************************************
Result so far: scale 1.000: max_log_p=-3.939, old_max_log_p =-3.939 (thresh=-3.9)
1.05465 0.15998 0.02106 -42.48047;
-0.14926 1.30633 0.17198 -115.40096;
0.00000 -0.13885 1.05465 11.77363;
0.00000 0.00000 0.00000 1.00000;
reducing scale to 0.2500
****************************************
Nine parameter search. iteration 3 nscales = 1 ...
****************************************
Result so far: scale 0.250: max_log_p=-3.839, old_max_log_p =-3.939 (thresh=-3.9)
1.02439 0.02700 -0.01488 -10.17288;
-0.04058 1.27359 0.06423 -111.71187;
0.02002 0.00098 1.08592 -23.76962;
0.00000 0.00000 0.00000 1.00000;
****************************************
Nine parameter search. iteration 4 nscales = 1 ...
****************************************
Result so far: scale 0.250: max_log_p=-3.828, old_max_log_p =-3.839 (thresh=-3.8)
1.02319 0.02696 -0.05040 -5.69355;
-0.03982 1.24971 0.06303 -108.82650;
0.05353 0.00187 1.08485 -28.11615;
0.00000 0.00000 0.00000 1.00000;
****************************************
Nine parameter search. iteration 5 nscales = 1 ...
****************************************
Result so far: scale 0.250: max_log_p=-3.828, old_max_log_p =-3.828 (thresh=-3.8)
1.02319 0.02696 -0.05040 -5.69355;
-0.03982 1.24971 0.06303 -108.82650;
0.05353 0.00187 1.08485 -28.11615;
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=-3.809, old_max_log_p =-3.828 (thresh=-3.8)
1.02359 0.04746 -0.04942 -9.00207;
-0.05636 1.24471 0.06362 -104.84605;
0.05353 0.00187 1.08485 -28.11615;
0.00000 0.00000 0.00000 1.00000;
****************************************
Nine parameter search. iteration 7 nscales = 2 ...
****************************************
Result so far: scale 0.062: max_log_p=-3.795, old_max_log_p =-3.809 (thresh=-3.8)
1.02117 0.03688 -0.03223 -10.08512;
-0.04716 1.24784 0.08129 -107.37074;
0.03769 -0.01927 1.08433 -21.37857;
0.00000 0.00000 0.00000 1.00000;
****************************************
Nine parameter search. iteration 8 nscales = 2 ...
****************************************
Result so far: scale 0.062: max_log_p=-3.791, old_max_log_p =-3.795 (thresh=-3.8)
1.02117 0.03688 -0.03223 -10.08512;
-0.04700 1.24345 0.08101 -106.49411;
0.03764 -0.01925 1.08306 -21.22353;
0.00000 0.00000 0.00000 1.00000;
****************************************
Nine parameter search. iteration 9 nscales = 2 ...
****************************************
Result so far: scale 0.062: max_log_p=-3.788, old_max_log_p =-3.791 (thresh=-3.8)
1.02117 0.03688 -0.03223 -10.08512;
-0.04694 1.24199 0.08091 -106.20201;
0.03777 -0.01932 1.08687 -21.68846;
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
1.02117 0.03688 -0.03223 -10.08512;
-0.04694 1.24199 0.08091 -106.20201;
0.03777 -0.01932 1.08687 -21.68846;
0.00000 0.00000 0.00000 1.00000;
nsamples 3243
Quasinewton: input matrix
1.02117 0.03688 -0.03223 -10.08512;
-0.04694 1.24199 0.08091 -106.20201;
0.03777 -0.01932 1.08687 -21.68846;
0.00000 0.00000 0.00000 1.00000;
outof QuasiNewtonEMA: 012: -log(p) = -0.0 tol 0.000010
Resulting transform:
1.02117 0.03688 -0.03223 -10.08512;
-0.04694 1.24199 0.08091 -106.20201;
0.03777 -0.01932 1.08687 -21.68846;
0.00000 0.00000 0.00000 1.00000;
pass 1, spacing 8: log(p) = -3.788 (old=-5.037)
transform before final EM align:
1.02117 0.03688 -0.03223 -10.08512;
-0.04694 1.24199 0.08091 -106.20201;
0.03777 -0.01932 1.08687 -21.68846;
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
1.02117 0.03688 -0.03223 -10.08512;
-0.04694 1.24199 0.08091 -106.20201;
0.03777 -0.01932 1.08687 -21.68846;
0.00000 0.00000 0.00000 1.00000;
nsamples 364799
Quasinewton: input matrix
1.02117 0.03688 -0.03223 -10.08512;
-0.04694 1.24199 0.08091 -106.20201;
0.03777 -0.01932 1.08687 -21.68846;
0.00000 0.00000 0.00000 1.00000;
outof QuasiNewtonEMA: 014: -log(p) = 4.2 tol 0.000000
final transform:
1.02117 0.03688 -0.03223 -10.08512;
-0.04694 1.24199 0.08091 -106.20201;
0.03777 -0.01932 1.08687 -21.68846;
0.00000 0.00000 0.00000 1.00000;
writing output transformation to transforms/talairach_with_skull.lta...
mri_em_register utimesec 1591.459167
mri_em_register stimesec 3.195054
mri_em_register ru_maxrss 609860
mri_em_register ru_ixrss 0
mri_em_register ru_idrss 0
mri_em_register ru_isrss 0
mri_em_register ru_minflt 157380
mri_em_register ru_majflt 1
mri_em_register ru_nswap 0
mri_em_register ru_inblock 149760
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 67
mri_em_register ru_nivcsw 5473
registration took 26 minutes and 35 seconds.
mri_watershed -rusage /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/touch/rusage.mri_watershed.dat -T1 -brain_atlas /usr/local/freesurfer/stable6/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=127 y=170 z=120 r=71
first estimation of the main basin volume: 1544218 voxels
Looking for seedpoints
2 found in the cerebellum
15 found in the rest of the brain
global maximum in x=149, y=166, z=87, Imax=255
CSF=16, 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=9346969007 voxels, voxel volume =1.000
= 9346969007 mmm3 = 9346968.576 cm3
done.
PostAnalyze...Basin Prior
102 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=128,y=178, z=115, r=9459 iterations
^^^^^^^^ couldn't find WM with original limits - expanding ^^^^^^
GLOBAL CSF_MIN=1, CSF_intensity=2, CSF_MAX=28 , nb = 45540
RIGHT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=24 , nb = 3150
LEFT_CER CSF_MIN=1, CSF_intensity=2, CSF_MAX=23 , nb = 2916
RIGHT_BRAIN CSF_MIN=1, CSF_intensity=2, CSF_MAX=24 , nb = 19764
LEFT_BRAIN CSF_MIN=1, CSF_intensity=2, CSF_MAX=28 , nb = 18936
OTHER CSF_MIN=0, CSF_intensity=7, CSF_MAX=44 , nb = 774
Problem with the least square interpolation in GM_MIN calculation.
CSF_MAX TRANSITION GM_MIN GM
GLOBAL
before analyzing : 28, 32, 37, 59
after analyzing : 28, 35, 37, 41
RIGHT_CER
before analyzing : 24, 36, 47, 64
after analyzing : 24, 43, 47, 48
LEFT_CER
before analyzing : 23, 29, 37, 59
after analyzing : 23, 34, 37, 40
RIGHT_BRAIN
before analyzing : 24, 29, 36, 60
after analyzing : 24, 33, 36, 39
LEFT_BRAIN
before analyzing : 28, 32, 37, 59
after analyzing : 28, 35, 37, 41
OTHER
before analyzing : 44, 81, 88, 95
after analyzing : 44, 85, 88, 87
mri_strip_skull: done peeling brain
highly tesselated surface with 10242 vertices
matching...68 iterations
*********************VALIDATION*********************
curvature mean = -0.014, std = 0.012
curvature mean = 67.614, std = 7.710
No Rigid alignment: -atlas Mode Off (basic atlas / no registration)
before rotation: sse = 5.17, sigma = 7.43
after rotation: sse = 5.17, sigma = 7.43
Localization of inacurate regions: Erosion-Dilation steps
the sse mean is 5.56, its var is 6.74
before Erosion-Dilatation 1.78% of inacurate vertices
after Erosion-Dilatation 0.00% of inacurate vertices
Validation of the shape of the surface done.
Scaling of atlas fields onto current surface fields
********FINAL ITERATIVE TEMPLATE DEFORMATION********
Compute Local values csf/gray
Fine Segmentation...50 iterations
mri_strip_skull: done peeling brain
Brain Size = 1546548 voxels, voxel volume = 1.000 mm3
= 1546548 mmm3 = 1546.548 cm3
******************************
Saving brainmask.auto.mgz
done
mri_watershed utimesec 28.086126
mri_watershed stimesec 0.430078
mri_watershed ru_maxrss 825240
mri_watershed ru_ixrss 0
mri_watershed ru_idrss 0
mri_watershed ru_isrss 0
mri_watershed ru_minflt 211668
mri_watershed ru_majflt 1
mri_watershed ru_nswap 0
mri_watershed ru_inblock 9224
mri_watershed ru_oublock 2664
mri_watershed ru_msgsnd 0
mri_watershed ru_msgrcv 0
mri_watershed ru_nsignals 0
mri_watershed ru_nvcsw 140
mri_watershed ru_nivcsw 18
mri_watershed done
cp brainmask.auto.mgz brainmask.mgz
#-------------------------------------
#@# EM Registration Mon Jan 13 11:21:28 EST 2020
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
mri_em_register -rusage /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/touch/rusage.mri_em_register.dat -uns 3 -mask brainmask.mgz nu.mgz /usr/local/freesurfer/stable6/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/stable6/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=20.9
skull bounding box = (57, 125, 42) --> (200, 235, 199)
using (105, 162, 121) as brain centroid...
mean wm in atlas = 107, using box (87,149,102) --> (122, 175,140) to find MRI wm
before smoothing, mri peak at 104
robust fit to distribution - 103 +- 6.3
after smoothing, mri peak at 103, scaling input intensities by 1.039
scaling channel 0 by 1.03883
initial log_p = -4.726
************************************************
First Search limited to translation only.
************************************************
max log p = -4.242296 @ (-9.091, -45.455, -9.091)
max log p = -4.053308 @ (4.545, -4.545, -4.545)
max log p = -4.001093 @ (2.273, 2.273, 2.273)
max log p = -3.931850 @ (1.136, -3.409, -1.136)
max log p = -3.915412 @ (-0.568, 0.568, 0.568)
max log p = -3.915412 @ (0.000, 0.000, 0.000)
Found translation: (-1.7, -50.6, -11.9): log p = -3.915
****************************************
Nine parameter search. iteration 0 nscales = 0 ...
****************************************
Result so far: scale 1.000: max_log_p=-3.744, old_max_log_p =-3.915 (thresh=-3.9)
0.99144 0.13053 0.00000 -22.77666;
-0.14032 1.06580 0.00000 -51.22304;
0.00000 0.00000 1.06375 -19.41455;
0.00000 0.00000 0.00000 1.00000;
****************************************
Nine parameter search. iteration 1 nscales = 0 ...
****************************************
Result so far: scale 1.000: max_log_p=-3.636, old_max_log_p =-3.744 (thresh=-3.7)
0.99562 -0.03168 -0.13766 21.52342;
-0.02930 1.13967 -0.01676 -76.47118;
0.12941 0.01704 1.05465 -37.86182;
0.00000 0.00000 0.00000 1.00000;
****************************************
Nine parameter search. iteration 2 nscales = 0 ...
****************************************
Result so far: scale 1.000: max_log_p=-3.636, old_max_log_p =-3.636 (thresh=-3.6)
0.99562 -0.03168 -0.13766 21.52342;
-0.02930 1.13967 -0.01676 -76.47118;
0.12941 0.01704 1.05465 -37.86182;
0.00000 0.00000 0.00000 1.00000;
reducing scale to 0.2500
****************************************
Nine parameter search. iteration 3 nscales = 1 ...
****************************************
Result so far: scale 0.250: max_log_p=-3.473, old_max_log_p =-3.636 (thresh=-3.6)
0.99869 0.03899 -0.06263 -2.28073;
-0.08569 1.15711 0.09291 -88.92015;
0.06754 -0.09429 1.07780 -9.06441;
0.00000 0.00000 0.00000 1.00000;
****************************************
Nine parameter search. iteration 4 nscales = 1 ...
****************************************
Result so far: scale 0.250: max_log_p=-3.473, old_max_log_p =-3.473 (thresh=-3.5)
0.99869 0.03899 -0.06263 -2.28073;
-0.08569 1.15711 0.09291 -88.92015;
0.06754 -0.09429 1.07780 -9.06441;
0.00000 0.00000 0.00000 1.00000;
reducing scale to 0.0625
****************************************
Nine parameter search. iteration 5 nscales = 2 ...
****************************************
Result so far: scale 0.062: max_log_p=-3.450, old_max_log_p =-3.473 (thresh=-3.5)
1.00125 0.03632 -0.03624 -4.20740;
-0.08530 1.16037 0.10208 -90.59559;
0.04386 -0.10505 1.08201 -5.48932;
0.00000 0.00000 0.00000 1.00000;
****************************************
Nine parameter search. iteration 6 nscales = 2 ...
****************************************
Result so far: scale 0.062: max_log_p=-3.450, old_max_log_p =-3.450 (thresh=-3.4)
1.00125 0.03632 -0.03624 -4.20740;
-0.08530 1.16037 0.10208 -90.59559;
0.04386 -0.10505 1.08201 -5.48932;
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
1.00125 0.03632 -0.03624 -4.20740;
-0.08530 1.16037 0.10208 -90.59559;
0.04386 -0.10505 1.08201 -5.48932;
0.00000 0.00000 0.00000 1.00000;
nsamples 2830
Quasinewton: input matrix
1.00125 0.03632 -0.03624 -4.20740;
-0.08530 1.16037 0.10208 -90.59559;
0.04386 -0.10505 1.08201 -5.48932;
0.00000 0.00000 0.00000 1.00000;
outof QuasiNewtonEMA: 009: -log(p) = -0.0 tol 0.000010
Resulting transform:
1.00125 0.03632 -0.03624 -4.20740;
-0.08530 1.16037 0.10208 -90.59559;
0.04386 -0.10505 1.08201 -5.48932;
0.00000 0.00000 0.00000 1.00000;
pass 1, spacing 8: log(p) = -3.450 (old=-4.726)
transform before final EM align:
1.00125 0.03632 -0.03624 -4.20740;
-0.08530 1.16037 0.10208 -90.59559;
0.04386 -0.10505 1.08201 -5.48932;
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
1.00125 0.03632 -0.03624 -4.20740;
-0.08530 1.16037 0.10208 -90.59559;
0.04386 -0.10505 1.08201 -5.48932;
0.00000 0.00000 0.00000 1.00000;
nsamples 315557
Quasinewton: input matrix
1.00125 0.03632 -0.03624 -4.20740;
-0.08530 1.16037 0.10208 -90.59559;
0.04386 -0.10505 1.08201 -5.48932;
0.00000 0.00000 0.00000 1.00000;
dfp_em_step_func: 010: -log(p) = 8.1
after pass:transform: ( 0.99, 0.04, -0.12, -4.21)
( 0.46, 1.84, 0.54, -90.60)
( 0.09, 0.04, 1.18, -5.49)
THE SEARCH DIRECTION IS NOT A DESCENT DIRECTION
pass 2 through quasi-newton minimization...
THE SEARCH DIRECTION IS NOT A DESCENT DIRECTION
outof QuasiNewtonEMA: 012: -log(p) = 8.1 tol 0.000000
final transform:
0.98781 0.03541 -0.12316 -4.20740;
0.46473 1.84231 0.53944 -90.59559;
0.09368 0.04027 1.18099 -5.48932;
0.00000 0.00000 0.00000 1.00000;
writing output transformation to transforms/talairach.lta...
mri_em_register utimesec 962.264122
mri_em_register stimesec 2.355953
mri_em_register ru_maxrss 598988
mri_em_register ru_ixrss 0
mri_em_register ru_idrss 0
mri_em_register ru_isrss 0
mri_em_register ru_minflt 159147
mri_em_register ru_majflt 0
mri_em_register ru_nswap 0
mri_em_register ru_inblock 140152
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 72
mri_em_register ru_nivcsw 1317
registration took 16 minutes and 5 seconds.
#--------------------------------------
#@# CA Normalize Mon Jan 13 11:37:33 EST 2020
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
mri_ca_normalize -c ctrl_pts.mgz -mask brainmask.mgz nu.mgz /usr/local/freesurfer/stable6/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/stable6/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=20.9
skull bounding box = (57, 125, 42) --> (200, 235, 199)
using (105, 162, 121) as brain centroid...
mean wm in atlas = 107, using box (87,149,102) --> (122, 175,140) to find MRI wm
before smoothing, mri peak at 104
robust fit to distribution - 103 +- 6.3
after smoothing, mri peak at 103, scaling input intensities by 1.039
scaling channel 0 by 1.03883
using 246344 sample points...
INFO: compute sample coordinates transform
0.98781 0.03541 -0.12316 -4.20740;
0.46473 1.84231 0.53944 -90.59559;
0.09368 0.04027 1.18099 -5.48932;
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 (131, 9, 12) --> (207, 91, 155)
finding control points in Right_Cerebral_White_Matter....
found 39557 control points for structure...
bounding box (73, 15, 15) --> (146, 94, 157)
finding control points in Left_Cerebellum_White_Matter....
found 3059 control points for structure...
bounding box (136, 71, 30) --> (186, 96, 76)
finding control points in Right_Cerebellum_White_Matter....
found 2705 control points for structure...
bounding box (90, 74, 30) --> (137, 106, 80)
finding control points in Brain_Stem....
found 3518 control points for structure...
bounding box (123, 60, 59) --> (157, 104, 88)
skipping region 1 with no control points detected
finding control points in Left_Cerebral_White_Matter....
found 39915 control points for structure...
bounding box (131, 9, 12) --> (207, 91, 155)
finding control points in Right_Cerebral_White_Matter....
found 39557 control points for structure...
bounding box (73, 15, 15) --> (146, 94, 157)
finding control points in Left_Cerebellum_White_Matter....
found 3059 control points for structure...
bounding box (136, 71, 30) --> (186, 96, 76)
finding control points in Right_Cerebellum_White_Matter....
found 2705 control points for structure...
bounding box (90, 74, 30) --> (137, 106, 80)
finding control points in Brain_Stem....
found 3518 control points for structure...
bounding box (123, 60, 59) --> (157, 104, 88)
skipping region 2 with no control points detected
finding control points in Left_Cerebral_White_Matter....
found 39915 control points for structure...
bounding box (131, 9, 12) --> (207, 91, 155)
finding control points in Right_Cerebral_White_Matter....
found 39557 control points for structure...
bounding box (73, 15, 15) --> (146, 94, 157)
finding control points in Left_Cerebellum_White_Matter....
found 3059 control points for structure...
bounding box (136, 71, 30) --> (186, 96, 76)
finding control points in Right_Cerebellum_White_Matter....
found 2705 control points for structure...
bounding box (90, 74, 30) --> (137, 106, 80)
finding control points in Brain_Stem....
found 3518 control points for structure...
bounding box (123, 60, 59) --> (157, 104, 88)
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 30 seconds.
#--------------------------------------
#@# CA Reg Mon Jan 13 11:38:03 EST 2020
/autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
mri_ca_register -rusage /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/touch/rusage.mri_ca_register.dat -nobigventricles -T transforms/talairach.lta -align-after -mask brainmask.mgz norm.mgz /usr/local/freesurfer/stable6/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/stable6/average/RB_all_2016-05-10.vc700.gca'...
label assignment complete, 0 changed (0.00%)
det(m_affine) = 2.13 (predicted orig area = 3.8)
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=5.120000, rms=2.181 (0.000%), neg=0, invalid=762
0101: dt=5.120000, rms=2.181 (0.000%), neg=0, invalid=762
0102: dt=5.120000, 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=16.384000, rms=2.181 (0.000%), neg=0, invalid=762
0104: dt=16.384000, 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
0105: 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
0106: dt=0.000000, rms=2.181 (0.000%), neg=0, invalid=762
writing output transformation to transforms/talairach.m3z...
GCAMwrite
mri_ca_register took 1 hours, 4 minutes and 53 seconds.
mri_ca_register utimesec 3889.775684
mri_ca_register stimesec 2.460031
mri_ca_register ru_maxrss 1311848
mri_ca_register ru_ixrss 0
mri_ca_register ru_idrss 0
mri_ca_register ru_isrss 0
mri_ca_register ru_minflt 1006615
mri_ca_register ru_majflt 0
mri_ca_register ru_nswap 0
mri_ca_register ru_inblock 232
mri_ca_register ru_oublock 58688
mri_ca_register ru_msgsnd 0
mri_ca_register ru_msgrcv 0
mri_ca_register ru_nsignals 0
mri_ca_register ru_nvcsw 285
mri_ca_register ru_nivcsw 7962
FSRUNTIME@ mri_ca_register 1.0813 hours 1 threads
#--------------------------------------
#@# SubCort Seg Mon Jan 13 12:42:56 EST 2020
mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /usr/local/freesurfer/stable6/average/RB_all_2016-05-10.vc700.gca aseg.auto_noCCseg.mgz
sysname Linux
hostname lemmiwinks.nmr.mgh.harvard.edu
machine x86_64
setenv SUBJECTS_DIR /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons
cd /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri
mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /usr/local/freesurfer/stable6/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/stable6/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 3.76
Atlas used for the 3D morph was /usr/local/freesurfer/stable6/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
9061 voxels changed in iteration 0 of unlikely voxel relabeling
0 voxels changed in iteration 1 of unlikely voxel relabeling
144 gm and wm labels changed (%100 to gray, % 0 to white out of all changed labels)
231 hippocampal voxels changed.
0 amygdala voxels changed.
pass 1: 15130 changed. image ll: -9.672, PF=0.500
pass 2: 2561 changed.
6212 voxels changed in iteration 0 of unlikely voxel relabeling
7 voxels changed in iteration 1 of unlikely voxel relabeling
0 voxels changed in iteration 2 of unlikely voxel relabeling
3965 voxels changed in iteration 0 of unlikely voxel relabeling
42 voxels changed in iteration 1 of unlikely voxel relabeling
0 voxels changed in iteration 2 of unlikely voxel relabeling
2608 voxels changed in iteration 0 of unlikely voxel relabeling
12 voxels changed in iteration 1 of unlikely voxel relabeling
0 voxels changed in iteration 2 of unlikely voxel relabeling
5185 voxels changed in iteration 0 of unlikely voxel relabeling
13 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 4079.121263
mri_ca_label stimesec 1.373296
mri_ca_label ru_maxrss 2097272
mri_ca_label ru_ixrss 0
mri_ca_label ru_idrss 0
mri_ca_label ru_isrss 0
mri_ca_label ru_minflt 535303
mri_ca_label ru_majflt 1
mri_ca_label ru_nswap 0
mri_ca_label ru_inblock 1208
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 228
mri_ca_label ru_nivcsw 8517
auto-labeling took 68 minutes and 1 seconds.
mri_cc -aseg aseg.auto_noCCseg.mgz -o aseg.auto.mgz -lta /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/transforms/cc_up.lta FS6
will read input aseg from aseg.auto_noCCseg.mgz
writing aseg with cc labels to aseg.auto.mgz
will write lta as /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/transforms/cc_up.lta
reading aseg from /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/aseg.auto_noCCseg.mgz
reading norm from /autofs/cluster/animal/scan_data/sleep_eeg/SLEEP_TTRA_M_89/recons/FS6/mri/norm.mgz
3392 voxels in left wm, 15737 in right wm, xrange [132, 149]
searching rotation angles z=[-10 4], y=[-13 1]
searching scale 1 Z rot -10.3
searching scale 1 Z rot -10.1
searching scale 1 Z rot -9.8
searching scale 1 Z rot -9.6
searching scale 1 Z rot -9.3
searching scale 1 Z rot -9.1
searching scale 1 Z rot -8.8
searching scale 1 Z rot -8.6
searching scale 1 Z rot -8.3
searching scale 1 Z rot -8.1
searching scale 1 Z rot -7.8
searching scale 1 Z rot -7.6
searching scale 1 Z rot -7.3
searching scale 1 Z rot -7.1
searching scale 1 Z rot -6.8
searching scale 1 Z rot -6.6
searching scale 1 Z rot -6.3
searching scale 1 Z rot -6.1
searching scale 1 Z rot -5.8
searching scale 1 Z rot -5.6
searching scale 1 Z rot -5.3
searching scale 1 Z rot -5.1
searching scale 1 Z rot -4.8
searching scale 1 Z rot -4.6
searching scale 1 Z rot -4.3
searching scale 1 Z rot -4.1
searching scale 1 Z rot -3.8
searching scale 1 Z rot -3.6
searching scale 1 Z rot -3.3
searching scale 1 Z rot -3.1
searching scale 1 Z rot -2.8
searching scale 1 Z rot -2.6
searching scale 1 Z rot -2.3
searching scale 1 Z rot -2.1 global minimum found at slice 141.0, rotations (-7.05, -8.83)
final transformation (x=141.0, yr=-7.050, zr=-8.832):
0.98067 0.15354 -0.12129 -8.55582;
-0.15238 0.98814 0.01885 96.59499;
0.12274 0.00000 0.99244 31.29823;
0.00000 0.00000 0.00000 1.00000;
mri_cc: no WM voxels found with norm > 40 -- check skull stripping
Linux lemmiwinks.nmr.mgh.harvard.edu 3.10.0-1062.4.3.el7.x86_64 #1 SMP Wed Nov 13 23:58:53 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
recon-all -s FS6 exited with ERRORS at Mon Jan 13 13:51:23 EST 2020
To report a problem, see http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
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