Freesurefer Example#
Author: Steffen Bollmann
Setup Neurodesk#
import os
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
os.environ["LD_PRELOAD"] = "";
os.environ["APPTAINER_BINDPATH"] = "/content,/tmp,/cvmfs"
os.environ["MPLCONFIGDIR"] = "/content/matplotlib-mpldir"
os.environ["LMOD_CMD"] = "/usr/share/lmod/lmod/libexec/lmod"
!curl -J -O https://raw.githubusercontent.com/NeuroDesk/neurocommand/main/googlecolab_setup.sh
!chmod +x googlecolab_setup.sh
!./googlecolab_setup.sh
os.environ["MODULEPATH"] = ':'.join(map(str, list(map(lambda x: os.path.join(os.path.abspath('/cvmfs/neurodesk.ardc.edu.au/neurodesk-modules/'), x),os.listdir('/cvmfs/neurodesk.ardc.edu.au/neurodesk-modules/')))))
from google.colab import output
output.enable_custom_widget_manager()
!pip install ipyniivue
# Output CPU information:
!cat /proc/cpuinfo | grep 'vendor' | uniq
!cat /proc/cpuinfo | grep 'model name' | uniq
vendor_id : AuthenticAMD
model name : AMD EPYC 7742 64-Core Processor
# we can use lmod to load fsl in a specific version
import lmod
await lmod.load('freesurfer/7.3.2')
await lmod.list()
['freesurfer/7.3.2']
!recon-all
USAGE: recon-all
Required Arguments:
-subjid <subjid>
-<process directive>
Fully-Automated Directive:
-all : performs all stages of cortical reconstruction
-autorecon-all : same as -all
Manual-Intervention Workflow Directives:
-autorecon1 : process stages 1-5 (see below)
-autorecon2 : process stages 6-23
after autorecon2, check white surfaces:
a. if wm edit was required, then run -autorecon2-wm
b. if control points added, then run -autorecon2-cp
c. proceed to run -autorecon3
-autorecon2-cp : process stages 12-23 (uses -f w/ mri_normalize, -keep w/ mri_seg)
-autorecon2-wm : process stages 15-23
-autorecon2-inflate1 : 6-18
-autorecon2-perhemi : tess, sm1, inf1, q, fix, sm2, inf2, finalsurf, ribbon
-autorecon3 : process stages 24-34
if edits made to correct pial, then run -autorecon-pial
-hemi ?h : just do lh or rh (default is to do both)
Autorecon Processing Stages (see -autorecon# flags above):
1. Motion Correction and Conform
2. NU (Non-Uniform intensity normalization)
3. Talairach transform computation
4. Intensity Normalization 1
5. Skull Strip
6. EM Register (linear volumetric registration)
7. CA Intensity Normalization
8. CA Non-linear Volumetric Registration
9. Remove neck
10. EM Register, with skull
11. CA Label (Aseg: Volumetric Labeling) and Statistics
12. Intensity Normalization 2 (start here for control points)
13. White matter segmentation
14. Edit WM With ASeg
15. Fill (start here for wm edits)
16. Tessellation (begins per-hemisphere operations)
17. Smooth1
18. Inflate1
19. QSphere
20. Automatic Topology Fixer
21. White Surfs (start here for brain edits for pial surf)
22. Smooth2
23. Inflate2
24. Spherical Mapping
25. Spherical Registration
26. Spherical Registration, Contralater hemisphere
27. Map average curvature to subject
28. Cortical Parcellation (Labeling)
29. Cortical Parcellation Statistics
30. Pial Surfs
31. WM/GM Contrast
32. Cortical Ribbon Mask
33. Cortical Parcellation mapped to ASeg
34 Brodmann and exvio EC labels
Step-wise Directives
See -help
Expert Preferences
-pons-crs C R S : col, row, slice of seed point for pons, used in fill
-cc-crs C R S : col, row, slice of seed point for corpus callosum, used in fill
-lh-crs C R S : col, row, slice of seed point for left hemisphere, used in fill
-rh-crs C R S : col, row, slice of seed point for right hemisphere, used in fill
-nofill : do not use the automatic subcort seg to fill
-watershed cmd : control skull stripping/watershed program
-xmask file : custom external brain mask to replace automated skullstripping
-wsless : decrease watershed threshold (leaves less skull, but can strip more brain)
-wsmore : increase watershed threshold (leaves more skull, but can strip less brain)
-wsatlas : use atlas when skull stripping
-no-wsatlas : do not use atlas when skull stripping
-no-wsgcaatlas : do not use GCA atlas when skull stripping
-wsthresh pct : explicity set watershed threshold
-wsseed C R S : identify an index (C, R, S) point in the skull
-norm3diters niters : number of 3d iterations for mri_normalize
-normmaxgrad maxgrad : max grad (-g) for mri_normalize. Default is 1.
-norm1-b N : in the _first_ usage of mri_normalize, use control
point with intensity N below target (default=10.0)
-norm2-b N : in the _second_ usage of mri_normalize, use control
point with intensity N below target (default=10.0)
-norm1-n N : in the _first_ usage of mri_normalize, do N number
of iterations
-norm2-n N : in the _second_ usage of mri_normalize, do N number
of iterations
-cm : conform volumes to the min voxel size
-no-fix-with-ga : do not use genetic algorithm when fixing topology
-fix-diag-only : topology fixer runs until ?h.defect_labels files
are created, then stops
-seg-wlo wlo : set wlo value for mri_segment and mris_make_surfaces
-seg-ghi ghi : set ghi value for mri_segment and mris_make_surfaces
-nothicken : pass '-thicken 0' to mri_segment
-no-ca-align-after : turn off -align-after with mri_ca_register
-no-ca-align : turn off -align with mri_ca_label
-deface : deface subject, written to orig_defaced.mgz
-expert file : read-in expert options file
-xopts-use : use pre-existing expert options file
-xopts-clean : delete pre-existing expert options file
-xopts-overwrite : overwrite pre-existing expert options file
-termscript script : run script before exiting (multiple -termscript flags possible)
This can be good for running custom post-processing after recon-all
The script must be in your path. The subjid is passed as the only argument
The current directory is changed to SUBJECTS_DIR before the script is run
The script should exit with 0 unless there is an error
-mprage : assume scan parameters are MGH MP-RAGE protocol
-washu_mprage : assume scan parameters are Wash.U. MP-RAGE protocol.
both mprage flags affect mri_normalize and mri_segment,
and assumes a darker gm.
-schwartzya3t-atlas : for tal reg, use special young adult 3T atlas
-threads num : set number of threads to use
Notification Files (Optional)
-waitfor file : wait for file to appear before beginning
-notify file : create this file after finishing
Status and Log files (Optional)
-log file : default is scripts/recon-all.log
-status file : default is scripts/recon-all-status.log
-noappend : start new log and status files instead of appending
-no-isrunning : do not check whether this subject is currently being processed
Segmentation of substructures of hippocampus and brainstem
(These deprecated; please see segmentHA_T1.sh, segmentHA_T1.sh, segmentHA_T1_long.sh, segmentBS.sh)
-hippocampal-subfields-T1 : segmentation of hippocampal subfields using input T1 scan
-hippocampal-subfields-T2 file ID : segmentation using an additional scan (given by file);
ID is a user-defined identifier for the analysis
-hippocampal-subfields-T1T2 file ID : segmentation using additional scan (given by file) and input T1
simultaneously; ID is a user-defined identifier for the analysis
-brainstem-structures : segmentation of brainstem structures
Other Arguments (Optional)
-sd subjectsdir : specify subjects dir (default env SUBJECTS_DIR)
-mail username : mail user when done
-umask umask : set unix file permission mask (default 002)
-grp groupid : check that current group is alpha groupid
-onlyversions : print version of each binary and exit
-debug : print out lots of info
-allowcoredump : set coredump limit to unlimited
-dontrun : do everything but execute each command
-version : print version of this script and exit
-help : voluminous bits of wisdom
download data#
![ -f ./mp2rage.nii ] && echo "$FILE exist." || wget https://imaging.org.au/uploads/Human7T/mp2rageModel_L13_work03-plus-hippocampus-7T-sym-norm-mincanon_v0.8.nii -O ./mp2rage.nii
--2023-06-29 01:43:20-- https://imaging.org.au/uploads/Human7T/mp2rageModel_L13_work03-plus-hippocampus-7T-sym-norm-mincanon_v0.8.nii
Resolving imaging.org.au (imaging.org.au)... 203.101.229.7
Connecting to imaging.org.au (imaging.org.au)|203.101.229.7|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1536000352 (1.4G) [application/octet-stream]
Saving to: ‘./mp2rage.nii’
./mp2rage.nii 100%[===================>] 1.43G 20.4MB/s in 74s
2023-06-29 01:44:35 (19.8 MB/s) - ‘./mp2rage.nii’ saved [1536000352/1536000352]
!ls
freesurfer_example.ipynb qsmxt_example.ipynb
mp2rage.nii sct_toolbox_example.ipynb
nipype_module_example.ipynb
Run#
!mkdir ./freesurfer_output
!recon-all -subject subjectname -i mp2rage.nii -all -sd ./freesurfer_output
fs-check-version --s subjectname --o /tmp/tmp.T7LgKS
Thu Jun 29 02:40:59 UTC 2023
setenv SUBJECTS_DIR /home/jovyan/example-notebooks/structural_imaging/freesurfer_output
cd /home/jovyan/example-notebooks/structural_imaging
/opt/freesurfer-7.3.2/bin/fs-check-version --s subjectname --o /tmp/tmp.T7LgKS
-rwxrwxr-x. 1 nobody nobody 18565 Aug 4 2022 /opt/freesurfer-7.3.2/bin/fs-check-version
freesurfer-linux-centos8_x86_64-7.3.2-20220804-6354275
$Id$
Linux jupyter-stebo85 5.4.17-2136.309.5.el7uek.x86_64 #2 SMP Mon Jul 18 02:08:52 PDT 2022 x86_64 x86_64 x86_64 GNU/Linux
pid 1399
Current FS Version freesurfer-linux-centos8_x86_64-7.3.2-20220804-6354275
Subject does not have a bstampfile, copying /opt/freesurfer-7.3.2/build-stamp.txt
Subject FS Version: freesurfer-linux-centos8_x86_64-7.3.2-20220804-6354275
No constraints on version because REQ=UnSet and FsVerFile=NotThere
#@#% fs-check-version match = 1
fs-check-version Done
INFO: SUBJECTS_DIR is /home/jovyan/example-notebooks/structural_imaging/freesurfer_output
Actual FREESURFER_HOME /usr/local/freesurfer/7.3.2-1
Linux jupyter-stebo85 5.4.17-2136.309.5.el7uek.x86_64 #2 SMP Mon Jul 18 02:08:52 PDT 2022 x86_64 x86_64 x86_64 GNU/Linux
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname
mri_convert /home/jovyan/example-notebooks/structural_imaging/mp2rage.nii /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig/001.mgz
mri_convert /home/jovyan/example-notebooks/structural_imaging/mp2rage.nii /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig/001.mgz
reading from /home/jovyan/example-notebooks/structural_imaging/mp2rage.nii...
TR=0.00, TE=0.00, TI=0.00, flip angle=0.00
i_ras = (1, 0, 0)
j_ras = (0, 1, 0)
k_ras = (0, 0, 1)
writing to /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig/001.mgz...
#--------------------------------------------
#@# MotionCor Thu Jun 29 02:43:10 UTC 2023
Found 1 runs
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig/001.mgz
Checking for (invalid) multi-frame inputs...
Only one run found so motion
correction will not be performed. I'll
copy the run to rawavg and continue.
cp /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig/001.mgz /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/rawavg.mgz
mri_info /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/rawavg.mgz
rawavg.mgz ========================================
Volume information for /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/rawavg.mgz
type: MGH
dimensions: 640 x 750 x 800
voxel sizes: 0.300000, 0.300000, 0.300000
type: FLOAT (3)
fov: 240.000
dof: 1
xstart: -96.0, xend: 96.0
ystart: -112.5, yend: 112.5
zstart: -120.0, zend: 120.0
TR: 0.00 msec, TE: 0.00 msec, TI: 0.00 msec, flip angle: 0.00 degrees
nframes: 1
PhEncDir: UNKNOWN
FieldStrength: 0.000000
ras xform present
xform info: x_r = 1.0000, y_r = 0.0000, z_r = 0.0000, c_r = 0.3750
: x_a = 0.0000, y_a = 1.0000, z_a = 0.0000, c_a = 0.3750
: x_s = 0.0000, y_s = 0.0000, z_s = 1.0000, c_s = 0.3750
talairach xfm :
Orientation : RAS
Primary Slice Direction: axial
voxel to ras transform:
0.3000 0.0000 0.0000 -95.6250
0.0000 0.3000 0.0000 -112.1250
0.0000 0.0000 0.3000 -119.6250
0.0000 0.0000 0.0000 1.0000
voxel-to-ras determinant 0.027
ras to voxel transform:
3.3333 0.0000 0.0000 318.7500
0.0000 3.3333 0.0000 373.7500
0.0000 0.0000 3.3333 398.7500
0.0000 0.0000 0.0000 1.0000
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname
mri_convert /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/rawavg.mgz /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig.mgz --conform
mri_convert /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/rawavg.mgz /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig.mgz --conform
reading from /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/rawavg.mgz...
TR=0.00, TE=0.00, TI=0.00, flip angle=0.00
i_ras = (1, 0, 0)
j_ras = (0, 1, 0)
k_ras = (0, 0, 1)
changing data type from float to uchar (noscale = 0)...
MRIchangeType: Building histogram 0 255 1000, flo=0, fhi=0.999, dest_type=0
Reslicing using trilinear interpolation
writing to /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig.mgz...
mri_add_xform_to_header -c /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/transforms/talairach.xfm /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig.mgz /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig.mgz
INFO: extension is mgz
mri_info /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig.mgz
orig.mgz ========================================
Volume information for /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/orig.mgz
type: MGH
dimensions: 256 x 256 x 256
voxel sizes: 1.000000, 1.000000, 1.000000
type: UCHAR (0)
fov: 256.000
dof: 1
xstart: -128.0, xend: 128.0
ystart: -128.0, yend: 128.0
zstart: -128.0, zend: 128.0
TR: 0.00 msec, TE: 0.00 msec, TI: 0.00 msec, flip angle: 0.00 degrees
nframes: 1
PhEncDir: UNKNOWN
FieldStrength: 0.000000
ras xform present
xform info: x_r = -1.0000, y_r = 0.0000, z_r = 0.0000, c_r = 0.3750
: x_a = 0.0000, y_a = 0.0000, z_a = 1.0000, c_a = 0.3750
: x_s = 0.0000, y_s = -1.0000, z_s = 0.0000, c_s = 0.3750
talairach xfm : /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/transforms/talairach.xfm
Orientation : LIA
Primary Slice Direction: coronal
voxel to ras transform:
-1.0000 0.0000 0.0000 128.3750
0.0000 0.0000 1.0000 -127.6250
0.0000 -1.0000 0.0000 128.3750
0.0000 0.0000 0.0000 1.0000
voxel-to-ras determinant -1
ras to voxel transform:
-1.0000 -0.0000 -0.0000 128.3750
-0.0000 -0.0000 -1.0000 128.3750
-0.0000 1.0000 -0.0000 127.6250
-0.0000 -0.0000 -0.0000 1.0000
#--------------------------------------------
#@# Talairach Thu Jun 29 02:47:13 UTC 2023
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
mri_nu_correct.mni --no-rescale --i orig.mgz --o orig_nu.mgz --ants-n4 --n 1 --proto-iters 1000 --distance 50
/usr/bin/bc
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
/opt/freesurfer-7.3.2/bin/mri_nu_correct.mni
--no-rescale --i orig.mgz --o orig_nu.mgz --ants-n4 --n 1 --proto-iters 1000 --distance 50
nIters 1
mri_nu_correct.mni 7.3.2
Linux jupyter-stebo85 5.4.17-2136.309.5.el7uek.x86_64 #2 SMP Mon Jul 18 02:08:52 PDT 2022 x86_64 x86_64 x86_64 GNU/Linux
Thu Jun 29 02:47:13 UTC 2023
tmpdir is ./tmp.mri_nu_correct.mni.1723
cd /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
AntsN4BiasFieldCorrectionFs -i orig.mgz -o ./tmp.mri_nu_correct.mni.1723/nu0.mgz --dtype uchar
AntsN4BiasFieldCorrectionFs done
mri_convert ./tmp.mri_nu_correct.mni.1723/nu0.mgz orig_nu.mgz --like orig.mgz --conform
mri_convert ./tmp.mri_nu_correct.mni.1723/nu0.mgz orig_nu.mgz --like orig.mgz --conform
reading from ./tmp.mri_nu_correct.mni.1723/nu0.mgz...
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 orig_nu.mgz...
Thu Jun 29 02:50:27 UTC 2023
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...
mv -f /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/talsrcimg_to_711-2C_as_mni_average_305_t4_vox2vox.txt /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/transforms/talsrcimg_to_711-2C_as_mni_average_305_t4_vox2vox.txt
Started at Thu Jun 29 02:50:27 UTC 2023
Ended at Thu Jun 29 02:51:13 UTC 2023
talairach_avi done
cp transforms/talairach.auto.xfm transforms/talairach.xfm
lta_convert --src orig.mgz --trg /opt/freesurfer-7.3.2/average/mni305.cor.mgz --inxfm transforms/talairach.xfm --outlta transforms/talairach.xfm.lta --subject fsaverage --ltavox2vox
7.3.2
--src: orig.mgz src image (geometry).
--trg: /opt/freesurfer-7.3.2/average/mni305.cor.mgz trg image (geometry).
--inmni: transforms/talairach.xfm input MNI/XFM transform.
--outlta: transforms/talairach.xfm.lta output LTA.
--s: fsaverage subject name
--ltavox2vox: output LTA as VOX_TO_VOX transform.
LTA read, type : 1
1.07958 0.00270 -0.01574 0.58327;
0.01399 0.99838 0.31440 -9.30106;
0.00758 -0.40444 1.15055 -29.25244;
0.00000 0.00000 0.00000 1.00000;
setting subject to fsaverage
Writing LTA to file transforms/talairach.xfm.lta...
lta_convert successful.
~/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/transforms ~/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
~/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
#--------------------------------------------
#@# Talairach Failure Detection Thu Jun 29 02:51:15 UTC 2023
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
talairach_afd -T 0.005 -xfm transforms/talairach.xfm
talairach_afd: Talairach Transform: transforms/talairach.xfm OK (p=0.7053, pval=0.4932 >= threshold=0.0050)
awk -f /opt/freesurfer-7.3.2/bin/extract_talairach_avi_QA.awk /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/transforms/talairach_avi.log
tal_QC_AZS /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/transforms/talairach_avi.log
TalAviQA: 0.97201
z-score: -1
#--------------------------------------------
#@# Nu Intensity Correction Thu Jun 29 02:51:15 UTC 2023
mri_nu_correct.mni --i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --n 2 --ants-n4
/usr/bin/bc
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
/opt/freesurfer-7.3.2/bin/mri_nu_correct.mni
--i orig.mgz --o nu.mgz --uchar transforms/talairach.xfm --n 2 --ants-n4
nIters 2
mri_nu_correct.mni 7.3.2
Linux jupyter-stebo85 5.4.17-2136.309.5.el7uek.x86_64 #2 SMP Mon Jul 18 02:08:52 PDT 2022 x86_64 x86_64 x86_64 GNU/Linux
Thu Jun 29 02:51:15 UTC 2023
tmpdir is ./tmp.mri_nu_correct.mni.1951
cd /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
AntsN4BiasFieldCorrectionFs -i orig.mgz -o ./tmp.mri_nu_correct.mni.1951/nu0.mgz --dtype uchar
AntsN4BiasFieldCorrectionFs done
mri_binarize --i ./tmp.mri_nu_correct.mni.1951/nu0.mgz --min -1 --o ./tmp.mri_nu_correct.mni.1951/ones.mgz
7.3.2
cwd /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
cmdline mri_binarize --i ./tmp.mri_nu_correct.mni.1951/nu0.mgz --min -1 --o ./tmp.mri_nu_correct.mni.1951/ones.mgz
sysname Linux
hostname jupyter-stebo85
machine x86_64
user jovyan
input ./tmp.mri_nu_correct.mni.1951/nu0.mgz
frame 0
nErode3d 0
nErode2d 0
output ./tmp.mri_nu_correct.mni.1951/ones.mgz
Binarizing based on threshold
min -1
max +infinity
binval 1
binvalnot 0
fstart = 0, fend = 0, nframes = 1
Starting parallel 1
Found 16777216 values in range
Counting number of voxels in first frame
Found 16777215 voxels in final mask
Writing output to ./tmp.mri_nu_correct.mni.1951/ones.mgz
Count: 16777215 16777215.000000 16777216 99.999994
mri_binarize done
mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.1951/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.1951/sum.junk --avgwf ./tmp.mri_nu_correct.mni.1951/input.mean.dat
7.3.2
cwd
cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.1951/ones.mgz --i orig.mgz --sum ./tmp.mri_nu_correct.mni.1951/sum.junk --avgwf ./tmp.mri_nu_correct.mni.1951/input.mean.dat
sysname Linux
hostname jupyter-stebo85
machine x86_64
user jovyan
whitesurfname white
UseRobust 0
Loading ./tmp.mri_nu_correct.mni.1951/ones.mgz
Loading orig.mgz
Voxel Volume is 1 mm^3
Generating list of segmentation ids
Found 1 segmentations
Computing statistics for each segmentation
Reporting on 1 segmentations
Using PrintSegStat
Computing spatial average of each frame
Writing to ./tmp.mri_nu_correct.mni.1951/input.mean.dat
mri_segstats done
mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.1951/ones.mgz --i ./tmp.mri_nu_correct.mni.1951/nu0.mgz --sum ./tmp.mri_nu_correct.mni.1951/sum.junk --avgwf ./tmp.mri_nu_correct.mni.1951/output.mean.dat
7.3.2
cwd
cmdline mri_segstats --id 1 --seg ./tmp.mri_nu_correct.mni.1951/ones.mgz --i ./tmp.mri_nu_correct.mni.1951/nu0.mgz --sum ./tmp.mri_nu_correct.mni.1951/sum.junk --avgwf ./tmp.mri_nu_correct.mni.1951/output.mean.dat
sysname Linux
hostname jupyter-stebo85
machine x86_64
user jovyan
whitesurfname white
UseRobust 0
Loading ./tmp.mri_nu_correct.mni.1951/ones.mgz
Loading ./tmp.mri_nu_correct.mni.1951/nu0.mgz
Voxel Volume is 1 mm^3
Generating list of segmentation ids
Found 1 segmentations
Computing statistics for each segmentation
Reporting on 1 segmentations
Using PrintSegStat
Computing spatial average of each frame
Writing to ./tmp.mri_nu_correct.mni.1951/output.mean.dat
mri_segstats done
mris_calc -o ./tmp.mri_nu_correct.mni.1951/nu0.mgz ./tmp.mri_nu_correct.mni.1951/nu0.mgz mul 1.16048835830789136545
Saving result to './tmp.mri_nu_correct.mni.1951/nu0.mgz' (type = MGH ) [ ok ]
mri_convert ./tmp.mri_nu_correct.mni.1951/nu0.mgz nu.mgz --like orig.mgz
mri_convert ./tmp.mri_nu_correct.mni.1951/nu0.mgz nu.mgz --like orig.mgz
reading from ./tmp.mri_nu_correct.mni.1951/nu0.mgz...
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 7 seconds.
FIRST_PERCENTILE 0.010000
WM_PERCENTILE 0.900000
MAX_R 50.000000
i1 = 6, i2 = 82
#mri_make_uchar# mapping 15 211 to 3 110 : b -5.93289 m 0.548893 : thresh 10.8088 maxsat 475.38 : nzero 12342897 nsat 0
Thu Jun 29 02:55:31 UTC 2023
mri_nu_correct.mni done
mri_add_xform_to_header -c /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri/transforms/talairach.xfm nu.mgz nu.mgz
INFO: extension is mgz
#--------------------------------------------
#@# Intensity Normalization Thu Jun 29 02:55:32 UTC 2023
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
mri_normalize -g 1 -seed 1234 -mprage nu.mgz T1.mgz
using max gradient = 1.000
setting seed for random number genererator to 1234
assuming input volume is MGH (Van der Kouwe) MP-RAGE
reading mri_src from nu.mgz...
normalizing image...
NOT doing gentle normalization with control points/label
talairach transform
1.07958 0.00270 -0.01574 0.58327;
0.01399 0.99838 0.31440 -9.30106;
0.00758 -0.40444 1.15055 -29.25244;
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 110
gm peak at 62 (62), valley at 51 (51)
csf peak at 32, setting threshold to 52
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 65 (65), valley at 53 (53)
csf peak at 33, setting threshold to 54
building Voronoi diagram...
performing soap bubble smoothing, sigma = 8...
Done iterating ---------------------------------
writing output to T1.mgz
3D bias adjustment took 2 minutes and 10 seconds.
#--------------------------------------------
#@# Skull Stripping Thu Jun 29 02:57:44 UTC 2023
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
mri_em_register -skull nu.mgz /opt/freesurfer-7.3.2/average/RB_all_withskull_2020_01_02.gca transforms/talairach_with_skull.lta
aligning to atlas containing skull, setting unknown_nbr_spacing = 5
== Number of threads available to mri_em_register for OpenMP = 1 ==
reading 1 input volumes...
logging results to talairach_with_skull.log
reading '/opt/freesurfer-7.3.2/average/RB_all_withskull_2020_01_02.gca'...
GCAread took 0 minutes and 1 seconds.
average std = 23.0 using min determinant for regularization = 52.8
0 singular and 9205 ill-conditioned covariance matrices regularized
reading 'nu.mgz'...
freeing gibbs priors...done.
accounting for voxel sizes in initial transform
bounding unknown intensity as < 8.9 or > 556.0
total sample mean = 77.3 (1403 zeros)
************************************************
spacing=8, using 3292 sample points, tol=1.00e-05...
************************************************
register_mri: find_optimal_transform
find_optimal_transform: nsamples 3292, passno 0, spacing 8
resetting wm mean[0]: 100 --> 108
resetting gm mean[0]: 61 --> 61
input volume #1 is the most T1-like
using real data threshold=25.0
skull bounding box = (51, 29, 17) --> (205, 241, 220)
finding center of left hemi white matter
using (102, 100, 119) as brain centroid of Right_Cerebral_White_Matter...
MRImask(): AllowDiffGeom = 1
mean wm in atlas = 108, using box (83,74,94) --> (120, 126,144) to find MRI wm
before smoothing, mri peak at 107
robust fit to distribution - 107 +- 3.7
after smoothing, mri peak at 107, scaling input intensities by 1.009
scaling channel 0 by 1.00935
initial log_p = -4.355
************************************************
First Search limited to translation only.
************************************************
max log p = -4.327209 @ (-10.526, 10.526, -10.526)
max log p = -4.238077 @ (5.263, 5.263, 5.263)
max log p = -4.103386 @ (2.632, -2.632, -2.632)
max log p = -4.103386 @ (0.000, 0.000, 0.000)
max log p = -4.087552 @ (-0.658, -0.658, -1.974)
max log p = -4.087552 @ (0.000, 0.000, 0.000)
max log p = -4.087552 @ (0.000, 0.000, 0.000)
max log p = -4.087552 @ (0.000, 0.000, 0.000)
Found translation: (-3.3, 12.5, -9.9): log p = -4.088
****************************************
Nine parameter search. iteration 0 nscales = 0 ...
****************************************
Result so far: scale 1.000: max_log_p=-3.772, old_max_log_p =-4.088 (thresh=-4.1)
1.06375 0.00000 0.00000 -11.47446;
0.00000 1.11081 0.29764 -30.89916;
0.00000 -0.27532 1.02750 26.07788;
0.00000 0.00000 0.00000 1.00000;
iteration took 1 minutes and 35 seconds.
****************************************
Nine parameter search. iteration 1 nscales = 0 ...
****************************************
Result so far: scale 1.000: max_log_p=-3.772, old_max_log_p =-3.772 (thresh=-3.8)
1.06375 0.00000 0.00000 -11.47446;
0.00000 1.11081 0.29764 -30.89916;
0.00000 -0.27532 1.02750 26.07788;
0.00000 0.00000 0.00000 1.00000;
reducing scale to 0.2500
iteration took 1 minutes and 28 seconds.
****************************************
Nine parameter search. iteration 2 nscales = 1 ...
****************************************
Result so far: scale 0.250: max_log_p=-3.654, old_max_log_p =-3.772 (thresh=-3.8)
1.04325 -0.00851 0.03175 -11.59169;
0.00000 1.08921 0.29185 -25.65857;
-0.03480 -0.26485 0.98844 30.03328;
0.00000 0.00000 0.00000 1.00000;
iteration took 1 minutes and 22 seconds.
****************************************
Nine parameter search. iteration 3 nscales = 1 ...
****************************************
Result so far: scale 0.250: max_log_p=-3.648, old_max_log_p =-3.654 (thresh=-3.7)
1.04325 -0.00851 0.03175 -11.59169;
-0.00116 1.10021 0.33011 -31.41711;
-0.03479 -0.30035 0.97836 35.56596;
0.00000 0.00000 0.00000 1.00000;
iteration took 1 minutes and 22 seconds.
****************************************
Nine parameter search. iteration 4 nscales = 1 ...
****************************************
Result so far: scale 0.250: max_log_p=-3.647, old_max_log_p =-3.648 (thresh=-3.6)
1.04383 0.00132 -0.00028 -9.04488;
-0.00118 1.12083 0.33630 -32.79573;
-0.00063 -0.30047 0.97888 31.13393;
0.00000 0.00000 0.00000 1.00000;
reducing scale to 0.0625
iteration took 1 minutes and 22 seconds.
****************************************
Nine parameter search. iteration 5 nscales = 2 ...
****************************************
Result so far: scale 0.062: max_log_p=-3.626, old_max_log_p =-3.647 (thresh=-3.6)
1.04240 0.01742 0.01298 -11.43840;
-0.01818 1.12155 0.31881 -29.55735;
-0.00921 -0.28194 0.98426 28.90162;
0.00000 0.00000 0.00000 1.00000;
iteration took 1 minutes and 18 seconds.
****************************************
Nine parameter search. iteration 6 nscales = 2 ...
****************************************
Result so far: scale 0.062: max_log_p=-3.625, old_max_log_p =-3.626 (thresh=-3.6)
1.04107 0.01509 0.02101 -12.41482;
-0.01816 1.12024 0.31844 -29.82493;
-0.01776 -0.28240 0.98528 29.92633;
0.00000 0.00000 0.00000 1.00000;
min search scale 0.025000 reached
***********************************************
Computing MAP estimate using 3292 samples...
***********************************************
dt = 5.00e-06, momentum=0.80, tol=1.00e-05
l_intensity = 1.0000
Aligning input volume to GCA...
Transform matrix
1.04107 0.01509 0.02101 -12.41482;
-0.01816 1.12024 0.31844 -29.82493;
-0.01776 -0.28240 0.98528 29.92633;
0.00000 0.00000 0.00000 1.00000;
nsamples 3292
Quasinewton: input matrix
1.04107 0.01509 0.02101 -12.41482;
-0.01816 1.12024 0.31844 -29.82493;
-0.01776 -0.28240 0.98528 29.92633;
0.00000 0.00000 0.00000 1.00000;
IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 3 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 009: -log(p) = -0.0 tol 0.000010
Resulting transform:
1.04107 0.01509 0.02101 -12.41482;
-0.01816 1.12024 0.31844 -29.82493;
-0.01776 -0.28240 0.98528 29.92633;
0.00000 0.00000 0.00000 1.00000;
pass 1, spacing 8: log(p) = -3.625 (old=-4.355)
transform before final EM align:
1.04107 0.01509 0.02101 -12.41482;
-0.01816 1.12024 0.31844 -29.82493;
-0.01776 -0.28240 0.98528 29.92633;
0.00000 0.00000 0.00000 1.00000;
**************************************************
EM alignment process ...
Computing final MAP estimate using 364986 samples.
**************************************************
dt = 5.00e-06, momentum=0.80, tol=1.00e-07
l_intensity = 1.0000
Aligning input volume to GCA...
Transform matrix
1.04107 0.01509 0.02101 -12.41482;
-0.01816 1.12024 0.31844 -29.82493;
-0.01776 -0.28240 0.98528 29.92633;
0.00000 0.00000 0.00000 1.00000;
nsamples 364986
Quasinewton: input matrix
1.04107 0.01509 0.02101 -12.41482;
-0.01816 1.12024 0.31844 -29.82493;
-0.01776 -0.28240 0.98528 29.92633;
0.00000 0.00000 0.00000 1.00000;
IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 6 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 011: -log(p) = 4.1 tol 0.000000
final transform:
1.04107 0.01509 0.02101 -12.41482;
-0.01816 1.12024 0.31844 -29.82493;
-0.01776 -0.28240 0.98528 29.92633;
0.00000 0.00000 0.00000 1.00000;
writing output transformation to transforms/talairach_with_skull.lta...
#VMPC# mri_em_register VmPeak 800756
FSRUNTIME@ mri_em_register 0.1833 hours 1 threads
registration took 11 minutes and 0 seconds.
mri_watershed -T1 -brain_atlas /opt/freesurfer-7.3.2/average/RB_all_withskull_2020_01_02.gca transforms/talairach_with_skull.lta T1.mgz brainmask.auto.mgz
Mode: T1 normalized volume
Mode: Use the information of atlas (default parms, --help for details)
*********************************************************
The input file is T1.mgz
The output file is brainmask.auto.mgz
Weighting the input with atlas information before watershed
*************************WATERSHED**************************
Sorting...
first estimation of the COG coord: x=129 y=99 z=114 r=79
first estimation of the main basin volume: 2144899 voxels
Looking for seedpoints
2 found in the cerebellum
16 found in the rest of the brain
global maximum in x=106, y=92, z=76, Imax=255
CSF=12, 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=2229910857 voxels, voxel volume =1.000
= 2229910857 mmm3 = 2229910.784 cm3
done.
PostAnalyze...Basin Prior
8 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=106, z=109, r=9955 iterations
^^^^^^^^ couldn't find WM with original limits - expanding ^^^^^^
GLOBAL CSF_MIN=0, CSF_intensity=7, CSF_MAX=21 , nb = 44406
RIGHT_CER CSF_MIN=0, CSF_intensity=4, CSF_MAX=11 , nb = 3024
LEFT_CER CSF_MIN=0, CSF_intensity=4, CSF_MAX=11 , nb = 3060
RIGHT_BRAIN CSF_MIN=0, CSF_intensity=9, CSF_MAX=20 , nb = 18684
LEFT_BRAIN CSF_MIN=0, CSF_intensity=9, CSF_MAX=21 , nb = 18684
OTHER CSF_MIN=0, CSF_intensity=4, CSF_MAX=14 , nb = 954
Problem with the least square interpolation in GM_MIN calculation.
CSF_MAX TRANSITION GM_MIN GM
GLOBAL
before analyzing : 21, 23, 31, 64
after analyzing : 21, 28, 31, 37
RIGHT_CER
before analyzing : 11, 16, 36, 64
after analyzing : 11, 29, 36, 37
LEFT_CER
before analyzing : 11, 16, 37, 61
after analyzing : 11, 30, 37, 37
RIGHT_BRAIN
before analyzing : 20, 22, 29, 64
after analyzing : 20, 26, 29, 35
LEFT_BRAIN
before analyzing : 21, 24, 32, 64
after analyzing : 21, 29, 32, 37
OTHER
before analyzing : 14, 15, 27, 94
after analyzing : 14, 23, 27, 40
mri_strip_skull: done peeling brain
highly tesselated surface with 10242 vertices
matching...65 iterations
*********************VALIDATION*********************
curvature mean = -0.013, std = 0.011
curvature mean = 69.801, std = 8.241
No Rigid alignment: -atlas Mode Off (basic atlas / no registration)
before rotation: sse = 2.23, sigma = 3.29
after rotation: sse = 2.23, sigma = 3.29
Localization of inacurate regions: Erosion-Dilation steps
the sse mean is 2.27, its var is 2.73
before Erosion-Dilatation 0.09% 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...34 iterations
mri_strip_skull: done peeling brain
Brain Size = 1611792 voxels, voxel volume = 1.000 mm3
= 1611792 mmm3 = 1611.792 cm3
******************************
Saving brainmask.auto.mgz
done
mri_watershed done
cp brainmask.auto.mgz brainmask.mgz
#-------------------------------------
#@# EM Registration Thu Jun 29 03:09:01 UTC 2023
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
mri_em_register -uns 3 -mask brainmask.mgz nu.mgz /opt/freesurfer-7.3.2/average/RB_all_2020-01-02.gca transforms/talairach.lta
setting unknown_nbr_spacing = 3
using MR volume brainmask.mgz to mask input volume...
== Number of threads available to mri_em_register for OpenMP = 1 ==
reading 1 input volumes...
logging results to talairach.log
reading '/opt/freesurfer-7.3.2/average/RB_all_2020-01-02.gca'...
GCAread took 0 minutes and 1 seconds.
average std = 7.2 using min determinant for regularization = 5.2
0 singular and 884 ill-conditioned covariance matrices regularized
reading 'nu.mgz'...
MRImask(): AllowDiffGeom = 1
MRImask(): AllowDiffGeom = 1
MRImask(): AllowDiffGeom = 1
MRImask(): AllowDiffGeom = 1
MRImask(): AllowDiffGeom = 1
freeing gibbs priors...done.
accounting for voxel sizes in initial transform
bounding unknown intensity as < 5.9 or > 519.0
total sample mean = 79.1 (1017 zeros)
************************************************
spacing=8, using 2841 sample points, tol=1.00e-05...
************************************************
register_mri: find_optimal_transform
find_optimal_transform: nsamples 2841, passno 0, spacing 8
resetting wm mean[0]: 98 --> 107
resetting gm mean[0]: 61 --> 61
input volume #1 is the most T1-like
using real data threshold=20.9
skull bounding box = (62, 44, 31) --> (194, 181, 196)
finding center of left hemi white matter
using (106, 90, 114) as brain centroid of Right_Cerebral_White_Matter...
MRImask(): AllowDiffGeom = 1
mean wm in atlas = 107, using box (90,73,94) --> (122, 106,134) to find MRI wm
before smoothing, mri peak at 107
robust fit to distribution - 107 +- 3.2
after smoothing, mri peak at 107, scaling input intensities by 1.000
scaling channel 0 by 1
initial log_p = -4.170
************************************************
First Search limited to translation only.
************************************************
max log p = -4.041770 @ (-10.526, 10.526, -10.526)
max log p = -3.763349 @ (5.263, 5.263, 5.263)
max log p = -3.695452 @ (2.632, -2.632, -2.632)
max log p = -3.663278 @ (1.316, 3.947, 1.316)
max log p = -3.653469 @ (-0.658, -0.658, -0.658)
max log p = -3.653469 @ (0.000, 0.000, 0.000)
max log p = -3.653469 @ (0.000, 0.000, 0.000)
max log p = -3.653469 @ (0.000, 0.000, 0.000)
Found translation: (-2.0, 16.4, -7.2): log p = -3.653
****************************************
Nine parameter search. iteration 0 nscales = 0 ...
****************************************
Result so far: scale 1.000: max_log_p=-3.190, old_max_log_p =-3.653 (thresh=-3.6)
1.07500 0.00000 0.00000 -11.63188;
0.00000 1.11081 0.29764 -28.48080;
0.00000 -0.25882 0.96593 30.71957;
0.00000 0.00000 0.00000 1.00000;
iteration took 1 minutes and 20 seconds.
****************************************
Nine parameter search. iteration 1 nscales = 0 ...
****************************************
Result so far: scale 1.000: max_log_p=-3.190, old_max_log_p =-3.190 (thresh=-3.2)
1.07500 0.00000 0.00000 -11.63188;
0.00000 1.11081 0.29764 -28.48080;
0.00000 -0.25882 0.96593 30.71957;
0.00000 0.00000 0.00000 1.00000;
reducing scale to 0.2500
iteration took 1 minutes and 19 seconds.
****************************************
Nine parameter search. iteration 2 nscales = 1 ...
****************************************
Result so far: scale 0.250: max_log_p=-3.104, old_max_log_p =-3.190 (thresh=-3.2)
1.05409 0.00000 0.00000 -8.93891;
0.00000 1.15247 0.30880 -34.02222;
0.00000 -0.26367 0.98404 27.44393;
0.00000 0.00000 0.00000 1.00000;
iteration took 1 minutes and 19 seconds.
****************************************
Nine parameter search. iteration 3 nscales = 1 ...
****************************************
Result so far: scale 0.250: max_log_p=-3.104, old_max_log_p =-3.104 (thresh=-3.1)
1.05409 0.00000 0.00000 -8.93891;
0.00000 1.15247 0.30880 -34.02222;
0.00000 -0.26367 0.98404 27.44393;
0.00000 0.00000 0.00000 1.00000;
reducing scale to 0.0625
iteration took 1 minutes and 16 seconds.
****************************************
Nine parameter search. iteration 4 nscales = 2 ...
****************************************
Result so far: scale 0.062: max_log_p=-3.052, old_max_log_p =-3.104 (thresh=-3.1)
1.05652 -0.00215 0.00803 -9.89662;
0.00000 1.14977 0.30808 -33.66290;
-0.00863 -0.26274 0.98055 28.36465;
0.00000 0.00000 0.00000 1.00000;
iteration took 1 minutes and 21 seconds.
****************************************
Nine parameter search. iteration 5 nscales = 2 ...
****************************************
Result so far: scale 0.062: max_log_p=-3.052, old_max_log_p =-3.052 (thresh=-3.0)
1.05652 -0.00215 0.00803 -9.89662;
0.00000 1.14977 0.30808 -33.66290;
-0.00863 -0.26274 0.98055 28.36465;
0.00000 0.00000 0.00000 1.00000;
min search scale 0.025000 reached
***********************************************
Computing MAP estimate using 2841 samples...
***********************************************
dt = 5.00e-06, momentum=0.80, tol=1.00e-05
l_intensity = 1.0000
Aligning input volume to GCA...
Transform matrix
1.05652 -0.00215 0.00803 -9.89662;
0.00000 1.14977 0.30808 -33.66290;
-0.00863 -0.26274 0.98055 28.36465;
0.00000 0.00000 0.00000 1.00000;
nsamples 2841
Quasinewton: input matrix
1.05652 -0.00215 0.00803 -9.89662;
0.00000 1.14977 0.30808 -33.66290;
-0.00863 -0.26274 0.98055 28.36465;
0.00000 0.00000 0.00000 1.00000;
IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 3 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 008: -log(p) = -0.0 tol 0.000010
Resulting transform:
1.05652 -0.00215 0.00803 -9.89662;
0.00000 1.14977 0.30808 -33.66290;
-0.00863 -0.26274 0.98055 28.36465;
0.00000 0.00000 0.00000 1.00000;
pass 1, spacing 8: log(p) = -3.052 (old=-4.170)
transform before final EM align:
1.05652 -0.00215 0.00803 -9.89662;
0.00000 1.14977 0.30808 -33.66290;
-0.00863 -0.26274 0.98055 28.36465;
0.00000 0.00000 0.00000 1.00000;
**************************************************
EM alignment process ...
Computing final MAP estimate using 315638 samples.
**************************************************
dt = 5.00e-06, momentum=0.80, tol=1.00e-07
l_intensity = 1.0000
Aligning input volume to GCA...
Transform matrix
1.05652 -0.00215 0.00803 -9.89662;
0.00000 1.14977 0.30808 -33.66290;
-0.00863 -0.26274 0.98055 28.36465;
0.00000 0.00000 0.00000 1.00000;
nsamples 315638
Quasinewton: input matrix
1.05652 -0.00215 0.00803 -9.89662;
0.00000 1.14977 0.30808 -33.66290;
-0.00863 -0.26274 0.98055 28.36465;
0.00000 0.00000 0.00000 1.00000;
IFLAG= -1 LINE SEARCH FAILED. SEE DOCUMENTATION OF ROUTINE MCSRCH ERROR RETURN OF LINE SEARCH: INFO= 6 POSSIBLE CAUSES: FUNCTION OR GRADIENT ARE INCORRECT OR INCORRECT TOLERANCESoutof QuasiNewtonEMA: 010: -log(p) = 3.7 tol 0.000000
final transform:
1.05652 -0.00215 0.00803 -9.89662;
0.00000 1.14977 0.30808 -33.66290;
-0.00863 -0.26274 0.98055 28.36465;
0.00000 0.00000 0.00000 1.00000;
writing output transformation to transforms/talairach.lta...
#VMPC# mri_em_register VmPeak 788204
FSRUNTIME@ mri_em_register 0.1472 hours 1 threads
registration took 8 minutes and 50 seconds.
#--------------------------------------
#@# CA Normalize Thu Jun 29 03:17:51 UTC 2023
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
mri_ca_normalize -c ctrl_pts.mgz -mask brainmask.mgz nu.mgz /opt/freesurfer-7.3.2/average/RB_all_2020-01-02.gca transforms/talairach.lta norm.mgz
writing control point volume to ctrl_pts.mgz
using MR volume brainmask.mgz to mask input volume...
reading 1 input volume
reading atlas from '/opt/freesurfer-7.3.2/average/RB_all_2020-01-02.gca'...
reading transform from 'transforms/talairach.lta'...
reading input volume from nu.mgz...
resetting wm mean[0]: 98 --> 107
resetting gm mean[0]: 61 --> 61
input volume #1 is the most T1-like
using real data threshold=20.9
skull bounding box = (62, 44, 31) --> (194, 181, 196)
finding center of left hemi white matter
using (106, 90, 114) as brain centroid of Right_Cerebral_White_Matter...
mean wm in atlas = 107, using box (90,73,94) --> (122, 106,134) to find MRI wm
before smoothing, mri peak at 107
robust fit to distribution - 107 +- 3.2
after smoothing, mri peak at 107, scaling input intensities by 1.000
scaling channel 0 by 1
using 246437 sample points...
INFO: compute sample coordinates transform
1.05652 -0.00215 0.00803 -9.89662;
0.00000 1.14977 0.30808 -33.66290;
-0.00863 -0.26274 0.98055 28.36465;
0.00000 0.00000 0.00000 1.00000;
INFO: transform used
finding control points in Left_Cerebral_White_Matter....
found 40230 control points for structure...
bounding box (129, 48, 34) --> (191, 150, 194)
Left_Cerebral_White_Matter: limiting intensities to 96.0 --> 132.0
20 of 9292 (0.2%) samples deleted
finding control points in Right_Cerebral_White_Matter....
found 39478 control points for structure...
bounding box (67, 47, 32) --> (130, 143, 195)
Right_Cerebral_White_Matter: limiting intensities to 97.0 --> 132.0
17 of 9476 (0.2%) samples deleted
finding control points in Left_Cerebellum_White_Matter....
found 3105 control points for structure...
bounding box (130, 124, 62) --> (176, 163, 115)
Left_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0
0 of 352 (0.0%) samples deleted
finding control points in Right_Cerebellum_White_Matter....
found 2710 control points for structure...
bounding box (87, 124, 59) --> (128, 162, 115)
Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0
2 of 393 (0.5%) samples deleted
finding control points in Brain_Stem....
found 3475 control points for structure...
bounding box (113, 114, 97) --> (145, 173, 125)
Brain_Stem: limiting intensities to 88.0 --> 132.0
0 of 523 (0.0%) samples deleted
using 20036 total control points for intensity normalization...
bias field = 0.973 +- 0.051
189 of 19997 control points discarded
finding control points in Left_Cerebral_White_Matter....
found 40230 control points for structure...
bounding box (129, 48, 34) --> (191, 150, 194)
Left_Cerebral_White_Matter: limiting intensities to 90.0 --> 132.0
30 of 9460 (0.3%) samples deleted
finding control points in Right_Cerebral_White_Matter....
found 39478 control points for structure...
bounding box (67, 47, 32) --> (130, 143, 195)
Right_Cerebral_White_Matter: limiting intensities to 91.0 --> 132.0
20 of 9622 (0.2%) samples deleted
finding control points in Left_Cerebellum_White_Matter....
found 3105 control points for structure...
bounding box (130, 124, 62) --> (176, 163, 115)
Left_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0
158 of 479 (33.0%) samples deleted
finding control points in Right_Cerebellum_White_Matter....
found 2710 control points for structure...
bounding box (87, 124, 59) --> (128, 162, 115)
Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0
157 of 462 (34.0%) samples deleted
finding control points in Brain_Stem....
found 3475 control points for structure...
bounding box (113, 114, 97) --> (145, 173, 125)
Brain_Stem: limiting intensities to 88.0 --> 132.0
40 of 600 (6.7%) samples deleted
using 20623 total control points for intensity normalization...
bias field = 1.029 +- 0.049
175 of 20122 control points discarded
finding control points in Left_Cerebral_White_Matter....
found 40230 control points for structure...
bounding box (129, 48, 34) --> (191, 150, 194)
Left_Cerebral_White_Matter: limiting intensities to 91.0 --> 132.0
21 of 9495 (0.2%) samples deleted
finding control points in Right_Cerebral_White_Matter....
found 39478 control points for structure...
bounding box (67, 47, 32) --> (130, 143, 195)
Right_Cerebral_White_Matter: limiting intensities to 91.0 --> 132.0
29 of 9631 (0.3%) samples deleted
finding control points in Left_Cerebellum_White_Matter....
found 3105 control points for structure...
bounding box (130, 124, 62) --> (176, 163, 115)
Left_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0
245 of 503 (48.7%) samples deleted
finding control points in Right_Cerebellum_White_Matter....
found 2710 control points for structure...
bounding box (87, 124, 59) --> (128, 162, 115)
Right_Cerebellum_White_Matter: limiting intensities to 88.0 --> 132.0
274 of 465 (58.9%) samples deleted
finding control points in Brain_Stem....
found 3475 control points for structure...
bounding box (113, 114, 97) --> (145, 173, 125)
Brain_Stem: limiting intensities to 88.0 --> 132.0
88 of 638 (13.8%) samples deleted
using 20732 total control points for intensity normalization...
bias field = 1.028 +- 0.043
159 of 19866 control points discarded
writing normalized volume to norm.mgz...
writing control points to ctrl_pts.mgz
freeing GCA...done.
normalization took 1 minutes and 30 seconds.
#--------------------------------------
#@# CA Reg Thu Jun 29 03:19:21 UTC 2023
/home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
mri_ca_register -nobigventricles -T transforms/talairach.lta -align-after -mask brainmask.mgz norm.mgz /opt/freesurfer-7.3.2/average/RB_all_2020-01-02.gca transforms/talairach.m3z
not handling expanded ventricles...
using previously computed transform transforms/talairach.lta
renormalizing sequences with structure alignment, equivalent to:
-renormalize
-regularize_mean 0.500
-regularize 0.500
using MR volume brainmask.mgz to mask input volume...
== Number of threads available to mri_ca_register for OpenMP = 1 ==
reading 1 input volumes...
logging results to talairach.log
reading input volume 'norm.mgz'...
reading GCA '/opt/freesurfer-7.3.2/average/RB_all_2020-01-02.gca'...
label assignment complete, 0 changed (0.00%)
freeing gibbs priors...done.
average std[0] = 5.0
Starting GCAMregister()
label assignment complete, 0 changed (0.00%)
npasses = 1, nlevels = 6
#pass# 1 of 1 ************************
enabling zero nodes
setting smoothness cost coefficient to 0.156
#GCAMreg# pass 0 level1 5 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.16
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.758584
#FOTS# QuadFit found better minimum quadopt=(dt=163.994,rms=0.698519) vs oldopt=(dt=92.48,rms=0.710544)
#GCMRL# 0 dt 163.993517 rms 0.699 7.918% neg 0 invalid 762 tFOTS 13.6960 tGradient 22.4980 tsec 37.2090
#FOTS# QuadFit found better minimum quadopt=(dt=164.117,rms=0.680674) vs oldopt=(dt=92.48,rms=0.684569)
#GCMRL# 1 dt 164.116959 rms 0.681 2.555% neg 0 invalid 762 tFOTS 13.5290 tGradient 22.6890 tsec 37.2140
#FOTS# QuadFit found better minimum quadopt=(dt=166.748,rms=0.672556) vs oldopt=(dt=92.48,rms=0.674419)
#GCMRL# 2 dt 166.748299 rms 0.673 1.193% neg 0 invalid 762 tFOTS 13.4120 tGradient 22.9490 tsec 37.3650
#FOTS# QuadFit found better minimum quadopt=(dt=144.162,rms=0.668033) vs oldopt=(dt=92.48,rms=0.668858)
#GCMRL# 3 dt 144.161616 rms 0.668 0.673% neg 0 invalid 762 tFOTS 13.4810 tGradient 22.1720 tsec 36.6610
#FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.662801) vs oldopt=(dt=369.92,rms=0.662948)
#GCMRL# 4 dt 295.936000 rms 0.663 0.783% neg 0 invalid 762 tFOTS 13.4150 tGradient 22.1260 tsec 36.5390
#FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.659698) vs oldopt=(dt=92.48,rms=0.659772)
#GCMRL# 5 dt 110.976000 rms 0.660 0.468% neg 0 invalid 762 tFOTS 14.2380 tGradient 22.1010 tsec 37.3480
#FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.654475) vs oldopt=(dt=369.92,rms=0.655164)
#GCMRL# 6 dt 517.888000 rms 0.654 0.792% neg 0 invalid 762 tFOTS 15.4310 tGradient 22.2300 tsec 38.7790
#GCMRL# 7 dt 92.480000 rms 0.652 0.346% neg 0 invalid 762 tFOTS 14.2470 tGradient 23.8680 tsec 39.1470
#GCMRL# 8 dt 1479.680000 rms 0.645 1.041% neg 0 invalid 762 tFOTS 13.5270 tGradient 22.9390 tsec 37.4660
#GCMRL# 9 dt 92.480000 rms 0.643 0.305% neg 0 invalid 762 tFOTS 13.4720 tGradient 22.8350 tsec 37.3090
#FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.641752) vs oldopt=(dt=369.92,rms=0.642094)
#GCMRL# 10 dt 517.888000 rms 0.642 0.264% neg 0 invalid 762 tFOTS 14.2290 tGradient 22.8760 tsec 38.1260
#FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.641504) vs oldopt=(dt=92.48,rms=0.641536)
#GCMRL# 11 dt 129.472000 rms 0.642 0.000% neg 0 invalid 762 tFOTS 13.4430 tGradient 22.3460 tsec 36.7980
#GCMRL# 12 dt 129.472000 rms 0.641 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.1880 tsec 23.2010
#GCMRL# 13 dt 129.472000 rms 0.641 0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.1700 tsec 23.1750
#GCMRL# 14 dt 129.472000 rms 0.640 0.091% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.6310 tsec 23.6240
#GCMRL# 15 dt 129.472000 rms 0.639 0.133% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.5700 tsec 23.5680
#GCMRL# 16 dt 129.472000 rms 0.638 0.154% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.5020 tsec 23.4980
#GCMRL# 17 dt 129.472000 rms 0.637 0.159% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.4060 tsec 23.4040
#GCMRL# 18 dt 129.472000 rms 0.636 0.146% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.3760 tsec 23.3680
#GCMRL# 19 dt 129.472000 rms 0.636 0.139% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.1930 tsec 23.1840
#GCMRL# 20 dt 129.472000 rms 0.635 0.145% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.4080 tsec 23.4440
#GCMRL# 21 dt 129.472000 rms 0.634 0.138% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.2990 tsec 23.3060
#GCMRL# 22 dt 129.472000 rms 0.633 0.131% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.8600 tsec 23.8540
#GCMRL# 23 dt 129.472000 rms 0.632 0.122% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.7470 tsec 23.7400
#GCMRL# 24 dt 129.472000 rms 0.631 0.118% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.9250 tsec 23.9310
#FOTS# QuadFit found better minimum quadopt=(dt=32.368,rms=0.631477) vs oldopt=(dt=23.12,rms=0.631477)
#GCAMreg# pass 0 level1 5 level2 1 tsec 801.6 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.16
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.632139
#FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.630971) vs oldopt=(dt=92.48,rms=0.631031)
#GCMRL# 26 dt 129.472000 rms 0.631 0.185% neg 0 invalid 762 tFOTS 13.3270 tGradient 22.7840 tsec 37.1060
#GCMRL# 27 dt 369.920000 rms 0.630 0.000% neg 0 invalid 762 tFOTS 13.3130 tGradient 22.9290 tsec 37.2560
#GCMRL# 28 dt 369.920000 rms 0.630 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.8380 tsec 23.8180
#GCMRL# 29 dt 369.920000 rms 0.630 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 23.0160 tsec 24.0200
#GCMRL# 30 dt 369.920000 rms 0.630 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 22.2670 tsec 23.2600
setting smoothness cost coefficient to 0.615
#GCAMreg# pass 0 level1 4 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.62
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.638461
#FOTS# QuadFit found better minimum quadopt=(dt=72.716,rms=0.633052) vs oldopt=(dt=103.68,rms=0.633761)
#GCMRL# 32 dt 72.715953 rms 0.633 0.847% neg 0 invalid 762 tFOTS 13.3520 tGradient 14.0030 tsec 28.3480
#FOTS# QuadFit found better minimum quadopt=(dt=217.924,rms=0.626214) vs oldopt=(dt=103.68,rms=0.627862)
#GCMRL# 33 dt 217.924051 rms 0.626 1.080% neg 0 invalid 762 tFOTS 14.1830 tGradient 13.9330 tsec 29.1080
#FOTS# QuadFit found better minimum quadopt=(dt=64.4384,rms=0.622) vs oldopt=(dt=25.92,rms=0.623136)
#GCMRL# 34 dt 64.438356 rms 0.622 0.673% neg 0 invalid 762 tFOTS 13.3560 tGradient 13.9550 tsec 28.3080
#FOTS# QuadFit found better minimum quadopt=(dt=101.517,rms=0.619487) vs oldopt=(dt=103.68,rms=0.619489)
#GCMRL# 35 dt 101.517241 rms 0.619 0.404% neg 0 invalid 762 tFOTS 13.4030 tGradient 13.9100 tsec 28.3100
#FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.618122) vs oldopt=(dt=25.92,rms=0.618385)
#GCMRL# 36 dt 36.288000 rms 0.618 0.000% neg 0 invalid 762 tFOTS 13.3780 tGradient 13.9190 tsec 28.3170
#GCMRL# 37 dt 36.288000 rms 0.617 0.101% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.8240 tsec 14.8140
#GCMRL# 38 dt 36.288000 rms 0.616 0.168% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.8390 tsec 14.8340
#GCMRL# 39 dt 36.288000 rms 0.615 0.233% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.8670 tsec 14.8660
#GCMRL# 40 dt 36.288000 rms 0.613 0.274% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.8710 tsec 14.8720
#GCMRL# 41 dt 36.288000 rms 0.612 0.285% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.9360 tsec 14.9330
#GCMRL# 42 dt 36.288000 rms 0.610 0.274% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.8280 tsec 14.8320
#GCMRL# 43 dt 36.288000 rms 0.608 0.287% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.8740 tsec 14.8780
#GCMRL# 44 dt 36.288000 rms 0.607 0.249% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.9500 tsec 14.9430
#GCMRL# 45 dt 36.288000 rms 0.605 0.211% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.2620 tsec 15.2630
#GCMRL# 46 dt 36.288000 rms 0.604 0.176% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.0640 tsec 15.1280
#GCMRL# 47 dt 36.288000 rms 0.604 0.125% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.4130 tsec 15.4140
#GCMRL# 48 dt 36.288000 rms 0.603 0.134% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.3510 tsec 15.3360
#GCMRL# 49 dt 36.288000 rms 0.602 0.126% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.1020 tsec 15.0960
#GCMRL# 50 dt 36.288000 rms 0.601 0.098% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.2210 tsec 15.2210
#GCMRL# 51 dt 36.288000 rms 0.601 0.067% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.1290 tsec 15.1830
#GCMRL# 52 dt 0.450000 rms 0.601 0.000% neg 0 invalid 762 tFOTS 13.4920 tGradient 14.3370 tsec 28.8460
#GCAMreg# pass 0 level1 4 level2 1 tsec 419.644 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.62
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.60173
#FOTS# QuadFit found better minimum quadopt=(dt=31.104,rms=0.600725) vs oldopt=(dt=25.92,rms=0.600734)
#GCMRL# 54 dt 31.104000 rms 0.601 0.167% neg 0 invalid 762 tFOTS 13.4430 tGradient 14.1980 tsec 28.6520
setting smoothness cost coefficient to 2.353
#GCAMreg# pass 0 level1 3 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.35
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.628375
#GCMRL# 56 dt 0.000000 rms 0.628 0.118% neg 0 invalid 762 tFOTS 11.8090 tGradient 8.9990 tsec 21.8210
#GCAMreg# pass 0 level1 3 level2 1 tsec 51.226 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.35
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.628375
#GCMRL# 58 dt 0.500000 rms 0.628 0.120% neg 0 invalid 762 tFOTS 12.6740 tGradient 9.0310 tsec 22.7010
#FOTS# QuadFit found better minimum quadopt=(dt=0.075,rms=0.627612) vs oldopt=(dt=0.125,rms=0.627613)
#GCMRL# 59 dt 0.075000 rms 0.628 0.000% neg 0 invalid 762 tFOTS 12.5520 tGradient 8.9930 tsec 22.5780
setting smoothness cost coefficient to 8.000
#GCAMreg# pass 0 level1 2 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=8.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.706275
#FOTS# QuadFit found better minimum quadopt=(dt=2.36825,rms=0.68245) vs oldopt=(dt=2.88,rms=0.683533)
#GCMRL# 61 dt 2.368248 rms 0.682 3.373% neg 0 invalid 762 tFOTS 12.8890 tGradient 6.0400 tsec 19.9420
#FOTS# QuadFit found better minimum quadopt=(dt=1.75771,rms=0.679391) vs oldopt=(dt=2.88,rms=0.680485)
#GCMRL# 62 dt 1.757709 rms 0.679 0.448% neg 0 invalid 762 tFOTS 12.7230 tGradient 6.0840 tsec 19.8080
#FOTS# QuadFit found better minimum quadopt=(dt=1.42308,rms=0.678637) vs oldopt=(dt=0.72,rms=0.678831)
#GCMRL# 63 dt 1.423077 rms 0.679 0.000% neg 0 invalid 762 tFOTS 12.6320 tGradient 6.0240 tsec 19.6780
#GCMRL# 64 dt 1.423077 rms 0.679 0.013% neg 0 invalid 762 tFOTS 0.0000 tGradient 5.8790 tsec 6.8800
#GCAMreg# pass 0 level1 2 level2 1 tsec 80.74 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=8.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.679068
#FOTS# QuadFit found better minimum quadopt=(dt=0.252,rms=0.678531) vs oldopt=(dt=0.18,rms=0.678534)
#GCMRL# 66 dt 0.252000 rms 0.679 0.079% neg 0 invalid 762 tFOTS 11.7920 tGradient 5.8860 tsec 18.6760
#FOTS# QuadFit found better minimum quadopt=(dt=0.1875,rms=0.678516) vs oldopt=(dt=0.18,rms=0.678516)
#GCMRL# 67 dt 0.187500 rms 0.679 0.000% neg 0 invalid 762 tFOTS 12.5900 tGradient 5.9090 tsec 19.5260
setting smoothness cost coefficient to 20.000
#GCAMreg# pass 0 level1 1 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=20.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.725471
#FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.724815) vs oldopt=(dt=0.08,rms=0.724842)
#GCMRL# 69 dt 0.112000 rms 0.725 0.091% neg 0 invalid 762 tFOTS 12.6070 tGradient 3.3590 tsec 16.9560
#FOTS# QuadFit found better minimum quadopt=(dt=0.384,rms=0.724213) vs oldopt=(dt=0.32,rms=0.724226)
#GCMRL# 70 dt 0.384000 rms 0.724 0.000% neg 0 invalid 762 tFOTS 12.6220 tGradient 3.4130 tsec 17.0530
#GCMRL# 71 dt 0.384000 rms 0.721 0.400% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3640 tsec 4.4050
#GCMRL# 72 dt 0.384000 rms 0.719 0.358% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3670 tsec 4.3670
#GCMRL# 73 dt 0.384000 rms 0.717 0.206% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4860 tsec 4.4880
#GCMRL# 74 dt 0.384000 rms 0.716 0.224% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4300 tsec 4.4210
#GCMRL# 75 dt 0.384000 rms 0.714 0.168% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4540 tsec 4.4490
#GCMRL# 76 dt 0.384000 rms 0.714 -0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.3910 tsec 5.3390
#FOTS# QuadFit found better minimum quadopt=(dt=0.768,rms=0.713161) vs oldopt=(dt=1.28,rms=0.713384)
#GCMRL# 77 dt 0.768000 rms 0.713 0.176% neg 0 invalid 762 tFOTS 12.6280 tGradient 3.4410 tsec 17.0650
#FOTS# QuadFit found better minimum quadopt=(dt=0.489247,rms=0.709572) vs oldopt=(dt=0.32,rms=0.710242)
#GCMRL# 78 dt 0.489247 rms 0.710 0.503% neg 0 invalid 762 tFOTS 12.5550 tGradient 3.4020 tsec 16.9370
#FOTS# QuadFit found better minimum quadopt=(dt=0.028,rms=0.709522) vs oldopt=(dt=0.02,rms=0.709528)
#GCMRL# 79 dt 0.028000 rms 0.710 0.000% neg 0 invalid 762 tFOTS 12.5190 tGradient 3.3990 tsec 16.9260
#GCMRL# 80 dt 0.028000 rms 0.710 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4070 tsec 4.3960
#GCAMreg# pass 0 level1 1 level2 1 tsec 128.749 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=20.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.709986
#FOTS# QuadFit found better minimum quadopt=(dt=0.028,rms=0.709517) vs oldopt=(dt=0.02,rms=0.709518)
#GCMRL# 82 dt 0.028000 rms 0.710 0.066% neg 0 invalid 762 tFOTS 12.5690 tGradient 3.5570 tsec 17.1190
resetting metric properties...
setting smoothness cost coefficient to 40.000
#GCAMreg# pass 0 level1 0 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=40.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.682406
#FOTS# QuadFit found better minimum quadopt=(dt=0.229784,rms=0.676832) vs oldopt=(dt=0.32,rms=0.677403)
#GCMRL# 84 dt 0.229784 rms 0.677 0.817% neg 0 invalid 762 tFOTS 12.5000 tGradient 2.7990 tsec 16.2990
#FOTS# QuadFit found better minimum quadopt=(dt=0.024,rms=0.676562) vs oldopt=(dt=0.02,rms=0.676563)
#GCMRL# 85 dt 0.024000 rms 0.677 0.000% neg 0 invalid 762 tFOTS 12.5180 tGradient 2.7760 tsec 16.3040
#GCAMreg# pass 0 level1 0 level2 1 tsec 43.762 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=40.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.677038
#FOTS# QuadFit found better minimum quadopt=(dt=0.006,rms=0.676552) vs oldopt=(dt=0.005,rms=0.676552)
#GCMRL# 87 dt 0.006000 rms 0.677 0.072% neg 0 invalid 762 tFOTS 12.6010 tGradient 2.7400 tsec 16.3440
#GCMRL# 88 dt 0.050000 rms 0.677 0.000% neg 0 invalid 762 tFOTS 11.7870 tGradient 2.7630 tsec 15.5610
GCAMregister done in 32.4389 min
Starting GCAmapRenormalizeWithAlignment() without scales
renormalizing by structure alignment....
renormalizing input #0
gca peak = 0.10253 (16)
mri peak = 0.24309 ( 7)
Left_Lateral_Ventricle (4): linear fit = 0.32 x + 0.0 (1137 voxels, overlap=0.004)
Left_Lateral_Ventricle (4): linear fit = 0.40 x + 0.0 (1137 voxels, peak = 5), gca=6.4
gca peak = 0.17690 (16)
mri peak = 0.19113 (10)
Right_Lateral_Ventricle (43): linear fit = 0.44 x + 0.0 (993 voxels, overlap=0.172)
Right_Lateral_Ventricle (43): linear fit = 0.44 x + 0.0 (993 voxels, peak = 7), gca=7.0
gca peak = 0.28275 (96)
mri peak = 0.11886 (106)
Right_Pallidum (52): linear fit = 1.10 x + 0.0 (865 voxels, overlap=0.105)
Right_Pallidum (52): linear fit = 1.10 x + 0.0 (865 voxels, peak = 105), gca=105.1
gca peak = 0.18948 (93)
mri peak = 0.10308 (110)
Left_Pallidum (13): linear fit = 1.13 x + 0.0 (790 voxels, overlap=0.030)
Left_Pallidum (13): linear fit = 1.13 x + 0.0 (790 voxels, peak = 106), gca=105.6
gca peak = 0.20755 (55)
mri peak = 0.09290 (56)
Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (737 voxels, overlap=1.000)
Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (737 voxels, peak = 55), gca=55.0
gca peak = 0.31831 (58)
mri peak = 0.11220 (60)
Left_Hippocampus (17): linear fit = 1.02 x + 0.0 (713 voxels, overlap=0.999)
Left_Hippocampus (17): linear fit = 1.02 x + 0.0 (713 voxels, peak = 59), gca=59.4
gca peak = 0.11957 (102)
mri peak = 0.13750 (102)
Right_Cerebral_White_Matter (41): linear fit = 1.02 x + 0.0 (61984 voxels, overlap=0.576)
Right_Cerebral_White_Matter (41): linear fit = 1.02 x + 0.0 (61984 voxels, peak = 105), gca=104.5
gca peak = 0.11429 (102)
mri peak = 0.13708 (104)
Left_Cerebral_White_Matter (2): linear fit = 1.02 x + 0.0 (64475 voxels, overlap=0.597)
Left_Cerebral_White_Matter (2): linear fit = 1.02 x + 0.0 (64475 voxels, peak = 105), gca=104.5
gca peak = 0.14521 (59)
mri peak = 0.03230 (63)
Left_Cerebral_Cortex (3): linear fit = 1.08 x + 0.0 (27092 voxels, overlap=0.559)
Left_Cerebral_Cortex (3): linear fit = 1.08 x + 0.0 (27092 voxels, peak = 63), gca=63.4
gca peak = 0.14336 (58)
mri peak = 0.03626 (60)
Right_Cerebral_Cortex (42): linear fit = 1.08 x + 0.0 (27239 voxels, overlap=0.765)
Right_Cerebral_Cortex (42): linear fit = 1.08 x + 0.0 (27239 voxels, peak = 62), gca=62.4
gca peak = 0.13305 (70)
mri peak = 0.12909 (75)
Right_Caudate (50): linear fit = 1.08 x + 0.0 (602 voxels, overlap=0.560)
Right_Caudate (50): linear fit = 1.08 x + 0.0 (602 voxels, peak = 75), gca=75.2
gca peak = 0.15761 (71)
mri peak = 0.11149 (78)
Left_Caudate (11): linear fit = 1.03 x + 0.0 (884 voxels, overlap=0.757)
Left_Caudate (11): linear fit = 1.03 x + 0.0 (884 voxels, peak = 73), gca=73.5
gca peak = 0.13537 (57)
mri peak = 0.04425 (46)
Left_Cerebellum_Cortex (8): linear fit = 0.86 x + 0.0 (20823 voxels, overlap=0.337)
Left_Cerebellum_Cortex (8): linear fit = 0.86 x + 0.0 (20823 voxels, peak = 49), gca=48.7
gca peak = 0.13487 (56)
mri peak = 0.04085 (44)
Right_Cerebellum_Cortex (47): linear fit = 0.82 x + 0.0 (23475 voxels, overlap=0.237)
Right_Cerebellum_Cortex (47): linear fit = 0.82 x + 0.0 (23475 voxels, peak = 46), gca=46.2
gca peak = 0.19040 (84)
mri peak = 0.05880 (87)
Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (6844 voxels, overlap=0.799)
Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (6844 voxels, peak = 89), gca=89.5
gca peak = 0.18871 (83)
mri peak = 0.07503 (88)
Right_Cerebellum_White_Matter (46): linear fit = 1.05 x + 0.0 (6722 voxels, overlap=0.828)
Right_Cerebellum_White_Matter (46): linear fit = 1.05 x + 0.0 (6722 voxels, peak = 88), gca=87.6
gca peak = 0.24248 (57)
mri peak = 0.09624 (65)
Left_Amygdala (18): linear fit = 1.10 x + 0.0 (430 voxels, overlap=1.007)
Left_Amygdala (18): linear fit = 1.10 x + 0.0 (430 voxels, peak = 62), gca=62.4
gca peak = 0.35833 (56)
mri peak = 0.10251 (58)
Right_Amygdala (54): linear fit = 1.04 x + 0.0 (501 voxels, overlap=0.958)
Right_Amygdala (54): linear fit = 1.04 x + 0.0 (501 voxels, peak = 59), gca=58.5
gca peak = 0.12897 (85)
mri peak = 0.06635 (91)
Left_Thalamus (10): linear fit = 1.08 x + 0.0 (5328 voxels, overlap=0.630)
Left_Thalamus (10): linear fit = 1.08 x + 0.0 (5328 voxels, peak = 91), gca=91.4
gca peak = 0.13127 (83)
mri peak = 0.06776 (89)
Right_Thalamus (49): linear fit = 1.12 x + 0.0 (4325 voxels, overlap=0.655)
Right_Thalamus (49): linear fit = 1.12 x + 0.0 (4325 voxels, peak = 93), gca=92.5
gca peak = 0.12974 (78)
mri peak = 0.07627 (84)
Left_Putamen (12): linear fit = 1.12 x + 0.0 (2361 voxels, overlap=0.481)
Left_Putamen (12): linear fit = 1.12 x + 0.0 (2361 voxels, peak = 87), gca=87.0
gca peak = 0.17796 (79)
mri peak = 0.09075 (90)
Right_Putamen (51): linear fit = 1.12 x + 0.0 (2325 voxels, overlap=0.594)
Right_Putamen (51): linear fit = 1.12 x + 0.0 (2325 voxels, peak = 89), gca=88.9
gca peak = 0.10999 (80)
mri peak = 0.06213 (91)
Brain_Stem (16): linear fit = 1.21 x + 0.0 (12102 voxels, overlap=0.417)
Brain_Stem (16): linear fit = 1.21 x + 0.0 (12102 voxels, peak = 96), gca=96.4
gca peak = 0.13215 (88)
mri peak = 0.08770 (101)
Right_VentralDC (60): linear fit = 1.21 x + 0.0 (1248 voxels, overlap=0.036)
Right_VentralDC (60): linear fit = 1.21 x + 0.0 (1248 voxels, peak = 106), gca=106.0
gca peak = 0.11941 (89)
mri peak = 0.09670 (101)
Left_VentralDC (28): linear fit = 1.18 x + 0.0 (1470 voxels, overlap=0.014)
Left_VentralDC (28): linear fit = 1.18 x + 0.0 (1470 voxels, peak = 105), gca=105.5
gca peak = 0.20775 (25)
mri peak = 0.25000 ( 7)
gca peak = 0.13297 (21)
mri peak = 0.54545 ( 6)
gca peak Unknown = 0.94777 ( 0)
gca peak Left_Inf_Lat_Vent = 0.19087 (28)
gca peak Third_Ventricle = 0.20775 (25)
gca peak Fourth_Ventricle = 0.13297 (21)
gca peak CSF = 0.16821 (33)
gca peak Left_Accumbens_area = 0.32850 (63)
gca peak Left_undetermined = 0.98480 (28)
gca peak Left_vessel = 0.40887 (53)
gca peak Left_choroid_plexus = 0.10898 (46)
gca peak Right_Inf_Lat_Vent = 0.17798 (26)
gca peak Right_Accumbens_area = 0.30137 (64)
gca peak Right_vessel = 0.47828 (52)
gca peak Right_choroid_plexus = 0.11612 (45)
gca peak Fifth_Ventricle = 0.59466 (35)
gca peak WM_hypointensities = 0.10053 (78)
gca peak non_WM_hypointensities = 0.07253 (60)
gca peak Optic_Chiasm = 0.25330 (73)
not using caudate to estimate GM means
estimating mean gm scale to be 1.05 x + 0.0
estimating mean wm scale to be 1.02 x + 0.0
estimating mean csf scale to be 0.42 x + 0.0
Left_Pallidum too bright - rescaling by 0.961 (from 1.135) to 101.4 (was 105.6)
Right_Pallidum too bright - rescaling by 0.965 (from 1.095) to 101.4 (was 105.1)
saving intensity scales to talairach.label_intensities.txt
GCAmapRenormalizeWithAlignment() took 4.90453 min
noneg pre
Starting GCAMregister()
label assignment complete, 0 changed (0.00%)
npasses = 1, nlevels = 6
#pass# 1 of 1 ************************
enabling zero nodes
setting smoothness cost coefficient to 0.008
#GCAMreg# pass 0 level1 5 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.01
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.6806
#FOTS# QuadFit found better minimum quadopt=(dt=139.795,rms=0.668653) vs oldopt=(dt=92.48,rms=0.670174)
#GCMRL# 90 dt 139.795222 rms 0.669 1.755% neg 0 invalid 762 tFOTS 14.8220 tGradient 13.0590 tsec 28.9730
#FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.660309) vs oldopt=(dt=369.92,rms=0.660784)
#GCMRL# 91 dt 295.936000 rms 0.660 1.248% neg 0 invalid 762 tFOTS 14.7560 tGradient 14.6300 tsec 30.4920
#FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.656212) vs oldopt=(dt=92.48,rms=0.656504)
#GCMRL# 92 dt 129.472000 rms 0.656 0.620% neg 0 invalid 762 tFOTS 15.6550 tGradient 14.8200 tsec 31.5930
#FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.653722) vs oldopt=(dt=92.48,rms=0.654172)
#GCMRL# 93 dt 129.472000 rms 0.654 0.379% neg 0 invalid 762 tFOTS 15.7150 tGradient 14.6390 tsec 31.4710
#FOTS# QuadFit found better minimum quadopt=(dt=443.904,rms=0.650128) vs oldopt=(dt=369.92,rms=0.650158)
#GCMRL# 94 dt 443.904000 rms 0.650 0.550% neg 0 invalid 762 tFOTS 14.9280 tGradient 15.0370 tsec 30.9620
#GCMRL# 95 dt 92.480000 rms 0.647 0.487% neg 0 invalid 762 tFOTS 14.2400 tGradient 13.0390 tsec 28.2870
#FOTS# QuadFit found better minimum quadopt=(dt=887.808,rms=0.641934) vs oldopt=(dt=1479.68,rms=0.643048)
#GCMRL# 96 dt 887.808000 rms 0.642 0.777% neg 0 invalid 762 tFOTS 13.4050 tGradient 13.1100 tsec 27.5140
#FOTS# QuadFit found better minimum quadopt=(dt=78.9173,rms=0.639691) vs oldopt=(dt=92.48,rms=0.639697)
#GCMRL# 97 dt 78.917293 rms 0.640 0.349% neg 0 invalid 762 tFOTS 14.2610 tGradient 13.0730 tsec 28.3380
#FOTS# QuadFit found better minimum quadopt=(dt=1183.74,rms=0.637391) vs oldopt=(dt=1479.68,rms=0.637705)
#GCMRL# 98 dt 1183.744000 rms 0.637 0.359% neg 0 invalid 762 tFOTS 13.3980 tGradient 13.1270 tsec 27.5240
#FOTS# QuadFit found better minimum quadopt=(dt=110.976,rms=0.634966) vs oldopt=(dt=92.48,rms=0.635015)
#GCMRL# 99 dt 110.976000 rms 0.635 0.381% neg 0 invalid 762 tFOTS 14.1890 tGradient 13.1280 tsec 28.3150
#FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.634523) vs oldopt=(dt=92.48,rms=0.634569)
#GCMRL# 100 dt 129.472000 rms 0.635 0.070% neg 0 invalid 762 tFOTS 14.0230 tGradient 13.4050 tsec 28.4310
#GCMRL# 101 dt 369.920000 rms 0.634 0.127% neg 0 invalid 762 tFOTS 14.2650 tGradient 13.2330 tsec 28.5410
#FOTS# QuadFit found better minimum quadopt=(dt=73.984,rms=0.633149) vs oldopt=(dt=92.48,rms=0.633153)
#GCMRL# 102 dt 73.984000 rms 0.633 0.090% neg 0 invalid 762 tFOTS 13.5180 tGradient 13.3570 tsec 27.9140
#FOTS# QuadFit found better minimum quadopt=(dt=1183.74,rms=0.631797) vs oldopt=(dt=1479.68,rms=0.631966)
#GCMRL# 103 dt 1183.744000 rms 0.632 0.214% neg 0 invalid 762 tFOTS 14.2520 tGradient 13.2970 tsec 28.5500
#GCMRL# 104 dt 92.480000 rms 0.630 0.249% neg 0 invalid 762 tFOTS 14.3070 tGradient 13.3820 tsec 28.6850
#FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.629901) vs oldopt=(dt=369.92,rms=0.62995)
#GCMRL# 105 dt 295.936000 rms 0.630 0.051% neg 0 invalid 762 tFOTS 14.3750 tGradient 13.3920 tsec 28.7590
#FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.629375) vs oldopt=(dt=92.48,rms=0.62943)
#GCMRL# 106 dt 129.472000 rms 0.629 0.083% neg 0 invalid 762 tFOTS 13.3510 tGradient 13.1600 tsec 27.5160
#FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.629167) vs oldopt=(dt=92.48,rms=0.629196)
#GCMRL# 107 dt 129.472000 rms 0.629 0.000% neg 0 invalid 762 tFOTS 14.1410 tGradient 13.0200 tsec 28.1610
#GCMRL# 108 dt 129.472000 rms 0.629 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.6110 tsec 14.6960
#GCMRL# 109 dt 129.472000 rms 0.628 0.070% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8210 tsec 15.9210
#GCMRL# 110 dt 129.472000 rms 0.628 0.092% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8700 tsec 15.9640
#GCMRL# 111 dt 129.472000 rms 0.627 0.109% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.7100 tsec 15.7970
#GCMRL# 112 dt 129.472000 rms 0.626 0.137% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.7300 tsec 15.8270
#GCMRL# 113 dt 129.472000 rms 0.625 0.139% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.6190 tsec 15.7290
#GCMRL# 114 dt 129.472000 rms 0.624 0.148% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8830 tsec 15.9820
#GCMRL# 115 dt 129.472000 rms 0.624 0.149% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8490 tsec 15.9590
#GCMRL# 116 dt 129.472000 rms 0.623 0.140% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8460 tsec 15.9450
#GCMRL# 117 dt 129.472000 rms 0.622 0.155% neg 0 invalid 762 tFOTS 0.0000 tGradient 15.0790 tsec 16.1770
#GCMRL# 118 dt 129.472000 rms 0.621 0.150% neg 0 invalid 762 tFOTS 0.0000 tGradient 15.2130 tsec 16.3100
#GCMRL# 119 dt 129.472000 rms 0.620 0.129% neg 0 invalid 762 tFOTS 0.0000 tGradient 15.0770 tsec 16.1730
#GCMRL# 120 dt 129.472000 rms 0.619 0.139% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8650 tsec 15.9660
#GCMRL# 121 dt 129.472000 rms 0.618 0.141% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.6610 tsec 15.7740
#GCMRL# 122 dt 129.472000 rms 0.617 0.135% neg 0 invalid 762 tFOTS 0.0000 tGradient 15.1100 tsec 16.2080
#GCMRL# 123 dt 129.472000 rms 0.617 0.114% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.7620 tsec 15.8670
#GCMRL# 124 dt 129.472000 rms 0.616 0.120% neg 0 invalid 762 tFOTS 0.0000 tGradient 15.2610 tsec 16.3760
#GCMRL# 125 dt 129.472000 rms 0.615 0.142% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8050 tsec 15.9100
#GCMRL# 126 dt 129.472000 rms 0.614 0.137% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.2690 tsec 15.2750
#GCMRL# 127 dt 129.472000 rms 0.613 0.134% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2040 tsec 14.2170
#GCMRL# 128 dt 129.472000 rms 0.613 0.117% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3650 tsec 14.3480
#GCMRL# 129 dt 129.472000 rms 0.612 0.100% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2840 tsec 14.2810
#GCMRL# 130 dt 129.472000 rms 0.611 0.124% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2730 tsec 14.2670
#GCMRL# 131 dt 129.472000 rms 0.611 0.119% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3620 tsec 14.3790
#GCMRL# 132 dt 129.472000 rms 0.610 0.108% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2590 tsec 14.2570
#GCMRL# 133 dt 129.472000 rms 0.609 0.100% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2120 tsec 14.2160
#GCMRL# 134 dt 129.472000 rms 0.609 0.084% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3370 tsec 14.3310
#GCMRL# 135 dt 129.472000 rms 0.608 0.099% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3420 tsec 14.3460
#GCMRL# 136 dt 129.472000 rms 0.608 0.084% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.4430 tsec 14.5030
#GCMRL# 137 dt 129.472000 rms 0.607 0.081% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3820 tsec 14.3780
#GCMRL# 138 dt 129.472000 rms 0.607 0.070% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2960 tsec 14.3000
#GCMRL# 139 dt 129.472000 rms 0.606 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3200 tsec 14.3480
#GCMRL# 140 dt 129.472000 rms 0.606 0.088% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2420 tsec 14.2390
#GCMRL# 141 dt 129.472000 rms 0.605 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2170 tsec 14.2110
#GCMRL# 142 dt 129.472000 rms 0.605 0.070% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2630 tsec 14.2710
#GCMRL# 143 dt 129.472000 rms 0.605 0.063% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.1580 tsec 14.1850
#GCMRL# 144 dt 129.472000 rms 0.604 0.079% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2950 tsec 14.2850
#GCMRL# 145 dt 129.472000 rms 0.604 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3080 tsec 14.2980
#GCMRL# 146 dt 129.472000 rms 0.603 0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2280 tsec 14.2180
#GCMRL# 147 dt 129.472000 rms 0.603 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2530 tsec 14.2570
#GCMRL# 148 dt 129.472000 rms 0.602 0.071% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.1600 tsec 14.1570
#GCMRL# 149 dt 129.472000 rms 0.602 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3440 tsec 14.3890
#GCMRL# 150 dt 129.472000 rms 0.602 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.4260 tsec 14.4260
#GCMRL# 151 dt 129.472000 rms 0.602 0.047% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2740 tsec 14.2860
#GCMRL# 152 dt 129.472000 rms 0.601 0.057% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3730 tsec 14.3670
#GCMRL# 153 dt 129.472000 rms 0.601 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2130 tsec 14.2040
#GCMRL# 154 dt 129.472000 rms 0.601 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2950 tsec 14.2870
#GCMRL# 155 dt 129.472000 rms 0.600 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2640 tsec 14.2560
#GCMRL# 156 dt 129.472000 rms 0.600 0.057% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2290 tsec 14.2360
#GCMRL# 157 dt 129.472000 rms 0.600 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3920 tsec 14.3820
#GCMRL# 158 dt 129.472000 rms 0.599 0.039% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2910 tsec 14.2850
#GCMRL# 159 dt 129.472000 rms 0.599 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.1910 tsec 14.1870
#GCMRL# 160 dt 129.472000 rms 0.599 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2700 tsec 14.2620
#GCMRL# 161 dt 129.472000 rms 0.599 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3320 tsec 14.3300
#GCMRL# 162 dt 129.472000 rms 0.599 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3740 tsec 14.3730
#GCMRL# 163 dt 129.472000 rms 0.598 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0620 tsec 14.0680
#GCMRL# 164 dt 129.472000 rms 0.598 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2460 tsec 14.2440
#GCMRL# 165 dt 129.472000 rms 0.598 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0420 tsec 14.0390
#GCMRL# 166 dt 129.472000 rms 0.598 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.1530 tsec 14.1540
#GCMRL# 167 dt 129.472000 rms 0.597 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0860 tsec 14.0790
#GCMRL# 168 dt 129.472000 rms 0.597 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 12.9830 tsec 13.9850
#GCMRL# 169 dt 129.472000 rms 0.597 0.047% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0750 tsec 14.0870
#GCMRL# 170 dt 129.472000 rms 0.597 0.047% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0930 tsec 14.0990
#GCMRL# 171 dt 129.472000 rms 0.596 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0610 tsec 14.0630
#GCMRL# 172 dt 129.472000 rms 0.596 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.1940 tsec 14.1990
#GCMRL# 173 dt 129.472000 rms 0.596 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0640 tsec 14.0590
#GCMRL# 174 dt 129.472000 rms 0.596 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0200 tsec 14.0130
#GCMRL# 175 dt 129.472000 rms 0.596 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0940 tsec 14.0860
#GCMRL# 176 dt 129.472000 rms 0.595 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.5890 tsec 14.6850
#GCMRL# 177 dt 129.472000 rms 0.595 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.7080 tsec 15.8080
#GCMRL# 178 dt 129.472000 rms 0.595 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8930 tsec 15.9930
#GCMRL# 179 dt 129.472000 rms 0.595 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.0850 tsec 15.0840
#GCMRL# 180 dt 129.472000 rms 0.595 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.1530 tsec 14.1440
#GCMRL# 181 dt 129.472000 rms 0.595 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0840 tsec 14.0660
#GCMRL# 182 dt 129.472000 rms 0.595 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0660 tsec 14.0570
#GCMRL# 183 dt 129.472000 rms 0.594 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0950 tsec 14.0850
#GCMRL# 184 dt 129.472000 rms 0.594 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0390 tsec 14.0410
#GCMRL# 185 dt 129.472000 rms 0.594 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0670 tsec 14.0610
#GCMRL# 186 dt 129.472000 rms 0.594 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0570 tsec 14.0540
#GCMRL# 187 dt 129.472000 rms 0.594 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0980 tsec 14.1030
#GCMRL# 188 dt 129.472000 rms 0.593 0.028% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0550 tsec 14.0510
#GCMRL# 189 dt 129.472000 rms 0.593 0.019% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0750 tsec 14.0790
#GCMRL# 190 dt 129.472000 rms 0.593 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0580 tsec 14.0610
#GCMRL# 191 dt 129.472000 rms 0.593 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.0190 tsec 14.0160
#GCMRL# 192 dt 129.472000 rms 0.593 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 12.9680 tsec 13.9840
#GCMRL# 193 dt 129.472000 rms 0.593 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.1630 tsec 14.1830
#GCMRL# 194 dt 129.472000 rms 0.593 0.016% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.3150 tsec 14.3390
#GCMRL# 195 dt 129.472000 rms 0.592 0.022% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2640 tsec 14.2620
#GCMRL# 196 dt 129.472000 rms 0.592 0.021% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.1850 tsec 14.2100
#FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.592305) vs oldopt=(dt=369.92,rms=0.592314)
#GCMRL# 197 dt 517.888000 rms 0.592 0.000% neg 0 invalid 762 tFOTS 14.4740 tGradient 13.1640 tsec 28.6880
#GCAMreg# pass 0 level1 5 level2 1 tsec 1873.44 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.01
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.592976
#GCMRL# 199 dt 369.920000 rms 0.591 0.362% neg 0 invalid 762 tFOTS 13.4540 tGradient 13.3610 tsec 27.8210
#FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.590529) vs oldopt=(dt=92.48,rms=0.590554)
#GCMRL# 200 dt 129.472000 rms 0.591 0.051% neg 0 invalid 762 tFOTS 13.4470 tGradient 13.3740 tsec 27.8110
#FOTS# QuadFit found better minimum quadopt=(dt=517.888,rms=0.590044) vs oldopt=(dt=369.92,rms=0.59011)
#GCMRL# 201 dt 517.888000 rms 0.590 0.082% neg 0 invalid 762 tFOTS 14.2080 tGradient 13.3350 tsec 28.5460
#FOTS# QuadFit found better minimum quadopt=(dt=129.472,rms=0.589824) vs oldopt=(dt=92.48,rms=0.589841)
#GCMRL# 202 dt 129.472000 rms 0.590 0.000% neg 0 invalid 762 tFOTS 15.6600 tGradient 13.7650 tsec 30.5460
#GCMRL# 203 dt 129.472000 rms 0.590 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8330 tsec 15.9280
#GCMRL# 204 dt 129.472000 rms 0.589 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8210 tsec 15.9180
#GCMRL# 205 dt 129.472000 rms 0.589 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8560 tsec 15.9530
#GCMRL# 206 dt 129.472000 rms 0.589 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.9420 tsec 16.0380
#GCMRL# 207 dt 129.472000 rms 0.589 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8970 tsec 16.0050
#GCMRL# 208 dt 129.472000 rms 0.588 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.6040 tsec 15.6150
#GCMRL# 209 dt 129.472000 rms 0.588 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.1700 tsec 14.1670
#GCMRL# 210 dt 129.472000 rms 0.588 0.021% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.1390 tsec 14.1350
#GCMRL# 211 dt 129.472000 rms 0.588 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.1540 tsec 14.1620
#GCMRL# 212 dt 129.472000 rms 0.588 0.036% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.0180 tsec 15.1280
#GCMRL# 213 dt 129.472000 rms 0.588 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 15.0400 tsec 16.1360
#GCMRL# 214 dt 129.472000 rms 0.587 0.012% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.8910 tsec 16.0170
#GCMRL# 215 dt 369.920000 rms 0.587 0.023% neg 0 invalid 762 tFOTS 16.5590 tGradient 14.9560 tsec 32.6030
#FOTS# QuadFit found better minimum quadopt=(dt=32.368,rms=0.587309) vs oldopt=(dt=23.12,rms=0.587313)
#GCMRL# 216 dt 32.368000 rms 0.587 0.000% neg 0 invalid 762 tFOTS 17.5530 tGradient 15.0360 tsec 33.7220
#GCMRL# 217 dt 32.368000 rms 0.587 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 15.0510 tsec 16.1530
#GCMRL# 218 dt 32.368000 rms 0.587 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.9500 tsec 16.0550
setting smoothness cost coefficient to 0.031
#GCAMreg# pass 0 level1 4 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.03
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.58983
#FOTS# QuadFit found better minimum quadopt=(dt=156.781,rms=0.585278) vs oldopt=(dt=103.68,rms=0.585659)
#GCMRL# 220 dt 156.781065 rms 0.585 0.772% neg 0 invalid 762 tFOTS 15.6760 tGradient 10.3330 tsec 27.1020
#FOTS# QuadFit found better minimum quadopt=(dt=207.515,rms=0.577415) vs oldopt=(dt=103.68,rms=0.579197)
#GCMRL# 221 dt 207.515152 rms 0.577 1.344% neg 0 invalid 762 tFOTS 15.6510 tGradient 10.1570 tsec 26.9130
#FOTS# QuadFit found better minimum quadopt=(dt=64,rms=0.573321) vs oldopt=(dt=25.92,rms=0.574334)
#GCMRL# 222 dt 64.000000 rms 0.573 0.709% neg 0 invalid 762 tFOTS 15.6170 tGradient 10.3160 tsec 27.0320
#FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.570234) vs oldopt=(dt=103.68,rms=0.570602)
#GCMRL# 223 dt 145.152000 rms 0.570 0.538% neg 0 invalid 762 tFOTS 14.7740 tGradient 10.1900 tsec 26.0660
#FOTS# QuadFit found better minimum quadopt=(dt=62.208,rms=0.567687) vs oldopt=(dt=103.68,rms=0.568579)
#GCMRL# 224 dt 62.208000 rms 0.568 0.447% neg 0 invalid 762 tFOTS 13.9180 tGradient 10.1550 tsec 25.0680
#FOTS# QuadFit found better minimum quadopt=(dt=113.672,rms=0.565925) vs oldopt=(dt=103.68,rms=0.565929)
#GCMRL# 225 dt 113.671642 rms 0.566 0.310% neg 0 invalid 762 tFOTS 13.7650 tGradient 8.6350 tsec 23.4980
#FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.564047) vs oldopt=(dt=25.92,rms=0.564412)
#GCMRL# 226 dt 36.288000 rms 0.564 0.332% neg 0 invalid 762 tFOTS 15.5940 tGradient 10.2690 tsec 26.9660
#FOTS# QuadFit found better minimum quadopt=(dt=580.608,rms=0.554882) vs oldopt=(dt=414.72,rms=0.556925)
#GCMRL# 227 dt 580.608000 rms 0.555 1.625% neg 0 invalid 762 tFOTS 14.6830 tGradient 10.2380 tsec 26.0170
#FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.55334) vs oldopt=(dt=25.92,rms=0.553672)
#GCMRL# 228 dt 36.288000 rms 0.553 0.278% neg 0 invalid 762 tFOTS 15.6610 tGradient 10.4240 tsec 27.1860
#FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.551974) vs oldopt=(dt=103.68,rms=0.552189)
#GCMRL# 229 dt 145.152000 rms 0.552 0.247% neg 0 invalid 762 tFOTS 15.5470 tGradient 10.2160 tsec 26.8620
#FOTS# QuadFit found better minimum quadopt=(dt=77.848,rms=0.550892) vs oldopt=(dt=103.68,rms=0.550996)
#GCMRL# 230 dt 77.847953 rms 0.551 0.196% neg 0 invalid 762 tFOTS 16.6410 tGradient 10.3180 tsec 28.0630
#FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.550122) vs oldopt=(dt=103.68,rms=0.550172)
#GCMRL# 231 dt 82.944000 rms 0.550 0.140% neg 0 invalid 762 tFOTS 15.6060 tGradient 10.3680 tsec 27.0900
#FOTS# QuadFit found better minimum quadopt=(dt=79.4161,rms=0.549216) vs oldopt=(dt=103.68,rms=0.549297)
#GCMRL# 232 dt 79.416058 rms 0.549 0.165% neg 0 invalid 762 tFOTS 16.6330 tGradient 10.2780 tsec 28.0130
#FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.548731) vs oldopt=(dt=25.92,rms=0.548831)
#GCMRL# 233 dt 36.288000 rms 0.549 0.088% neg 0 invalid 762 tFOTS 14.7610 tGradient 10.3340 tsec 26.2050
#FOTS# QuadFit found better minimum quadopt=(dt=580.608,rms=0.545387) vs oldopt=(dt=414.72,rms=0.546)
#GCMRL# 234 dt 580.608000 rms 0.545 0.609% neg 0 invalid 762 tFOTS 14.7620 tGradient 10.1480 tsec 25.9000
#FOTS# QuadFit found better minimum quadopt=(dt=67.2593,rms=0.543626) vs oldopt=(dt=103.68,rms=0.544153)
#GCMRL# 235 dt 67.259259 rms 0.544 0.323% neg 0 invalid 762 tFOTS 13.4490 tGradient 8.5980 tsec 23.0530
#FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.543101) vs oldopt=(dt=103.68,rms=0.543167)
#GCMRL# 236 dt 82.944000 rms 0.543 0.097% neg 0 invalid 762 tFOTS 13.2850 tGradient 8.5950 tsec 22.8790
#FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.542423) vs oldopt=(dt=103.68,rms=0.542478)
#GCMRL# 237 dt 82.944000 rms 0.542 0.125% neg 0 invalid 762 tFOTS 13.3650 tGradient 8.5670 tsec 22.9430
#FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.542141) vs oldopt=(dt=25.92,rms=0.542193)
#GCMRL# 238 dt 36.288000 rms 0.542 0.052% neg 0 invalid 762 tFOTS 13.2910 tGradient 8.5820 tsec 22.8640
#GCMRL# 239 dt 1658.880000 rms 0.537 0.923% neg 0 invalid 762 tFOTS 14.8560 tGradient 8.4790 tsec 24.4270
#FOTS# QuadFit found better minimum quadopt=(dt=69.8182,rms=0.534) vs oldopt=(dt=103.68,rms=0.534844)
#GCMRL# 240 dt 69.818182 rms 0.534 0.584% neg 0 invalid 762 tFOTS 15.5830 tGradient 10.3710 tsec 27.0670
#FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.533615) vs oldopt=(dt=25.92,rms=0.533669)
#GCMRL# 241 dt 36.288000 rms 0.534 0.072% neg 0 invalid 762 tFOTS 13.2650 tGradient 9.6690 tsec 23.9320
#FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.533199) vs oldopt=(dt=103.68,rms=0.533246)
#GCMRL# 242 dt 145.152000 rms 0.533 0.078% neg 0 invalid 762 tFOTS 13.3350 tGradient 8.5990 tsec 22.9430
#FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.532682) vs oldopt=(dt=103.68,rms=0.532732)
#GCMRL# 243 dt 82.944000 rms 0.533 0.097% neg 0 invalid 762 tFOTS 13.3590 tGradient 8.5530 tsec 22.9170
#FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.532493) vs oldopt=(dt=25.92,rms=0.532519)
#GCMRL# 244 dt 36.288000 rms 0.532 0.000% neg 0 invalid 762 tFOTS 13.3280 tGradient 8.5500 tsec 22.8970
#GCMRL# 245 dt 36.288000 rms 0.532 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5260 tsec 9.5170
#GCMRL# 246 dt 36.288000 rms 0.532 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5440 tsec 9.5420
#GCMRL# 247 dt 36.288000 rms 0.532 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4200 tsec 9.4070
#GCMRL# 248 dt 36.288000 rms 0.532 0.069% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5310 tsec 9.5330
#GCMRL# 249 dt 36.288000 rms 0.531 0.079% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5550 tsec 9.5630
#GCMRL# 250 dt 36.288000 rms 0.531 0.081% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4930 tsec 9.4930
#GCMRL# 251 dt 36.288000 rms 0.530 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.7910 tsec 9.9070
#GCMRL# 252 dt 36.288000 rms 0.530 0.082% neg 0 invalid 762 tFOTS 0.0000 tGradient 10.1680 tsec 11.2670
#GCMRL# 253 dt 36.288000 rms 0.529 0.094% neg 0 invalid 762 tFOTS 0.0000 tGradient 10.3300 tsec 11.4460
#GCMRL# 254 dt 36.288000 rms 0.529 0.118% neg 0 invalid 762 tFOTS 0.0000 tGradient 10.2430 tsec 11.3500
#GCMRL# 255 dt 36.288000 rms 0.528 0.116% neg 0 invalid 762 tFOTS 0.0000 tGradient 10.1210 tsec 11.2250
#GCMRL# 256 dt 36.288000 rms 0.527 0.121% neg 0 invalid 762 tFOTS 0.0000 tGradient 10.3770 tsec 11.4960
#GCMRL# 257 dt 36.288000 rms 0.527 0.121% neg 0 invalid 762 tFOTS 0.0000 tGradient 10.3400 tsec 11.4420
#GCMRL# 258 dt 36.288000 rms 0.526 0.106% neg 0 invalid 762 tFOTS 0.0000 tGradient 10.3380 tsec 11.4380
#GCMRL# 259 dt 36.288000 rms 0.526 0.110% neg 0 invalid 762 tFOTS 0.0000 tGradient 9.7470 tsec 10.7530
#GCMRL# 260 dt 36.288000 rms 0.525 0.107% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5820 tsec 9.5800
#GCMRL# 261 dt 36.288000 rms 0.525 0.108% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6170 tsec 9.6050
#GCMRL# 262 dt 36.288000 rms 0.524 0.094% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.7010 tsec 9.7000
#GCMRL# 263 dt 36.288000 rms 0.524 0.088% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6160 tsec 9.6040
#GCMRL# 264 dt 36.288000 rms 0.523 0.098% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5940 tsec 9.5800
#GCMRL# 265 dt 36.288000 rms 0.523 0.092% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6160 tsec 9.6040
#GCMRL# 266 dt 36.288000 rms 0.522 0.090% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5420 tsec 9.5230
#GCMRL# 267 dt 36.288000 rms 0.522 0.093% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4930 tsec 9.4740
#GCMRL# 268 dt 36.288000 rms 0.521 0.096% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6280 tsec 9.6200
#GCMRL# 269 dt 36.288000 rms 0.521 0.084% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5620 tsec 9.5580
#GCMRL# 270 dt 36.288000 rms 0.520 0.075% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6820 tsec 9.6710
#GCMRL# 271 dt 36.288000 rms 0.520 0.060% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5700 tsec 9.5520
#GCMRL# 272 dt 36.288000 rms 0.520 0.076% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6420 tsec 9.6340
#GCMRL# 273 dt 36.288000 rms 0.519 0.083% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5180 tsec 9.5160
#GCMRL# 274 dt 36.288000 rms 0.519 0.080% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5720 tsec 9.5580
#GCMRL# 275 dt 36.288000 rms 0.518 0.068% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5550 tsec 9.5570
#GCMRL# 276 dt 36.288000 rms 0.518 0.059% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6930 tsec 9.6900
#GCMRL# 277 dt 36.288000 rms 0.518 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6230 tsec 9.6190
#GCMRL# 278 dt 36.288000 rms 0.518 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6480 tsec 9.6310
#GCMRL# 279 dt 36.288000 rms 0.517 0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5330 tsec 9.5200
#GCMRL# 280 dt 36.288000 rms 0.517 0.073% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6620 tsec 9.6510
#GCMRL# 281 dt 36.288000 rms 0.517 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5970 tsec 9.5860
#GCMRL# 282 dt 36.288000 rms 0.516 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6520 tsec 9.6420
#GCMRL# 283 dt 36.288000 rms 0.516 0.059% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.7060 tsec 9.7000
#GCMRL# 284 dt 36.288000 rms 0.516 0.064% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6420 tsec 9.6400
#GCMRL# 285 dt 36.288000 rms 0.515 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5930 tsec 9.5870
#GCMRL# 286 dt 36.288000 rms 0.515 0.042% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6760 tsec 9.6670
#GCMRL# 287 dt 36.288000 rms 0.515 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5510 tsec 9.5450
#GCMRL# 288 dt 36.288000 rms 0.515 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4990 tsec 9.4960
#GCMRL# 289 dt 36.288000 rms 0.515 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6190 tsec 9.6170
#GCMRL# 290 dt 36.288000 rms 0.514 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6100 tsec 9.5910
#GCMRL# 291 dt 36.288000 rms 0.514 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5300 tsec 9.5250
#GCMRL# 292 dt 36.288000 rms 0.514 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5700 tsec 9.5650
#GCMRL# 293 dt 36.288000 rms 0.514 0.047% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5080 tsec 9.5100
#GCMRL# 294 dt 36.288000 rms 0.514 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6030 tsec 9.6000
#GCMRL# 295 dt 36.288000 rms 0.513 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6530 tsec 9.6450
#GCMRL# 296 dt 36.288000 rms 0.513 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6160 tsec 9.6150
#GCMRL# 297 dt 36.288000 rms 0.513 0.040% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6240 tsec 9.6130
#GCMRL# 298 dt 36.288000 rms 0.513 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5710 tsec 9.5700
#GCMRL# 299 dt 36.288000 rms 0.513 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6240 tsec 9.6170
#GCMRL# 300 dt 36.288000 rms 0.512 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5640 tsec 9.5580
#GCMRL# 301 dt 36.288000 rms 0.512 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6700 tsec 9.6690
#GCMRL# 302 dt 36.288000 rms 0.512 0.011% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5500 tsec 9.5550
#GCMRL# 303 dt 36.288000 rms 0.512 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6650 tsec 9.6620
#GCMRL# 304 dt 36.288000 rms 0.512 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5680 tsec 9.5690
#GCMRL# 305 dt 36.288000 rms 0.512 0.024% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6780 tsec 9.6790
#GCMRL# 306 dt 36.288000 rms 0.512 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6450 tsec 9.6510
#GCMRL# 307 dt 36.288000 rms 0.511 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6300 tsec 9.6190
#GCMRL# 308 dt 36.288000 rms 0.511 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6080 tsec 9.6020
#GCMRL# 309 dt 36.288000 rms 0.511 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5920 tsec 9.5910
#GCMRL# 310 dt 36.288000 rms 0.511 0.027% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5420 tsec 9.5330
#GCMRL# 311 dt 36.288000 rms 0.511 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5310 tsec 9.5280
#GCMRL# 312 dt 36.288000 rms 0.511 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6360 tsec 9.6270
#GCMRL# 313 dt 36.288000 rms 0.511 0.006% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6270 tsec 9.6280
#GCMRL# 314 dt 36.288000 rms 0.510 0.018% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6410 tsec 9.6380
#GCMRL# 315 dt 36.288000 rms 0.510 0.012% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6160 tsec 9.6220
#FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.510125) vs oldopt=(dt=103.68,rms=0.510161)
#GCMRL# 316 dt 145.152000 rms 0.510 0.055% neg 0 invalid 762 tFOTS 13.3480 tGradient 8.4830 tsec 22.8430
#FOTS# QuadFit found better minimum quadopt=(dt=9.072,rms=0.510125) vs oldopt=(dt=6.48,rms=0.510128)
#GCMRL# 317 dt 9.072000 rms 0.510 0.000% neg 0 invalid 762 tFOTS 13.3500 tGradient 8.5390 tsec 23.8110
#GCMRL# 318 dt 9.072000 rms 0.510 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5420 tsec 9.5260
#GCAMreg# pass 0 level1 4 level2 1 tsec 1405.16 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.03
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.510875
#FOTS# QuadFit found better minimum quadopt=(dt=115.469,rms=0.508374) vs oldopt=(dt=103.68,rms=0.508378)
#GCMRL# 320 dt 115.468531 rms 0.508 0.490% neg 0 invalid 762 tFOTS 14.1900 tGradient 8.6250 tsec 23.8200
#FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.507497) vs oldopt=(dt=25.92,rms=0.507669)
#GCMRL# 321 dt 36.288000 rms 0.507 0.172% neg 0 invalid 762 tFOTS 12.5590 tGradient 8.5950 tsec 22.1520
#FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.506808) vs oldopt=(dt=103.68,rms=0.506945)
#GCMRL# 322 dt 145.152000 rms 0.507 0.136% neg 0 invalid 762 tFOTS 13.4620 tGradient 8.5890 tsec 23.0560
#FOTS# QuadFit found better minimum quadopt=(dt=82.944,rms=0.506307) vs oldopt=(dt=103.68,rms=0.506332)
#GCMRL# 323 dt 82.944000 rms 0.506 0.099% neg 0 invalid 762 tFOTS 13.4650 tGradient 8.5990 tsec 23.0600
#FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.506184) vs oldopt=(dt=25.92,rms=0.506193)
#GCMRL# 324 dt 36.288000 rms 0.506 0.000% neg 0 invalid 762 tFOTS 14.2220 tGradient 8.7150 tsec 23.9580
#GCMRL# 325 dt 36.288000 rms 0.506 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5690 tsec 9.5790
#GCMRL# 326 dt 36.288000 rms 0.506 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5100 tsec 9.5290
#GCMRL# 327 dt 36.288000 rms 0.506 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5390 tsec 9.5500
#GCMRL# 328 dt 36.288000 rms 0.505 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4710 tsec 9.4800
#GCMRL# 329 dt 36.288000 rms 0.505 0.069% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4950 tsec 9.5050
#GCMRL# 330 dt 36.288000 rms 0.505 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4840 tsec 9.4860
#GCMRL# 331 dt 36.288000 rms 0.504 0.057% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4490 tsec 9.4510
#GCMRL# 332 dt 36.288000 rms 0.504 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5380 tsec 9.5430
#GCMRL# 333 dt 36.288000 rms 0.504 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5480 tsec 9.5350
#GCMRL# 334 dt 36.288000 rms 0.504 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5200 tsec 9.5110
#GCMRL# 335 dt 36.288000 rms 0.503 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5880 tsec 9.5790
#GCMRL# 336 dt 36.288000 rms 0.503 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5120 tsec 9.5060
#GCMRL# 337 dt 36.288000 rms 0.503 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4960 tsec 9.4960
#GCMRL# 338 dt 36.288000 rms 0.503 0.049% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5620 tsec 9.5660
#GCMRL# 339 dt 36.288000 rms 0.502 0.054% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5630 tsec 9.5570
#GCMRL# 340 dt 36.288000 rms 0.502 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5670 tsec 9.5560
#GCMRL# 341 dt 36.288000 rms 0.502 0.038% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5910 tsec 9.5750
#GCMRL# 342 dt 36.288000 rms 0.502 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5860 tsec 9.5660
#GCMRL# 343 dt 36.288000 rms 0.501 0.044% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5160 tsec 9.5050
#GCMRL# 344 dt 36.288000 rms 0.501 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5080 tsec 9.5080
#GCMRL# 345 dt 36.288000 rms 0.501 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5520 tsec 9.5440
#GCMRL# 346 dt 36.288000 rms 0.501 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4970 tsec 9.5000
#GCMRL# 347 dt 36.288000 rms 0.501 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5170 tsec 9.5180
#GCMRL# 348 dt 36.288000 rms 0.500 0.043% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5030 tsec 9.5000
#GCMRL# 349 dt 36.288000 rms 0.500 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5440 tsec 9.5460
#GCMRL# 350 dt 36.288000 rms 0.500 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4700 tsec 9.4770
#GCMRL# 351 dt 36.288000 rms 0.500 0.033% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5810 tsec 9.5720
#GCMRL# 352 dt 36.288000 rms 0.500 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5410 tsec 9.5430
#GCMRL# 353 dt 36.288000 rms 0.500 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5990 tsec 9.5880
#GCMRL# 354 dt 36.288000 rms 0.500 0.032% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5170 tsec 9.5060
#GCMRL# 355 dt 36.288000 rms 0.499 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6180 tsec 9.6130
#GCMRL# 356 dt 36.288000 rms 0.499 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6410 tsec 9.6350
#GCMRL# 357 dt 36.288000 rms 0.499 0.031% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6320 tsec 9.6340
#GCMRL# 358 dt 36.288000 rms 0.499 0.037% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5710 tsec 9.5580
#GCMRL# 359 dt 36.288000 rms 0.499 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5530 tsec 9.5460
#GCMRL# 360 dt 36.288000 rms 0.499 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6700 tsec 9.6750
#GCMRL# 361 dt 36.288000 rms 0.499 0.029% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6320 tsec 9.6500
#GCMRL# 362 dt 36.288000 rms 0.498 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6400 tsec 9.6740
#GCMRL# 363 dt 36.288000 rms 0.498 0.018% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.6950 tsec 9.7150
#FOTS# QuadFit found better minimum quadopt=(dt=62.208,rms=0.498249) vs oldopt=(dt=103.68,rms=0.498259)
#GCMRL# 364 dt 62.208000 rms 0.498 0.000% neg 0 invalid 762 tFOTS 13.4550 tGradient 8.6730 tsec 23.1350
#GCMRL# 365 dt 62.208000 rms 0.498 0.003% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.7330 tsec 9.8480
#GCMRL# 366 dt 62.208000 rms 0.498 0.007% neg 0 invalid 762 tFOTS 0.0000 tGradient 10.2120 tsec 11.3230
#GCMRL# 367 dt 62.208000 rms 0.498 0.018% neg 0 invalid 762 tFOTS 0.0000 tGradient 9.8620 tsec 10.8600
setting smoothness cost coefficient to 0.118
#GCAMreg# pass 0 level1 3 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.12
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.509209
#FOTS# QuadFit found better minimum quadopt=(dt=23,rms=0.507552) vs oldopt=(dt=32,rms=0.50775)
#GCMRL# 369 dt 23.000000 rms 0.508 0.325% neg 0 invalid 762 tFOTS 13.3260 tGradient 6.3580 tsec 20.6960
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.506084) vs oldopt=(dt=32,rms=0.506238)
#GCMRL# 370 dt 44.800000 rms 0.506 0.289% neg 0 invalid 762 tFOTS 12.6350 tGradient 6.3110 tsec 19.9490
#FOTS# QuadFit found better minimum quadopt=(dt=94.6087,rms=0.503948) vs oldopt=(dt=32,rms=0.50453)
#GCMRL# 371 dt 94.608696 rms 0.504 0.422% neg 0 invalid 762 tFOTS 13.4950 tGradient 6.4020 tsec 20.9390
#FOTS# QuadFit found better minimum quadopt=(dt=35.2711,rms=0.500346) vs oldopt=(dt=32,rms=0.500346)
#GCMRL# 372 dt 35.271111 rms 0.500 0.715% neg 0 invalid 762 tFOTS 12.7110 tGradient 6.4880 tsec 20.2070
#FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.498996) vs oldopt=(dt=32,rms=0.499012)
#GCMRL# 373 dt 38.400000 rms 0.499 0.270% neg 0 invalid 762 tFOTS 12.6120 tGradient 6.3770 tsec 19.9840
#FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.497041) vs oldopt=(dt=32,rms=0.497145)
#GCMRL# 374 dt 38.400000 rms 0.497 0.392% neg 0 invalid 762 tFOTS 12.5990 tGradient 6.3060 tsec 19.9170
#FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.496371) vs oldopt=(dt=32,rms=0.496441)
#GCMRL# 375 dt 25.600000 rms 0.496 0.135% neg 0 invalid 762 tFOTS 12.6790 tGradient 6.2870 tsec 19.9750
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.49469) vs oldopt=(dt=32,rms=0.495015)
#GCMRL# 376 dt 44.800000 rms 0.495 0.339% neg 0 invalid 762 tFOTS 12.5870 tGradient 6.3210 tsec 19.8960
#FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.494218) vs oldopt=(dt=32,rms=0.494242)
#GCMRL# 377 dt 25.600000 rms 0.494 0.095% neg 0 invalid 762 tFOTS 12.6600 tGradient 6.3310 tsec 19.9840
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.49293) vs oldopt=(dt=32,rms=0.493188)
#GCMRL# 378 dt 44.800000 rms 0.493 0.261% neg 0 invalid 762 tFOTS 12.5920 tGradient 6.2990 tsec 19.9000
#FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.492541) vs oldopt=(dt=32,rms=0.492576)
#GCMRL# 379 dt 25.600000 rms 0.493 0.079% neg 0 invalid 762 tFOTS 12.6880 tGradient 6.3130 tsec 20.0090
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.491364) vs oldopt=(dt=32,rms=0.491524)
#GCMRL# 380 dt 44.800000 rms 0.491 0.239% neg 0 invalid 762 tFOTS 13.4540 tGradient 6.3320 tsec 20.7800
#FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.491054) vs oldopt=(dt=8,rms=0.491117)
#GCMRL# 381 dt 11.200000 rms 0.491 0.063% neg 0 invalid 762 tFOTS 12.5800 tGradient 6.2720 tsec 19.8480
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.490527) vs oldopt=(dt=32,rms=0.490592)
#GCMRL# 382 dt 44.800000 rms 0.491 0.107% neg 0 invalid 762 tFOTS 12.6480 tGradient 6.3150 tsec 19.9680
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.489446) vs oldopt=(dt=32,rms=0.489571)
#GCMRL# 383 dt 44.800000 rms 0.489 0.221% neg 0 invalid 762 tFOTS 13.4660 tGradient 6.2830 tsec 20.7640
#FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.489184) vs oldopt=(dt=8,rms=0.489243)
#GCMRL# 384 dt 11.200000 rms 0.489 0.053% neg 0 invalid 762 tFOTS 12.6160 tGradient 6.2450 tsec 19.8580
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.488658) vs oldopt=(dt=32,rms=0.48874)
#GCMRL# 385 dt 44.800000 rms 0.489 0.108% neg 0 invalid 762 tFOTS 12.6380 tGradient 6.2070 tsec 19.8450
#FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.487978) vs oldopt=(dt=32,rms=0.487989)
#GCMRL# 386 dt 38.400000 rms 0.488 0.139% neg 0 invalid 762 tFOTS 13.3890 tGradient 6.2840 tsec 20.6820
#FOTS# QuadFit found better minimum quadopt=(dt=19.2,rms=0.487699) vs oldopt=(dt=32,rms=0.487782)
#GCMRL# 387 dt 19.200000 rms 0.488 0.057% neg 0 invalid 762 tFOTS 12.6100 tGradient 6.1790 tsec 19.7910
#FOTS# QuadFit found better minimum quadopt=(dt=409.6,rms=0.485709) vs oldopt=(dt=512,rms=0.485764)
#GCMRL# 388 dt 409.600000 rms 0.486 0.408% neg 0 invalid 762 tFOTS 13.4830 tGradient 6.1350 tsec 20.6120
#GCMRL# 389 dt 32.000000 rms 0.484 0.317% neg 0 invalid 762 tFOTS 12.6890 tGradient 6.6490 tsec 20.3420
#FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.483185) vs oldopt=(dt=32,rms=0.483254)
#GCMRL# 390 dt 38.400000 rms 0.483 0.204% neg 0 invalid 762 tFOTS 13.3880 tGradient 6.4900 tsec 20.8720
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.482337) vs oldopt=(dt=32,rms=0.482434)
#GCMRL# 391 dt 44.800000 rms 0.482 0.176% neg 0 invalid 762 tFOTS 12.6820 tGradient 6.1370 tsec 19.8130
#FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.481884) vs oldopt=(dt=32,rms=0.481886)
#GCMRL# 392 dt 25.600000 rms 0.482 0.094% neg 0 invalid 762 tFOTS 12.6640 tGradient 6.4310 tsec 20.0970
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.481628) vs oldopt=(dt=32,rms=0.481662)
#GCMRL# 393 dt 44.800000 rms 0.482 0.053% neg 0 invalid 762 tFOTS 12.5780 tGradient 6.1320 tsec 19.6980
#GCMRL# 394 dt 32.000000 rms 0.481 0.053% neg 0 invalid 762 tFOTS 13.3970 tGradient 6.1500 tsec 20.5400
#FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.481192) vs oldopt=(dt=32,rms=0.481214)
#GCMRL# 395 dt 25.600000 rms 0.481 0.000% neg 0 invalid 762 tFOTS 13.3880 tGradient 6.1770 tsec 20.5780
#GCMRL# 396 dt 25.600000 rms 0.481 0.015% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.3720 tsec 7.3700
#GCMRL# 397 dt 25.600000 rms 0.481 0.073% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.1420 tsec 7.1380
#GCMRL# 398 dt 25.600000 rms 0.480 0.098% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.1560 tsec 7.1500
#GCMRL# 399 dt 25.600000 rms 0.480 0.081% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.1920 tsec 7.1800
#GCMRL# 400 dt 25.600000 rms 0.479 0.108% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.2330 tsec 7.2310
#GCMRL# 401 dt 25.600000 rms 0.479 0.116% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.4780 tsec 7.4790
#GCMRL# 402 dt 25.600000 rms 0.478 0.131% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.2340 tsec 7.2290
#GCMRL# 403 dt 25.600000 rms 0.477 0.155% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.1760 tsec 7.1880
#GCMRL# 404 dt 25.600000 rms 0.477 0.112% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.2580 tsec 7.2620
#GCMRL# 405 dt 25.600000 rms 0.476 0.146% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.4250 tsec 7.4210
#GCMRL# 406 dt 25.600000 rms 0.476 0.091% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.3020 tsec 7.3060
#GCMRL# 407 dt 25.600000 rms 0.475 0.081% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.2750 tsec 7.2710
#GCMRL# 408 dt 25.600000 rms 0.475 0.115% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.2220 tsec 7.2250
#GCMRL# 409 dt 25.600000 rms 0.474 0.142% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.2150 tsec 7.2070
#GCMRL# 410 dt 25.600000 rms 0.474 0.130% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0530 tsec 7.0580
#GCMRL# 411 dt 25.600000 rms 0.473 0.087% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.1050 tsec 7.1070
#GCMRL# 412 dt 25.600000 rms 0.473 -0.021% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.3570 tsec 8.2920
#FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.473133) vs oldopt=(dt=8,rms=0.47314)
#GCMRL# 413 dt 11.200000 rms 0.473 0.009% neg 0 invalid 762 tFOTS 13.4040 tGradient 6.3660 tsec 20.7670
#GCMRL# 414 dt 32.000000 rms 0.473 0.029% neg 0 invalid 762 tFOTS 12.5730 tGradient 6.2210 tsec 19.7840
#FOTS# QuadFit found better minimum quadopt=(dt=19.2,rms=0.472969) vs oldopt=(dt=32,rms=0.472976)
#GCAMreg# pass 0 level1 3 level2 1 tsec 742.447 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.12
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.473628
#FOTS# QuadFit found better minimum quadopt=(dt=80.8989,rms=0.469444) vs oldopt=(dt=32,rms=0.470339)
#GCMRL# 416 dt 80.898876 rms 0.469 0.883% neg 0 invalid 762 tFOTS 15.6300 tGradient 7.8800 tsec 24.6220
#FOTS# QuadFit found better minimum quadopt=(dt=24.1717,rms=0.468538) vs oldopt=(dt=32,rms=0.46869)
#GCMRL# 417 dt 24.171674 rms 0.469 0.193% neg 0 invalid 762 tFOTS 14.7480 tGradient 7.7850 tsec 23.6390
#FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.468213) vs oldopt=(dt=32,rms=0.468216)
#GCMRL# 418 dt 38.400000 rms 0.468 0.069% neg 0 invalid 762 tFOTS 14.7020 tGradient 7.7630 tsec 23.5750
#FOTS# QuadFit found better minimum quadopt=(dt=19.2,rms=0.468077) vs oldopt=(dt=32,rms=0.468108)
#GCMRL# 419 dt 19.200000 rms 0.468 0.000% neg 0 invalid 762 tFOTS 14.7840 tGradient 7.8010 tsec 23.7270
#GCMRL# 420 dt 19.200000 rms 0.468 0.066% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.7330 tsec 8.8450
#GCMRL# 421 dt 19.200000 rms 0.468 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.9000 tsec 9.0160
#GCMRL# 422 dt 19.200000 rms 0.467 0.076% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.9650 tsec 9.0730
#GCMRL# 423 dt 19.200000 rms 0.467 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.9330 tsec 9.0600
#GCMRL# 424 dt 19.200000 rms 0.467 0.086% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.7690 tsec 8.8720
#GCMRL# 425 dt 19.200000 rms 0.466 0.081% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.6840 tsec 8.7920
#GCMRL# 426 dt 19.200000 rms 0.466 0.068% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.7640 tsec 8.8690
#GCMRL# 427 dt 19.200000 rms 0.466 0.064% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.7910 tsec 8.8950
#GCMRL# 428 dt 19.200000 rms 0.465 0.080% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.7430 tsec 8.8580
#GCMRL# 429 dt 19.200000 rms 0.465 0.065% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.7860 tsec 8.9100
#GCMRL# 430 dt 19.200000 rms 0.465 0.059% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.7290 tsec 8.8380
#GCMRL# 431 dt 19.200000 rms 0.464 0.035% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.6370 tsec 8.7530
#GCMRL# 432 dt 19.200000 rms 0.464 0.063% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.7550 tsec 8.8720
#GCMRL# 433 dt 19.200000 rms 0.464 0.053% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.8360 tsec 8.9490
#GCMRL# 434 dt 19.200000 rms 0.464 0.050% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.0030 tsec 9.1110
#GCMRL# 435 dt 19.200000 rms 0.463 0.064% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.9160 tsec 9.0310
#GCMRL# 436 dt 19.200000 rms 0.463 0.045% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.8200 tsec 8.9330
#GCMRL# 437 dt 19.200000 rms 0.463 0.018% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.9250 tsec 9.0430
#GCMRL# 438 dt 19.200000 rms 0.463 0.048% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.5630 tsec 8.6760
#GCMRL# 439 dt 19.200000 rms 0.463 0.060% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.7170 tsec 8.8210
#GCMRL# 440 dt 19.200000 rms 0.463 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.6670 tsec 8.7690
#GCMRL# 441 dt 19.200000 rms 0.462 0.034% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.8000 tsec 8.8990
#GCMRL# 442 dt 19.200000 rms 0.462 0.055% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.8120 tsec 8.9080
#GCMRL# 443 dt 19.200000 rms 0.462 -0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.7960 tsec 9.9450
#FOTS# QuadFit found better minimum quadopt=(dt=38.4,rms=0.462012) vs oldopt=(dt=32,rms=0.462015)
#GCMRL# 444 dt 38.400000 rms 0.462 0.023% neg 0 invalid 762 tFOTS 14.7400 tGradient 7.7910 tsec 23.6270
#FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.461955) vs oldopt=(dt=8,rms=0.46197)
setting smoothness cost coefficient to 0.400
#GCAMreg# pass 0 level1 2 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.40
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.485295
#GCMRL# 446 dt 0.000000 rms 0.485 0.128% neg 0 invalid 762 tFOTS 14.7530 tGradient 6.2980 tsec 22.1560
#GCMRL# 447 dt 0.150000 rms 0.485 0.000% neg 0 invalid 762 tFOTS 14.8050 tGradient 6.2990 tsec 23.2580
#GCAMreg# pass 0 level1 2 level2 1 tsec 63.552 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.40
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.485295
#GCMRL# 449 dt 0.000000 rms 0.485 0.128% neg 0 invalid 762 tFOTS 14.7400 tGradient 6.2830 tsec 22.1230
#GCMRL# 450 dt 0.150000 rms 0.485 0.000% neg 0 invalid 762 tFOTS 14.7800 tGradient 6.2950 tsec 23.2270
setting smoothness cost coefficient to 1.000
#GCAMreg# pass 0 level1 1 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=1.00
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.530295
#GCMRL# 452 dt 1.280000 rms 0.527 0.634% neg 0 invalid 762 tFOTS 14.7650 tGradient 4.7540 tsec 20.6330
#FOTS# QuadFit found better minimum quadopt=(dt=0.064,rms=0.526903) vs oldopt=(dt=0.08,rms=0.526903)
#GCMRL# 453 dt 0.064000 rms 0.527 0.000% neg 0 invalid 762 tFOTS 14.8510 tGradient 4.7980 tsec 20.7810
#GCAMreg# pass 0 level1 1 level2 1 tsec 58.174 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=1.00
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.527486
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.526684) vs oldopt=(dt=0.32,rms=0.526705)
#GCMRL# 455 dt 0.448000 rms 0.527 0.152% neg 0 invalid 762 tFOTS 14.7500 tGradient 4.9010 tsec 20.7720
#GCMRL# 456 dt 0.080000 rms 0.527 0.000% neg 0 invalid 762 tFOTS 14.8190 tGradient 4.8630 tsec 20.8110
#GCMRL# 457 dt 0.080000 rms 0.527 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8390 tsec 5.9600
resetting metric properties...
setting smoothness cost coefficient to 2.000
#GCAMreg# pass 0 level1 0 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.00
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=2.0,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.470002
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.456551) vs oldopt=(dt=0.32,rms=0.46005)
#GCMRL# 459 dt 0.448000 rms 0.457 2.862% neg 0 invalid 762 tFOTS 13.9000 tGradient 3.4080 tsec 18.4340
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.454157) vs oldopt=(dt=0.32,rms=0.45483)
#GCMRL# 460 dt 0.448000 rms 0.454 0.524% neg 0 invalid 762 tFOTS 13.8370 tGradient 3.4540 tsec 18.4020
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.452642) vs oldopt=(dt=0.32,rms=0.453058)
#GCMRL# 461 dt 0.448000 rms 0.453 0.334% neg 0 invalid 762 tFOTS 11.8200 tGradient 2.9670 tsec 15.7930
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.45174) vs oldopt=(dt=0.32,rms=0.451997)
#GCMRL# 462 dt 0.448000 rms 0.452 0.199% neg 0 invalid 762 tFOTS 11.8500 tGradient 2.6680 tsec 15.5200
#FOTS# QuadFit found better minimum quadopt=(dt=0.458333,rms=0.45102) vs oldopt=(dt=0.32,rms=0.45123)
#GCMRL# 463 dt 0.458333 rms 0.451 0.159% neg 0 invalid 762 tFOTS 11.8260 tGradient 2.6800 tsec 15.5060
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.450522) vs oldopt=(dt=0.32,rms=0.450659)
#GCMRL# 464 dt 0.448000 rms 0.451 0.111% neg 0 invalid 762 tFOTS 11.8470 tGradient 2.7640 tsec 15.6040
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.450085) vs oldopt=(dt=0.32,rms=0.450201)
#GCMRL# 465 dt 0.448000 rms 0.450 0.097% neg 0 invalid 762 tFOTS 11.7900 tGradient 2.6500 tsec 15.4330
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.449786) vs oldopt=(dt=0.32,rms=0.44987)
#GCMRL# 466 dt 0.448000 rms 0.450 0.066% neg 0 invalid 762 tFOTS 11.7750 tGradient 2.7600 tsec 15.5330
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.449478) vs oldopt=(dt=0.32,rms=0.449555)
#GCMRL# 467 dt 0.448000 rms 0.449 0.068% neg 0 invalid 762 tFOTS 11.8330 tGradient 2.6700 tsec 15.5010
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.449275) vs oldopt=(dt=0.32,rms=0.449329)
#GCMRL# 468 dt 0.448000 rms 0.449 0.000% neg 0 invalid 762 tFOTS 11.8520 tGradient 2.7760 tsec 15.6610
#GCMRL# 469 dt 0.448000 rms 0.449 0.051% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6460 tsec 3.6480
#GCMRL# 470 dt 0.448000 rms 0.449 0.074% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7640 tsec 3.7690
#GCMRL# 471 dt 0.448000 rms 0.448 0.094% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7230 tsec 3.7190
#GCMRL# 472 dt 0.448000 rms 0.448 0.096% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6180 tsec 3.6170
#GCMRL# 473 dt 0.448000 rms 0.447 0.085% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6550 tsec 3.6500
#GCMRL# 474 dt 0.448000 rms 0.447 0.070% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6880 tsec 3.6830
#GCMRL# 475 dt 0.448000 rms 0.447 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6870 tsec 3.6910
#GCMRL# 476 dt 0.448000 rms 0.447 0.030% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7190 tsec 3.7360
#GCMRL# 477 dt 0.448000 rms 0.447 -0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6750 tsec 5.0330
#FOTS# QuadFit found better minimum quadopt=(dt=0.112,rms=0.446836) vs oldopt=(dt=0.08,rms=0.446836)
#GCMRL# 478 dt 0.112000 rms 0.447 0.001% neg 0 invalid 762 tFOTS 11.7340 tGradient 2.7220 tsec 15.4520
#FOTS# QuadFit found better minimum quadopt=(dt=0.064,rms=0.44684) vs oldopt=(dt=0.08,rms=0.44684)
#GCAMreg# pass 0 level1 0 level2 1 tsec 237.373 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.00
tol=5.00e-02, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=0.5,type=2, relabel=0, neg=no
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.447587
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.443597) vs oldopt=(dt=0.32,rms=0.444475)
#GCMRL# 480 dt 0.448000 rms 0.444 0.891% neg 0 invalid 762 tFOTS 11.6750 tGradient 2.7090 tsec 15.3690
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.443144) vs oldopt=(dt=0.32,rms=0.443265)
#GCMRL# 481 dt 0.448000 rms 0.443 0.102% neg 0 invalid 762 tFOTS 11.7260 tGradient 2.6500 tsec 15.3640
#FOTS# QuadFit found better minimum quadopt=(dt=0.448,rms=0.442984) vs oldopt=(dt=0.32,rms=0.443023)
#GCMRL# 482 dt 0.448000 rms 0.443 0.000% neg 0 invalid 762 tFOTS 11.7060 tGradient 2.6190 tsec 15.3370
#GCMRL# 483 dt 0.448000 rms 0.443 0.006% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6390 tsec 3.6380
#GCMRL# 484 dt 0.448000 rms 0.443 0.018% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.7150 tsec 3.7200
#GCMRL# 485 dt 0.448000 rms 0.443 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.6730 tsec 4.0920
GCAMregister done in 98.3608 min
********************* ALLOWING NEGATIVE NODES IN DEFORMATION********************************
noneg post
Starting GCAMregister()
label assignment complete, 0 changed (0.00%)
npasses = 1, nlevels = 6
#pass# 1 of 1 ************************
enabling zero nodes
setting smoothness cost coefficient to 0.008
#GCAMreg# pass 0 level1 5 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.01
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.441636
#GCMRL# 487 dt 0.000000 rms 0.441 0.164% neg 0 invalid 762 tFOTS 14.2670 tGradient 12.8830 tsec 28.1450
#GCAMreg# pass 0 level1 5 level2 1 tsec 65.791 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.01
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.441636
#FOTS# QuadFit found better minimum quadopt=(dt=295.936,rms=0.440537) vs oldopt=(dt=369.92,rms=0.440559)
#GCMRL# 489 dt 295.936000 rms 0.441 0.249% neg 0 invalid 762 tFOTS 14.1710 tGradient 12.8790 tsec 28.0430
#FOTS# QuadFit found better minimum quadopt=(dt=32.368,rms=0.440489) vs oldopt=(dt=23.12,rms=0.440497)
#GCMRL# 490 dt 32.368000 rms 0.440 0.000% neg 0 invalid 762 tFOTS 14.9120 tGradient 12.9120 tsec 28.8390
#GCMRL# 491 dt 32.368000 rms 0.440 0.007% neg 0 invalid 762 tFOTS 0.0000 tGradient 12.8110 tsec 13.8080
#GCMRL# 492 dt 32.368000 rms 0.440 0.005% neg 0 invalid 762 tFOTS 0.0000 tGradient 12.9780 tsec 13.9780
#GCMRL# 493 dt 32.368000 rms 0.440 0.007% neg 0 invalid 762 tFOTS 0.0000 tGradient 13.2270 tsec 14.3310
#GCMRL# 494 dt 32.368000 rms 0.440 0.009% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.5970 tsec 15.7010
#GCMRL# 495 dt 32.368000 rms 0.440 0.017% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.4980 tsec 15.5970
#GCMRL# 496 dt 32.368000 rms 0.440 0.019% neg 0 invalid 762 tFOTS 0.0000 tGradient 14.6190 tsec 15.7320
setting smoothness cost coefficient to 0.031
#GCAMreg# pass 0 level1 4 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.03
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.440894
#FOTS# QuadFit found better minimum quadopt=(dt=9.072,rms=0.440119) vs oldopt=(dt=6.48,rms=0.440123)
#GCMRL# 498 dt 9.072000 rms 0.440 0.176% neg 0 invalid 762 tFOTS 14.8520 tGradient 8.4590 tsec 24.3000
#FOTS# QuadFit found better minimum quadopt=(dt=9.072,rms=0.440099) vs oldopt=(dt=6.48,rms=0.440104)
#GCMRL# 499 dt 9.072000 rms 0.440 0.000% neg 0 invalid 762 tFOTS 14.9540 tGradient 8.4290 tsec 24.4110
#GCMRL# 500 dt 9.072000 rms 0.440 0.006% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.5200 tsec 9.5280
#GCAMreg# pass 0 level1 4 level2 1 tsec 77.563 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.03
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.440843
#FOTS# QuadFit found better minimum quadopt=(dt=145.152,rms=0.437637) vs oldopt=(dt=103.68,rms=0.438117)
iter 0, gcam->neg = 1
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 502 dt 145.152000 rms 0.438 0.727% neg 0 invalid 762 tFOTS 14.2710 tGradient 8.3070 tsec 24.8250
#FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.437455) vs oldopt=(dt=25.92,rms=0.437473)
#GCMRL# 503 dt 36.288000 rms 0.437 0.000% neg 0 invalid 762 tFOTS 14.2440 tGradient 8.3150 tsec 23.5790
#GCMRL# 504 dt 36.288000 rms 0.437 0.041% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4490 tsec 9.4400
#GCMRL# 505 dt 36.288000 rms 0.437 0.068% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4280 tsec 9.4160
#GCMRL# 506 dt 36.288000 rms 0.437 0.096% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.3380 tsec 9.3390
iter 0, gcam->neg = 1
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 507 dt 36.288000 rms 0.436 0.128% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.3840 tsec 10.6210
iter 0, gcam->neg = 1
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 508 dt 36.288000 rms 0.435 0.119% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4180 tsec 10.6620
#GCMRL# 509 dt 36.288000 rms 0.435 0.123% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.3590 tsec 9.3500
iter 0, gcam->neg = 2
after 7 iterations, nbhd size=1, neg = 0
#GCMRL# 510 dt 36.288000 rms 0.435 0.093% neg 0 invalid 762 tFOTS 0.0000 tGradient 8.4250 tsec 14.7140
#FOTS# QuadFit found better minimum quadopt=(dt=36.288,rms=0.434557) vs oldopt=(dt=25.92,rms=0.43456)
setting smoothness cost coefficient to 0.118
#GCAMreg# pass 0 level1 3 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.12
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.435641
#FOTS# QuadFit found better minimum quadopt=(dt=30.2959,rms=0.433782) vs oldopt=(dt=32,rms=0.433795)
iter 0, gcam->neg = 4
after 7 iterations, nbhd size=1, neg = 0
#GCMRL# 512 dt 30.295858 rms 0.434 0.420% neg 0 invalid 762 tFOTS 14.9780 tGradient 6.0870 tsec 27.3030
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.432413) vs oldopt=(dt=32,rms=0.432535)
iter 0, gcam->neg = 4
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 513 dt 44.800000 rms 0.432 0.323% neg 0 invalid 762 tFOTS 15.0110 tGradient 5.9390 tsec 23.1790
#FOTS# QuadFit found better minimum quadopt=(dt=22.2762,rms=0.431624) vs oldopt=(dt=32,rms=0.431815)
#GCMRL# 514 dt 22.276243 rms 0.432 0.000% neg 0 invalid 762 tFOTS 14.9920 tGradient 6.0550 tsec 22.0560
#GCMRL# 515 dt 22.276243 rms 0.431 0.138% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0320 tsec 7.0290
iter 0, gcam->neg = 2
after 7 iterations, nbhd size=1, neg = 0
#GCMRL# 516 dt 22.276243 rms 0.430 0.194% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0610 tsec 12.3080
#GCMRL# 517 dt 22.276243 rms 0.429 0.192% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.3680 tsec 7.3420
iter 0, gcam->neg = 3
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 518 dt 22.276243 rms 0.429 0.199% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0920 tsec 8.3090
iter 0, gcam->neg = 1
after 6 iterations, nbhd size=1, neg = 0
#GCMRL# 519 dt 22.276243 rms 0.428 0.141% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0220 tsec 11.7510
iter 0, gcam->neg = 6
after 10 iterations, nbhd size=1, neg = 0
#GCMRL# 520 dt 22.276243 rms 0.427 0.176% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0360 tsec 14.0540
#GCMRL# 521 dt 22.276243 rms 0.427 0.143% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0350 tsec 7.0280
iter 0, gcam->neg = 6
after 11 iterations, nbhd size=1, neg = 0
#GCMRL# 522 dt 22.276243 rms 0.426 0.088% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0500 tsec 14.5750
iter 0, gcam->neg = 2
after 2 iterations, nbhd size=0, neg = 0
#GCMRL# 523 dt 22.276243 rms 0.426 0.141% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.1190 tsec 9.4860
#GCMRL# 524 dt 22.276243 rms 0.425 0.130% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0950 tsec 7.0910
iter 0, gcam->neg = 1
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 525 dt 22.276243 rms 0.425 0.100% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.1510 tsec 8.3860
iter 0, gcam->neg = 3
after 8 iterations, nbhd size=1, neg = 0
#GCMRL# 526 dt 22.276243 rms 0.424 0.058% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.2000 tsec 13.4850
#FOTS# QuadFit found better minimum quadopt=(dt=25.6,rms=0.42397) vs oldopt=(dt=32,rms=0.423973)
#GCMRL# 527 dt 25.600000 rms 0.424 0.089% neg 0 invalid 762 tFOTS 14.8820 tGradient 6.2230 tsec 22.0990
#FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.423915) vs oldopt=(dt=8,rms=0.423917)
#GCMRL# 528 dt 11.200000 rms 0.424 0.000% neg 0 invalid 762 tFOTS 14.9390 tGradient 6.0920 tsec 22.0410
#GCMRL# 529 dt 11.200000 rms 0.424 0.023% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0940 tsec 7.0900
iter 0, gcam->neg = 1
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 530 dt 11.200000 rms 0.424 0.026% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0800 tsec 8.3120
iter 0, gcam->neg = 1
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 531 dt 11.200000 rms 0.424 0.025% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0680 tsec 8.2800
iter 0, gcam->neg = 1
after 6 iterations, nbhd size=1, neg = 0
#GCAMreg# pass 0 level1 3 level2 1 tsec 281.365 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.12
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.42429
#FOTS# QuadFit found better minimum quadopt=(dt=66.3358,rms=0.419519) vs oldopt=(dt=32,rms=0.420377)
#GCMRL# 533 dt 66.335766 rms 0.420 1.124% neg 0 invalid 762 tFOTS 17.6110 tGradient 7.2310 tsec 25.9370
#FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.418766) vs oldopt=(dt=8,rms=0.418914)
iter 0, gcam->neg = 1
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 534 dt 11.200000 rms 0.419 0.000% neg 0 invalid 762 tFOTS 17.5110 tGradient 7.6420 tsec 29.2620
#GCMRL# 535 dt 11.200000 rms 0.419 0.061% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.6120 tsec 8.7240
#GCMRL# 536 dt 11.200000 rms 0.418 0.057% neg 0 invalid 762 tFOTS 0.0000 tGradient 7.5360 tsec 8.5360
#GCMRL# 537 dt 11.200000 rms 0.418 0.056% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.0950 tsec 7.1160
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.417494) vs oldopt=(dt=32,rms=0.417535)
#GCMRL# 538 dt 44.800000 rms 0.417 0.129% neg 0 invalid 762 tFOTS 14.9210 tGradient 6.0900 tsec 22.0020
#FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.417241) vs oldopt=(dt=8,rms=0.417279)
setting smoothness cost coefficient to 0.400
#GCAMreg# pass 0 level1 2 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.40
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.423798
#FOTS# QuadFit found better minimum quadopt=(dt=2.304,rms=0.422956) vs oldopt=(dt=2.88,rms=0.422956)
#GCMRL# 540 dt 2.304000 rms 0.423 0.199% neg 0 invalid 762 tFOTS 15.0290 tGradient 4.8060 tsec 20.8300
#FOTS# QuadFit found better minimum quadopt=(dt=1.008,rms=0.422955) vs oldopt=(dt=0.72,rms=0.422958)
#GCMRL# 541 dt 1.008000 rms 0.423 0.000% neg 0 invalid 762 tFOTS 14.9530 tGradient 4.8470 tsec 20.8080
#GCMRL# 542 dt 1.008000 rms 0.423 0.003% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.9090 tsec 5.9000
#GCAMreg# pass 0 level1 2 level2 1 tsec 63.023 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=0.40
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.423656
#FOTS# QuadFit found better minimum quadopt=(dt=8.67257,rms=0.421995) vs oldopt=(dt=11.52,rms=0.422103)
#GCMRL# 544 dt 8.672566 rms 0.422 0.392% neg 0 invalid 762 tFOTS 14.9780 tGradient 4.7960 tsec 20.7670
#FOTS# QuadFit found better minimum quadopt=(dt=13.9429,rms=0.421202) vs oldopt=(dt=11.52,rms=0.421229)
#GCMRL# 545 dt 13.942857 rms 0.421 0.000% neg 0 invalid 762 tFOTS 14.9540 tGradient 4.6860 tsec 20.6600
#GCMRL# 546 dt 13.942857 rms 0.420 0.273% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.7520 tsec 5.7470
iter 0, gcam->neg = 2
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 547 dt 13.942857 rms 0.419 0.356% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8320 tsec 7.0470
iter 0, gcam->neg = 2
after 1 iterations, nbhd size=0, neg = 0
#GCMRL# 548 dt 13.942857 rms 0.417 0.301% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8880 tsec 7.6960
iter 0, gcam->neg = 2
after 1 iterations, nbhd size=0, neg = 0
#GCMRL# 549 dt 13.942857 rms 0.415 0.468% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8170 tsec 7.7150
iter 0, gcam->neg = 2
after 3 iterations, nbhd size=0, neg = 0
#GCMRL# 550 dt 13.942857 rms 0.413 0.469% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8800 tsec 8.8520
iter 0, gcam->neg = 6
after 9 iterations, nbhd size=1, neg = 0
#GCMRL# 551 dt 13.942857 rms 0.412 0.342% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8370 tsec 12.2790
iter 0, gcam->neg = 3
after 1 iterations, nbhd size=0, neg = 0
#GCMRL# 552 dt 13.942857 rms 0.411 0.129% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8110 tsec 7.6300
iter 0, gcam->neg = 2
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 553 dt 13.942857 rms 0.411 -0.010% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.8510 tsec 8.0130
#FOTS# QuadFit found better minimum quadopt=(dt=4.032,rms=0.411389) vs oldopt=(dt=2.88,rms=0.411395)
iter 0, gcam->neg = 1
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 554 dt 4.032000 rms 0.411 0.012% neg 0 invalid 762 tFOTS 15.0030 tGradient 4.8440 tsec 22.0950
#FOTS# QuadFit found better minimum quadopt=(dt=0.0016875,rms=0.411365) vs oldopt=(dt=0.0028125,rms=0.411365)
setting smoothness cost coefficient to 1.000
#GCAMreg# pass 0 level1 1 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=1.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.417255
#GCMRL# 556 dt 0.000000 rms 0.416 0.193% neg 0 invalid 762 tFOTS 14.3560 tGradient 3.2650 tsec 18.6190
#GCAMreg# pass 0 level1 1 level2 1 tsec 46.647 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=1.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.417255
#GCMRL# 558 dt 0.000000 rms 0.416 0.193% neg 0 invalid 762 tFOTS 14.2540 tGradient 3.2390 tsec 18.4880
resetting metric properties...
setting smoothness cost coefficient to 2.000
#GCAMreg# pass 0 level1 0 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.408762
#FOTS# QuadFit found better minimum quadopt=(dt=1.88843,rms=0.385579) vs oldopt=(dt=1.28,rms=0.388123)
iter 0, gcam->neg = 624
after 13 iterations, nbhd size=1, neg = 0
#GCMRL# 560 dt 1.888427 rms 0.386 5.618% neg 0 invalid 762 tFOTS 15.0570 tGradient 2.6290 tsec 27.5280
#GCMRL# 561 dt 0.000013 rms 0.386 0.000% neg 0 invalid 762 tFOTS 19.8990 tGradient 2.8220 tsec 23.8520
#GCAMreg# pass 0 level1 0 level2 1 tsec 65.756 sigma 0.5
l_jacobian=1.00 l_label=1.00 l_log_likelihood=0.20 l_smoothness=2.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.386748
#FOTS# QuadFit found better minimum quadopt=(dt=0.028,rms=0.385744) vs oldopt=(dt=0.02,rms=0.385752)
#GCMRL# 563 dt 0.028000 rms 0.386 0.259% neg 0 invalid 762 tFOTS 17.6780 tGradient 3.4960 tsec 22.2760
#FOTS# QuadFit found better minimum quadopt=(dt=0.007,rms=0.385783) vs oldopt=(dt=0.005,rms=0.385784)
#GCMRL# 564 dt 0.007000 rms 0.386 0.000% neg 0 invalid 762 tFOTS 17.7370 tGradient 3.5610 tsec 23.4560
#GCMRL# 565 dt 0.007000 rms 0.386 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.6010 tsec 4.7050
#GCMRL# 566 dt 0.007000 rms 0.386 0.006% neg 0 invalid 762 tFOTS 0.0000 tGradient 3.4570 tsec 4.5570
label assignment complete, 0 changed (0.00%)
GCAMregister done in 21.6806 min
Starting GCAMcomputeMaxPriorLabels()
Morphing with label term set to 0 *******************************
Starting GCAMregister()
label assignment complete, 0 changed (0.00%)
npasses = 1, nlevels = 6
#pass# 1 of 1 ************************
enabling zero nodes
setting smoothness cost coefficient to 0.008
#GCAMreg# pass 0 level1 5 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.01
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.375653
#GCAMreg# pass 0 level1 5 level2 1 tsec 41.725 sigma 0.5
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.01
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=256, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.375653
#FOTS# QuadFit found better minimum quadopt=(dt=8.092,rms=0.375651) vs oldopt=(dt=5.78,rms=0.375651)
#GCMRL# 569 dt 8.092000 rms 0.376 0.001% neg 0 invalid 762 tFOTS 14.6390 tGradient 13.2170 tsec 28.8410
#FOTS# QuadFit found better minimum quadopt=(dt=2.023,rms=0.375651) vs oldopt=(dt=1.445,rms=0.375651)
#GCMRL# 570 dt 2.023000 rms 0.376 0.000% neg 0 invalid 762 tFOTS 14.6550 tGradient 12.4410 tsec 28.1080
#GCMRL# 571 dt 2.023000 rms 0.376 0.000% neg 0 invalid 762 tFOTS 0.0000 tGradient 12.5240 tsec 13.5120
setting smoothness cost coefficient to 0.031
#GCAMreg# pass 0 level1 4 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.03
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.37578
#FOTS# QuadFit found better minimum quadopt=(dt=2.268,rms=0.375776) vs oldopt=(dt=1.62,rms=0.375777)
#GCMRL# 573 dt 2.268000 rms 0.376 0.001% neg 0 invalid 762 tFOTS 15.5660 tGradient 7.8830 tsec 24.5310
#FOTS# QuadFit found better minimum quadopt=(dt=1.944,rms=0.375775) vs oldopt=(dt=1.62,rms=0.375775)
#GCMRL# 574 dt 1.944000 rms 0.376 0.000% neg 0 invalid 762 tFOTS 17.2520 tGradient 9.0320 tsec 27.3990
#GCAMreg# pass 0 level1 4 level2 1 tsec 71.774 sigma 0.5
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.03
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=64, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.375775
#GCMRL# 576 dt 25.920000 rms 0.376 0.015% neg 0 invalid 762 tFOTS 16.2590 tGradient 9.0250 tsec 26.3680
#FOTS# QuadFit found better minimum quadopt=(dt=20.8,rms=0.375706) vs oldopt=(dt=25.92,rms=0.375708)
#GCMRL# 577 dt 20.800000 rms 0.376 0.000% neg 0 invalid 762 tFOTS 17.1880 tGradient 9.0380 tsec 27.3300
#GCMRL# 578 dt 20.800000 rms 0.376 0.002% neg 0 invalid 762 tFOTS 0.0000 tGradient 9.0090 tsec 10.0920
setting smoothness cost coefficient to 0.118
#GCAMreg# pass 0 level1 3 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.12
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.376188
#FOTS# QuadFit found better minimum quadopt=(dt=11.2,rms=0.375971) vs oldopt=(dt=8,rms=0.375989)
#GCMRL# 580 dt 11.200000 rms 0.376 0.058% neg 0 invalid 762 tFOTS 17.2790 tGradient 6.6850 tsec 25.0560
#FOTS# QuadFit found better minimum quadopt=(dt=6.4,rms=0.37594) vs oldopt=(dt=8,rms=0.375943)
#GCMRL# 581 dt 6.400000 rms 0.376 0.000% neg 0 invalid 762 tFOTS 17.2550 tGradient 6.7100 tsec 25.0810
#GCAMreg# pass 0 level1 3 level2 1 tsec 68.697 sigma 0.5
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.12
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=16, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.37594
#FOTS# QuadFit found better minimum quadopt=(dt=86.9565,rms=0.374039) vs oldopt=(dt=32,rms=0.374485)
iter 0, gcam->neg = 22
after 9 iterations, nbhd size=1, neg = 0
#GCMRL# 583 dt 86.956522 rms 0.374 0.497% neg 0 invalid 762 tFOTS 17.2580 tGradient 6.6680 tsec 40.0460
#FOTS# QuadFit found better minimum quadopt=(dt=25.4316,rms=0.373277) vs oldopt=(dt=32,rms=0.373375)
iter 0, gcam->neg = 7
after 3 iterations, nbhd size=0, neg = 0
#GCMRL# 584 dt 25.431579 rms 0.373 0.000% neg 0 invalid 762 tFOTS 17.2140 tGradient 6.6830 tsec 31.9530
iter 0, gcam->neg = 1
after 0 iterations, nbhd size=0, neg = 0
#GCMRL# 585 dt 25.431579 rms 0.373 0.083% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.7230 tsec 10.6670
iter 0, gcam->neg = 7
after 2 iterations, nbhd size=0, neg = 0
#GCMRL# 586 dt 25.431579 rms 0.373 0.095% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.6550 tsec 13.3030
iter 0, gcam->neg = 9
after 12 iterations, nbhd size=1, neg = 0
#GCMRL# 587 dt 25.431579 rms 0.372 0.083% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.7120 tsec 26.9190
iter 0, gcam->neg = 10
after 5 iterations, nbhd size=0, neg = 0
#GCMRL# 588 dt 25.431579 rms 0.372 0.108% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.6460 tsec 17.3870
iter 0, gcam->neg = 15
after 11 iterations, nbhd size=1, neg = 0
#GCMRL# 589 dt 25.431579 rms 0.372 0.096% neg 0 invalid 762 tFOTS 0.0000 tGradient 6.7090 tsec 25.5300
#FOTS# QuadFit found better minimum quadopt=(dt=44.8,rms=0.371393) vs oldopt=(dt=32,rms=0.371426)
iter 0, gcam->neg = 1
after 0 iterations, nbhd size=0, neg = 0
setting smoothness cost coefficient to 0.400
#GCAMreg# pass 0 level1 2 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.40
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.374087
#FOTS# QuadFit found better minimum quadopt=(dt=3.456,rms=0.373984) vs oldopt=(dt=2.88,rms=0.373988)
#GCMRL# 591 dt 3.456000 rms 0.374 0.028% neg 0 invalid 762 tFOTS 14.6320 tGradient 4.2830 tsec 19.8870
#FOTS# QuadFit found better minimum quadopt=(dt=1.008,rms=0.373976) vs oldopt=(dt=0.72,rms=0.373977)
#GCMRL# 592 dt 1.008000 rms 0.374 0.000% neg 0 invalid 762 tFOTS 13.8560 tGradient 4.2860 tsec 19.1400
#GCMRL# 593 dt 1.008000 rms 0.374 0.001% neg 0 invalid 762 tFOTS 0.0000 tGradient 4.3080 tsec 5.2920
iter 0, gcam->neg = 1
after 1 iterations, nbhd size=0, neg = 0
#GCAMreg# pass 0 level1 2 level2 1 tsec 61.044 sigma 0.5
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=0.40
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=4, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.373974
#FOTS# QuadFit found better minimum quadopt=(dt=21.7358,rms=0.372501) vs oldopt=(dt=11.52,rms=0.372842)
iter 0, gcam->neg = 11
after 3 iterations, nbhd size=0, neg = 0
#GCMRL# 595 dt 21.735849 rms 0.372 0.394% neg 0 invalid 762 tFOTS 16.2350 tGradient 4.2850 tsec 26.0320
#FOTS# QuadFit found better minimum quadopt=(dt=22.8571,rms=0.37164) vs oldopt=(dt=11.52,rms=0.371845)
iter 0, gcam->neg = 17
after 10 iterations, nbhd size=1, neg = 0
#GCMRL# 596 dt 22.857143 rms 0.372 0.000% neg 0 invalid 762 tFOTS 14.6410 tGradient 4.2500 tsec 26.9380
iter 0, gcam->neg = 27
after 15 iterations, nbhd size=1, neg = 0
setting smoothness cost coefficient to 1.000
#GCAMreg# pass 0 level1 1 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=1.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.376315
#FOTS# QuadFit found better minimum quadopt=(dt=-0.000160217,rms=0.376315) vs oldopt=(dt=7.8125e-05,rms=0.376315)
#GCMRL# 598 dt -0.000160 rms 0.376 0.000% neg 0 invalid 762 tFOTS 18.4170 tGradient 2.7270 tsec 22.1140
#GCAMreg# pass 0 level1 1 level2 1 tsec 49.123 sigma 0.5
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=1.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=1, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.376315
resetting metric properties...
setting smoothness cost coefficient to 2.000
#GCAMreg# pass 0 level1 0 level2 0 tsec 0 sigma 2
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=2.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=2.0,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=2.000...
GCAMRegisterLevel(): init RMS 0.368515
#FOTS# QuadFit found better minimum quadopt=(dt=1.11117,rms=0.361078) vs oldopt=(dt=1.28,rms=0.361224)
iter 0, gcam->neg = 480
after 16 iterations, nbhd size=1, neg = 0
#GCMRL# 601 dt 1.111171 rms 0.362 1.841% neg 0 invalid 762 tFOTS 14.6090 tGradient 2.0770 tsec 28.2070
#FOTS# QuadFit found better minimum quadopt=(dt=2.34375e-05,rms=0.361732) vs oldopt=(dt=1.95313e-05,rms=0.361732)
#GCMRL# 602 dt 0.000023 rms 0.362 0.000% neg 0 invalid 762 tFOTS 18.3960 tGradient 2.0970 tsec 21.4880
#GCAMreg# pass 0 level1 0 level2 1 tsec 62.043 sigma 0.5
l_jacobian=1.00 l_log_likelihood=0.20 l_smoothness=2.00
tol=2.50e-01, dt=5.00e-02, exp_k=20.0, momentum=0.90, levels=6, niter=500, lbl_dist=10.00, avgs=0, sigma=0.5,type=2, relabel=0, neg=yes
blurring input image with Gaussian with sigma=0.500...
GCAMRegisterLevel(): init RMS 0.361732
#FOTS# QuadFit found better minimum quadopt=(dt=0.064,rms=0.361659) vs oldopt=(dt=0.08,rms=0.361659)
#GCMRL# 604 dt 0.064000 rms 0.362 0.020% neg 0 invalid 762 tFOTS 14.6680 tGradient 2.1200 tsec 17.7680
#FOTS# QuadFit found better minimum quadopt=(dt=0.028,rms=0.361639) vs oldopt=(dt=0.02,rms=0.361643)
#GCMRL# 605 dt 0.028000 rms 0.362 0.000% neg 0 invalid 762 tFOTS 14.7730 tGradient 2.0850 tsec 17.8680
#GCMRL# 606 dt 0.028000 rms 0.362 0.004% neg 0 invalid 762 tFOTS 0.0000 tGradient 2.0870 tsec 3.0680
GCAMregister done in 14.3127 min
writing output transformation to transforms/talairach.m3z...
GCAMwrite
Calls to gcamLogLikelihoodEnergy 4499 tmin = 15.7958
Calls to gcamLabelEnergy 3971 tmin = 1.40543
Calls to gcamJacobianEnergy 4499 tmin = 15.9149
Calls to gcamSmoothnessEnergy 4499 tmin = 9.72668
Calls to gcamLogLikelihoodTerm 608 tmin = 5.17678
Calls to gcamLabelTerm 568 tmin = 7.171
Calls to gcamJacobianTerm 608 tmin = 12.186
Calls to gcamSmoothnessTerm 608 tmin = 2.22427
Calls to gcamComputeGradient 608 tmin = 96.8958
Calls to gcamComputeMetricProperties 6257 tmin = 17.1354
mri_ca_register took 2 hours, 52 minutes and 0 seconds.
#VMPC# mri_ca_register VmPeak 2023732
FSRUNTIME@ mri_ca_register 2.8666 hours 1 threads
#--------------------------------------
#@# SubCort Seg Thu Jun 29 06:11:21 UTC 2023
mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /opt/freesurfer-7.3.2/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz
sysname Linux
hostname jupyter-stebo85
machine x86_64
setenv SUBJECTS_DIR /home/jovyan/example-notebooks/structural_imaging/freesurfer_output
cd /home/jovyan/example-notebooks/structural_imaging/freesurfer_output/subjectname/mri
mri_ca_label -relabel_unlikely 9 .3 -prior 0.5 -align norm.mgz transforms/talairach.m3z /opt/freesurfer-7.3.2/average/RB_all_2020-01-02.gca aseg.auto_noCCseg.mgz
relabeling unlikely voxels with window_size = 9 and prior threshold 0.30
using Gibbs prior factor = 0.500
renormalizing sequences with structure alignment, equivalent to:
-renormalize
-renormalize_mean 0.500
-regularize 0.500
== Number of threads available to for OpenMP = 1 ==
reading 1 input volumes
reading classifier array from /opt/freesurfer-7.3.2/average/RB_all_2020-01-02.gca
reading input volume from norm.mgz
average std[0] = 7.2
reading transform from transforms/talairach.m3z
setting orig areas to linear transform determinant scaled 6.27
Atlas used for the 3D morph was /opt/freesurfer-7.3.2/average/RB_all_2020-01-02.gca
average std = 7.2 using min determinant for regularization = 5.2
0 singular and 0 ill-conditioned covariance matrices regularized
labeling volume...
renormalizing by structure alignment....
renormalizing input #0
gca peak = 0.15521 (20)
mri peak = 0.17945 ( 7)
Left_Lateral_Ventricle (4): linear fit = 0.35 x + 0.0 (1560 voxels, overlap=0.004)
Left_Lateral_Ventricle (4): linear fit = 0.40 x + 0.0 (1560 voxels, peak = 7), gca=8.0
gca peak = 0.20380 (13)
mri peak = 0.17272 ( 7)
Right_Lateral_Ventricle (43): linear fit = 0.43 x + 0.0 (1589 voxels, overlap=0.049)
Right_Lateral_Ventricle (43): linear fit = 0.43 x + 0.0 (1589 voxels, peak = 6), gca=5.5
gca peak = 0.26283 (96)
mri peak = 0.16332 (106)
Right_Pallidum (52): linear fit = 1.10 x + 0.0 (819 voxels, overlap=0.128)
Right_Pallidum (52): linear fit = 1.10 x + 0.0 (819 voxels, peak = 105), gca=105.1
gca peak = 0.15814 (97)
mri peak = 0.11072 (106)
Left_Pallidum (13): linear fit = 1.12 x + 0.0 (858 voxels, overlap=0.026)
Left_Pallidum (13): linear fit = 1.12 x + 0.0 (858 voxels, peak = 109), gca=109.1
gca peak = 0.27624 (56)
mri peak = 0.10696 (56)
Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (782 voxels, overlap=1.011)
Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (782 voxels, peak = 56), gca=56.0
gca peak = 0.28723 (59)
mri peak = 0.09347 (60)
Left_Hippocampus (17): linear fit = 1.02 x + 0.0 (838 voxels, overlap=1.011)
Left_Hippocampus (17): linear fit = 1.02 x + 0.0 (838 voxels, peak = 60), gca=60.5
gca peak = 0.07623 (103)
mri peak = 0.13857 (102)
Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (41973 voxels, overlap=0.615)
Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (41973 voxels, peak = 103), gca=103.0
gca peak = 0.07837 (105)
mri peak = 0.13751 (104)
Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (43265 voxels, overlap=0.620)
Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (43265 voxels, peak = 105), gca=105.0
gca peak = 0.10165 (58)
mri peak = 0.04398 (66)
Left_Cerebral_Cortex (3): linear fit = 1.10 x + 0.0 (29448 voxels, overlap=0.783)
Left_Cerebral_Cortex (3): linear fit = 1.10 x + 0.0 (29448 voxels, peak = 64), gca=63.5
gca peak = 0.11113 (58)
mri peak = 0.04406 (64)
Right_Cerebral_Cortex (42): linear fit = 1.08 x + 0.0 (28388 voxels, overlap=0.735)
Right_Cerebral_Cortex (42): linear fit = 1.08 x + 0.0 (28388 voxels, peak = 62), gca=62.4
gca peak = 0.27796 (67)
mri peak = 0.18231 (76)
Right_Caudate (50): linear fit = 1.12 x + 0.0 (935 voxels, overlap=0.231)
Right_Caudate (50): linear fit = 1.12 x + 0.0 (935 voxels, peak = 75), gca=74.7
gca peak = 0.14473 (69)
mri peak = 0.13884 (77)
Left_Caudate (11): linear fit = 1.00 x + 0.0 (901 voxels, overlap=0.937)
Left_Caudate (11): linear fit = 1.00 x + 0.0 (901 voxels, peak = 69), gca=69.0
gca peak = 0.14301 (56)
mri peak = 0.05310 (48)
Left_Cerebellum_Cortex (8): linear fit = 0.86 x + 0.0 (14929 voxels, overlap=0.755)
Left_Cerebellum_Cortex (8): linear fit = 0.86 x + 0.0 (14929 voxels, peak = 48), gca=47.9
gca peak = 0.14610 (55)
mri peak = 0.04965 (46)
Right_Cerebellum_Cortex (47): linear fit = 0.83 x + 0.0 (15407 voxels, overlap=0.643)
Right_Cerebellum_Cortex (47): linear fit = 0.83 x + 0.0 (15407 voxels, peak = 46), gca=45.9
gca peak = 0.16309 (85)
mri peak = 0.07994 (89)
Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (5075 voxels, overlap=0.821)
Left_Cerebellum_White_Matter (7): linear fit = 1.07 x + 0.0 (5075 voxels, peak = 91), gca=90.5
gca peak = 0.15172 (84)
mri peak = 0.08530 (88)
Right_Cerebellum_White_Matter (46): linear fit = 1.07 x + 0.0 (5192 voxels, overlap=0.827)
Right_Cerebellum_White_Matter (46): linear fit = 1.07 x + 0.0 (5192 voxels, peak = 89), gca=89.5
gca peak = 0.30461 (58)
mri peak = 0.10000 (63)
Left_Amygdala (18): linear fit = 1.10 x + 0.0 (469 voxels, overlap=0.856)
Left_Amygdala (18): linear fit = 1.10 x + 0.0 (469 voxels, peak = 64), gca=63.5
gca peak = 0.32293 (57)
mri peak = 0.11073 (62)
Right_Amygdala (54): linear fit = 1.10 x + 0.0 (449 voxels, overlap=0.625)
Right_Amygdala (54): linear fit = 1.10 x + 0.0 (449 voxels, peak = 62), gca=62.4
gca peak = 0.11083 (90)
mri peak = 0.08778 (91)
Left_Thalamus (10): linear fit = 1.05 x + 0.0 (4105 voxels, overlap=0.842)
Left_Thalamus (10): linear fit = 1.05 x + 0.0 (4105 voxels, peak = 95), gca=94.9
gca peak = 0.11393 (83)
mri peak = 0.08291 (89)
Right_Thalamus (49): linear fit = 1.10 x + 0.0 (4093 voxels, overlap=0.697)
Right_Thalamus (49): linear fit = 1.10 x + 0.0 (4093 voxels, peak = 91), gca=90.9
gca peak = 0.08575 (81)
mri peak = 0.09072 (84)
Left_Putamen (12): linear fit = 1.10 x + 0.0 (1968 voxels, overlap=0.477)
Left_Putamen (12): linear fit = 1.10 x + 0.0 (1968 voxels, peak = 89), gca=88.7
gca peak = 0.08618 (78)
mri peak = 0.09372 (88)
Right_Putamen (51): linear fit = 1.10 x + 0.0 (2102 voxels, overlap=0.522)
Right_Putamen (51): linear fit = 1.10 x + 0.0 (2102 voxels, peak = 85), gca=85.4
gca peak = 0.08005 (78)
mri peak = 0.06509 (96)
Brain_Stem (16): linear fit = 1.18 x + 0.0 (10329 voxels, overlap=0.363)
Brain_Stem (16): linear fit = 1.18 x + 0.0 (10329 voxels, peak = 92), gca=92.4
gca peak = 0.12854 (88)
mri peak = 0.08940 (101)
Right_VentralDC (60): linear fit = 1.20 x + 0.0 (1512 voxels, overlap=0.017)
Right_VentralDC (60): linear fit = 1.20 x + 0.0 (1512 voxels, peak = 105), gca=105.2
gca peak = 0.15703 (87)
mri peak = 0.10143 (101)
Left_VentralDC (28): linear fit = 1.20 x + 0.0 (1493 voxels, overlap=0.012)
Left_VentralDC (28): linear fit = 1.20 x + 0.0 (1493 voxels, peak = 104), gca=104.0
gca peak = 0.17522 (25)
mri peak = 0.42105 ( 9)
gca peak = 0.17113 (14)
mri peak = 0.35667 ( 3)
Fourth_Ventricle (15): linear fit = 0.12 x + 0.0 (291 voxels, overlap=0.016)
Fourth_Ventricle (15): linear fit = 0.12 x + 0.0 (291 voxels, peak = 2), gca=1.8
gca peak Unknown = 0.94777 ( 0)
gca peak Left_Inf_Lat_Vent = 0.16627 (28)
gca peak Third_Ventricle = 0.17522 (25)
gca peak Fourth_Ventricle = 0.17113 (14)
gca peak CSF = 0.20346 (36)
gca peak Left_Accumbens_area = 0.70646 (62)
gca peak Left_undetermined = 1.00000 (28)
gca peak Left_vessel = 0.89917 (53)
gca peak Left_choroid_plexus = 0.11689 (35)
gca peak Right_Inf_Lat_Vent = 0.25504 (23)
gca peak Right_Accumbens_area = 0.31650 (65)
gca peak Right_vessel = 0.77268 (52)
gca peak Right_choroid_plexus = 0.13275 (38)
gca peak Fifth_Ventricle = 0.60973 (33)
gca peak WM_hypointensities = 0.11013 (77)
gca peak non_WM_hypointensities = 0.11354 (41)
gca peak Optic_Chiasm = 0.51646 (76)
not using caudate to estimate GM means
estimating mean gm scale to be 1.06 x + 0.0
estimating mean wm scale to be 1.00 x + 0.0
estimating mean csf scale to be 0.41 x + 0.0
Left_Pallidum too bright - rescaling by 0.924 (from 1.125) to 100.9 (was 109.1)
Right_Pallidum too bright - rescaling by 0.960 (from 1.095) to 100.9 (was 105.1)
saving intensity scales to aseg.auto_noCCseg.label_intensities.txt
renormalizing by structure alignment....
renormalizing input #0
gca peak = 0.31706 ( 7)
mri peak = 0.17945 ( 7)
Left_Lateral_Ventricle (4): linear fit = 0.82 x + 0.0 (1560 voxels, overlap=0.926)
Left_Lateral_Ventricle (4): linear fit = 0.82 x + 0.0 (1560 voxels, peak = 6), gca=5.8
gca peak = 0.29738 ( 6)
mri peak = 0.17272 ( 7)
Right_Lateral_Ventricle (43): linear fit = 1.25 x + 0.0 (1589 voxels, overlap=0.924)
Right_Lateral_Ventricle (43): linear fit = 1.25 x + 0.0 (1589 voxels, peak = 7), gca=7.5
gca peak = 0.28156 (100)
mri peak = 0.16332 (106)
Right_Pallidum (52): linear fit = 1.04 x + 0.0 (819 voxels, overlap=0.821)
Right_Pallidum (52): linear fit = 1.04 x + 0.0 (819 voxels, peak = 104), gca=104.5
gca peak = 0.15478 (99)
mri peak = 0.11072 (106)
Left_Pallidum (13): linear fit = 1.08 x + 0.0 (858 voxels, overlap=0.466)
Left_Pallidum (13): linear fit = 1.08 x + 0.0 (858 voxels, peak = 106), gca=106.4
gca peak = 0.26416 (56)
mri peak = 0.10696 (56)
Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (782 voxels, overlap=1.011)
Right_Hippocampus (53): linear fit = 1.00 x + 0.0 (782 voxels, peak = 56), gca=56.0
gca peak = 0.33569 (58)
mri peak = 0.09347 (60)
Left_Hippocampus (17): linear fit = 0.99 x + 0.0 (838 voxels, overlap=1.009)
Left_Hippocampus (17): linear fit = 0.99 x + 0.0 (838 voxels, peak = 57), gca=57.1
gca peak = 0.07621 (103)
mri peak = 0.13857 (102)
Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (41973 voxels, overlap=0.615)
Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (41973 voxels, peak = 103), gca=103.0
gca peak = 0.07845 (105)
mri peak = 0.13751 (104)
Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (43265 voxels, overlap=0.620)
Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (43265 voxels, peak = 105), gca=105.0
gca peak = 0.08782 (63)
mri peak = 0.04398 (66)
Left_Cerebral_Cortex (3): linear fit = 1.00 x + 0.0 (29448 voxels, overlap=0.960)
Left_Cerebral_Cortex (3): linear fit = 1.00 x + 0.0 (29448 voxels, peak = 63), gca=63.0
gca peak = 0.10681 (63)
mri peak = 0.04406 (64)
Right_Cerebral_Cortex (42): linear fit = 1.02 x + 0.0 (28388 voxels, overlap=0.926)
Right_Cerebral_Cortex (42): linear fit = 1.02 x + 0.0 (28388 voxels, peak = 65), gca=64.6
gca peak = 0.22478 (74)
mri peak = 0.18231 (76)
Right_Caudate (50): linear fit = 1.00 x + 0.0 (935 voxels, overlap=1.000)
Right_Caudate (50): linear fit = 1.00 x + 0.0 (935 voxels, peak = 74), gca=74.0
gca peak = 0.14473 (69)
mri peak = 0.13884 (77)
Left_Caudate (11): linear fit = 1.00 x + 0.0 (901 voxels, overlap=0.937)
Left_Caudate (11): linear fit = 1.00 x + 0.0 (901 voxels, peak = 69), gca=69.0
gca peak = 0.16470 (48)
mri peak = 0.05310 (48)
Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (14929 voxels, overlap=1.000)
Left_Cerebellum_Cortex (8): linear fit = 1.00 x + 0.0 (14929 voxels, peak = 48), gca=48.0
gca peak = 0.18229 (46)
mri peak = 0.04965 (46)
Right_Cerebellum_Cortex (47): linear fit = 1.05 x + 0.0 (15407 voxels, overlap=0.996)
Right_Cerebellum_Cortex (47): linear fit = 1.05 x + 0.0 (15407 voxels, peak = 49), gca=48.5
gca peak = 0.15119 (90)
mri peak = 0.07994 (89)
Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (5075 voxels, overlap=0.982)
Left_Cerebellum_White_Matter (7): linear fit = 1.00 x + 0.0 (5075 voxels, peak = 90), gca=89.6
gca peak = 0.16700 (90)
mri peak = 0.08530 (88)
Right_Cerebellum_White_Matter (46): linear fit = 0.99 x + 0.0 (5192 voxels, overlap=0.982)
Right_Cerebellum_White_Matter (46): linear fit = 0.99 x + 0.0 (5192 voxels, peak = 89), gca=88.7
gca peak = 0.23301 (63)
mri peak = 0.10000 (63)
Left_Amygdala (18): linear fit = 1.01 x + 0.0 (469 voxels, overlap=1.028)
Left_Amygdala (18): linear fit = 1.01 x + 0.0 (469 voxels, peak = 64), gca=63.9
gca peak = 0.29567 (63)
mri peak = 0.11073 (62)
Right_Amygdala (54): linear fit = 1.00 x + 0.0 (449 voxels, overlap=1.021)
Right_Amygdala (54): linear fit = 1.00 x + 0.0 (449 voxels, peak = 63), gca=63.0
gca peak = 0.10834 (95)
mri peak = 0.08778 (91)
Left_Thalamus (10): linear fit = 0.99 x + 0.0 (4105 voxels, overlap=0.971)
Left_Thalamus (10): linear fit = 0.99 x + 0.0 (4105 voxels, peak = 94), gca=93.6
gca peak = 0.11610 (91)
mri peak = 0.08291 (89)
Right_Thalamus (49): linear fit = 1.00 x + 0.0 (4093 voxels, overlap=0.955)
Right_Thalamus (49): linear fit = 1.00 x + 0.0 (4093 voxels, peak = 91), gca=91.0
gca peak = 0.07778 (89)
mri peak = 0.09072 (84)
Left_Putamen (12): linear fit = 1.00 x + 0.0 (1968 voxels, overlap=0.718)
Left_Putamen (12): linear fit = 1.00 x + 0.0 (1968 voxels, peak = 89), gca=89.0
gca peak = 0.08743 (83)
mri peak = 0.09372 (88)
Right_Putamen (51): linear fit = 1.00 x + 0.0 (2102 voxels, overlap=0.840)
Right_Putamen (51): linear fit = 1.00 x + 0.0 (2102 voxels, peak = 83), gca=83.0
gca peak = 0.07618 (93)
mri peak = 0.06509 (96)
Brain_Stem (16): linear fit = 0.98 x + 0.0 (10329 voxels, overlap=0.855)
Brain_Stem (16): linear fit = 0.98 x + 0.0 (10329 voxels, peak = 91), gca=90.7
gca peak = 0.10886 (105)
mri peak = 0.08940 (101)
Right_VentralDC (60): linear fit = 1.00 x + 0.0 (1512 voxels, overlap=0.835)
Right_VentralDC (60): linear fit = 1.00 x + 0.0 (1512 voxels, peak = 106), gca=105.5
gca peak = 0.16520 (102)
mri peak = 0.10143 (101)
Left_VentralDC (28): linear fit = 0.99 x + 0.0 (1493 voxels, overlap=0.880)
Left_VentralDC (28): linear fit = 0.99 x + 0.0 (1493 voxels, peak = 100), gca=100.5
gca peak = 0.33670 (11)
mri peak = 0.42105 ( 9)
gca peak = 0.38155 ( 6)
mri peak = 0.35667 ( 3)
Fourth_Ventricle (15): linear fit = 0.32 x + 0.0 (291 voxels, overlap=0.435)
Fourth_Ventricle (15): linear fit = 0.32 x + 0.0 (291 voxels, peak = 2), gca=1.9
gca peak Unknown = 0.94777 ( 0)
gca peak Left_Inf_Lat_Vent = 0.20560 (29)
gca peak Third_Ventricle = 0.33670 (11)
gca peak Fourth_Ventricle = 0.38155 ( 6)
gca peak CSF = 0.34330 (15)
gca peak Left_Accumbens_area = 0.61521 (62)
gca peak Left_undetermined = 1.00000 (28)
gca peak Left_vessel = 0.89917 (53)
gca peak Left_choroid_plexus = 0.11689 (35)
gca peak Right_Inf_Lat_Vent = 0.25695 (23)
gca peak Right_Accumbens_area = 0.37254 (72)
gca peak Right_vessel = 0.77268 (52)
gca peak Right_choroid_plexus = 0.13275 (38)
gca peak Fifth_Ventricle = 0.59846 (14)
gca peak WM_hypointensities = 0.11042 (77)
gca peak non_WM_hypointensities = 0.11354 (41)
gca peak Optic_Chiasm = 0.54524 (76)
not using caudate to estimate GM means
estimating mean gm scale to be 1.00 x + 0.0
estimating mean wm scale to be 1.00 x + 0.0
estimating mean csf scale to be 1.03 x + 0.0
Left_Pallidum too bright - rescaling by 0.948 (from 1.075) to 100.9 (was 106.4)
Right_Pallidum too bright - rescaling by 0.965 (from 1.045) to 100.9 (was 104.5)
saving intensity scales to aseg.auto_noCCseg.label_intensities.txt
saving sequentially combined intensity scales to aseg.auto_noCCseg.label_intensities.txt
!ls ./freesurfer_output/subjectname/mri
T1.mgz mri_nu_correct.mni.log.bak
antsdn.brain.mgz norm.mgz
aparc+aseg.mgz nu.mgz
aparc.DKTatlas+aseg.mgz orig
aparc.a2009s+aseg.mgz orig.mgz
aseg.auto.mgz orig_nu.mgz
aseg.auto_noCCseg.label_intensities.txt rawavg.mgz
aseg.auto_noCCseg.mgz rh.ribbon.mgz
aseg.mgz ribbon.mgz
aseg.presurf.hypos.mgz segment.dat
aseg.presurf.mgz surface.defects.mgz
brain.finalsurfs.mgz talairach.label_intensities.txt
brain.mgz talairach.log
brainmask.auto.mgz talairach_with_skull.log
brainmask.mgz transforms
ctrl_pts.mgz wm.asegedit.mgz
filled.auto.mgz wm.mgz
filled.mgz wm.seg.mgz
lh.ribbon.mgz wmparc.mgz
mri_nu_correct.mni.log
!ls ./freesurfer_output/subjectname/surf
autodet.gw.stats.lh.dat lh.smoothwm.K1.crv rh.inflated.nofix
autodet.gw.stats.rh.dat lh.smoothwm.K2.crv rh.jacobian_white
lh.area lh.smoothwm.S.crv rh.orig
lh.area.mid lh.smoothwm.nofix rh.orig.nofix
lh.area.pial lh.sphere rh.orig.premesh
lh.avg_curv lh.sphere.reg rh.pial
lh.curv lh.sulc rh.pial.T1
lh.curv.pial lh.thickness rh.qsphere.nofix
lh.defect_borders lh.volume rh.smoothwm
lh.defect_chull lh.w-g.pct.mgh rh.smoothwm.BE.crv
lh.defect_labels lh.white rh.smoothwm.C.crv
lh.defects.pointset lh.white.H rh.smoothwm.FI.crv
lh.fsaverage.sphere.reg lh.white.K rh.smoothwm.H.crv
lh.inflated lh.white.preaparc rh.smoothwm.K.crv
lh.inflated.H lh.white.preaparc.H rh.smoothwm.K1.crv
lh.inflated.K lh.white.preaparc.K rh.smoothwm.K2.crv
lh.inflated.nofix rh.area rh.smoothwm.S.crv
lh.jacobian_white rh.area.mid rh.smoothwm.nofix
lh.orig rh.area.pial rh.sphere
lh.orig.nofix rh.avg_curv rh.sphere.reg
lh.orig.premesh rh.curv rh.sulc
lh.pial rh.curv.pial rh.thickness
lh.pial.T1 rh.defect_borders rh.volume
lh.qsphere.nofix rh.defect_chull rh.w-g.pct.mgh
lh.smoothwm rh.defect_labels rh.white
lh.smoothwm.BE.crv rh.defects.pointset rh.white.H
lh.smoothwm.C.crv rh.fsaverage.sphere.reg rh.white.K
lh.smoothwm.FI.crv rh.inflated rh.white.preaparc
lh.smoothwm.H.crv rh.inflated.H rh.white.preaparc.H
lh.smoothwm.K.crv rh.inflated.K rh.white.preaparc.K
import ipyniivue
nv = ipyniivue.Niivue(crosshair_color=[0,1,0,1])
nv.add_volume('./freesurfer_output/subjectname/mri/orig.mgz')
nv.add_volume('./freesurfer_output/subjectname/mri/aseg.mgz')