Freesurfer February 2007

freesurfer@nmr.mgh.harvard.edu

59 participants
80 discussions

tissue segmentation output file
by nayoung＠cis.jhu.edu 27 Feb '07

27 Feb '07

Hello, I am wondering if I can get a mask(labelled volume) for each tissue types - not a structure. I can see that Freesurfer is generating a surfaces between gray/white and gray/CSF. Can I get a volume which correspond to WM/GM/CSF? Thank you. Nayoung

2 1

file conversion
by Lindsay Casmaer 27 Feb '07

27 Feb '07

I am trying to run the first recon-all command on Seimens dicom files to convert to mgz. I moved the files for each acquisition to a separate directory ex: 001 002 003, 3 subdirectories each containing the files for each scan acquisition. I then run the command recon-all -i 447-5-440.ima -i 447-6-568.ima -i 447-4-312.ima -s 030217_vc11174 -where 447-5-440.ima, 447-6-568.ima, and 447-4-312.ima are the first files that appear in each acquisition directory. The error I get is "cannot find 447-5-440.ima". Any help would be greatly appreciated. Do I need to run this command from the directory where the files are located? Do I need to set my SUBJECTS_DIR each time to the individual session? Thank you. Lindsay Casmaer Washington University in St. Louis lcasmaer(a)artsci.wustl.edu

3 2

segmentation
by Sasha Wolosin 26 Feb '07

26 Feb '07

In freesurfer, is there any way to run a gray/white matter segmentation within the cortex? Thanks, Sasha Disclaimer: The materials in this e-mail are private and may contain Protected Health Information. Please note that e-mail is not necessarily confidential or secure. Your use of e-mail constitutes your acknowledgment of these confidentiality and security limitations. If you are not the intended recipient, be advised that any unauthorized use, disclosure, copying, distribution, or the taking of any action in reliance on the contents of this information is strictly prohibited. If you have received this e-mail in error, please immediately notify the sender via telephone or return e-mail.

3 6

Question about trials w/subject errors
by Dan Dillon 26 Feb '07

26 Feb '07

Dear FreeSurfers, We are running a paradigm that occasionally elicits erroneous responses from subjects. In our mkanalysis step, we have 13 conditions-10 are legit, and codes 11, 12, 13 correspond to various kinds of errors. Thus, we only use the first 10 codes in mkcontrast, etc. However, occasionally we have a subject who makes no errors. How should we account for this? Is it okay to just change the # of conditions in the mkanalysis .info file (from 13 to 10) and then run selxavg on these folks? Can we then average their data in with people who have all 13 conditions? Thanks! Dan Dillon

2 1

26 Feb '07

FreeSurfers, I am using mri_volcluster on the output of a fixed-effects analysis, but am getting an error regarding my register.dat file. The register.dat file I'm directing mri_volcluster to is in the group's tal-ffx directory and lists subject "talairach". mri_volcluster is thus looking for subject "talairach" in my SUBJECTS_DIR. The problem is there's no such subject in SUBJECTS_DIR. Here's the message-ERROR: cannot find subject talairach in /exp/REfMRI/mg_2_21/structurals/. How do I fix this? Is a Talairach subject supposed to be put in SUBJECTS_DIR when I run func2tal-sess, or is this a part of a library I should have somewhere? Or do I create this file? Any help would be appreciated. Thanks! Dan Dillon

2 1

NIFTI Files
by Marek Kosinski 26 Feb '07

26 Feb '07

Hi, I am a freesurfer starter. I am trying to overlay statistical maps ,which I've created with FSL, on the brain surface. I've some questions regarding to that: 1) can the newest version of freesurfer deal with NIFTI files? (Because everytime when I try to use NIFTI files I get an error message.) 2) how can use mat files which I create with FSL-Flirt to overlay functional maps to the brain surface? 3) I do not want to use talaraich registration. Is there a possibility to omit talaraich registration. Thanks a lot Marek ___________________________________________________________ Der frühe Vogel fängt den Wurm. Hier gelangen Sie zum neuen Yahoo! Mail: http://mail.yahoo.de

3 2

how to get with BOLD change
by Lucia Maria Vaina 26 Feb '07

26 Feb '07

Hi: We're trying to do a correlation analysis between BOLD % Signal Change and behavioral performance. The fMRI data is being analyzed using freesurfer (specifically, the program roisummary-sess, see https://surfer.nmr.mgh.harvard.edu/fswiki/roisummary_2dsess? action=highlight&value=roisummary-sess). This program outputs a couple values (detailed on that page), notably, the number of voxels in the area, the number that were active above statistical threshold, and the "mean functional baseline offset". Unfortunately, since I am a novice in Freesurgfe, it's unclear to me how to get a % signal change from this data. Presumably, the offset value says something about the difference in activity between the on and off periods, but I don't know the exact meaning of this value, and how to get a % signal change from it. We get a single output file per run (~280s) and have 12 runs total (8 on day 1, 6 on day 2). We have various behavior metrics corresponding to each of these. I'm attaching one of the output files here. Thanks a lot! Lucia n002-task-3-008 98 32 1093.3451 8.9397 76.000000 3.300000 0.000000 1 1 7.8309

2 1

Why the .auto.-files?
by Oddbjørn Sæther 23 Feb '07

23 Feb '07

Hi, what is the meaning of the .auto.-files generated? In autorecon1 the brainmask.auto.mgz is generated, and thereafter copied to brainmask.mgz. The same for aseg.auto.mgz in autorecon2. What is the difference between "with/without" auto? Thanks in advance, oddbjørn

2 1

(no subject)
by Giri Prashanth 23 Feb '07

23 Feb '07

Hi Freesurfer experts, I have encountered the following error running the reconall -autorecon2 script, blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.474, neg=0, invalid=1511 0445: dt=0.080000, rms=0.471 (0.499%), neg=0, invalid=1Segmentation fault ERROR: mri_ca_register with non-zero status The complete ouput from the log file is attached below. I ran the autorecon1 earlier without any errors. The talirach transformation and skull stripping went fine on examination. There were also no memory errors logged in var/spool/mail directory. I am currently using Red Hat Enterprise Linux Client release 4.91 (Tikanga), with 2GB RAM on 64-bit Intel dual core. I would greatly appreciate any help on this. Thanks, -Giri /usr/local/bin/freesurfer/subjects/GK1 /usr/local/bin/freesurfer/bin/recon-all -autorecon2 -sid GK1 subjid GK1 setenv SUBJECTS_DIR /usr/local/bin/freesurfer/subjects FREESURFER_HOME /usr/local/bin/freesurfer Actual FREESURFER_HOME /usr/local/bin/freesurfer build-stamp.txt: freesurfer-Linux-centos4_x86_64-stable-pub-v3.0.4 Linux lab1 2.6.17-1.2519.4.21.el5 #1 SMP Wed Aug 30 18:18:28 EDT 2006 x86_64 x86_64 x86_64 GNU/Linux cputime unlimited filesize unlimited datasize unlimited stacksize 10240 kbytes coredumpsize 0 kbytes memoryuse unlimited vmemoryuse unlimited descriptors 1024 memorylocked 32 kbytes maxproc 16369 ######################################## program versions used $Id: recon-all,v 1.17.2.15 2006/11/21 04:58:13 nicks Exp $ $Id: mri_motion_correct.fsl,v 1.4 2006/01/01 21:56:44 greve Exp $ mri_convert -all-info ProgramName: mri_convert ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mri_convert.c,v 1.121.2.1 2006/08/28 18:00:04 greve Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5 CompilerName: GCC CompilerVersion: 30400 $Id: talairach,v 1.2 2005/12/12 23:04:35 greve Exp $ mri_convert --version stable3 Program nu_correct, built from: Package MNI N3, version 1.10, compiled by nicks@minerva(x86_64-unknown-linux-gnu) on 2005-11-15 at 20:37:35 ProgramName: mri_normalize ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mri_normalize.c,v 1.44.2.2 2006/06/28 23:38:13 nicks Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5CompilerName: GCC CompilerVersion: 30400 ProgramName: mri_watershed ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mri_watershed.cpp,v 1.48 2006/02/27 16:57:57 segonne Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5CompilerName: GCC CompilerVersion: 30400 ProgramName: mri_segment ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mri_segment.c,v 1.28.2.1 2006/04/12 02:02:58 nicks Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5 CompilerName: GCC CompilerVersion: 30400 ProgramName: mri_fill ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mri_fill.c,v 1.1012006/02/22 23:05:06 nicks Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5 CompilerName: GCC CompilerVersion: 30400 ProgramName: mri_tessellate ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mri_tessellate.c,v 1.27.2.1 2006/05/02 21:41:01 nicks Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5CompilerName: GCC CompilerVersion: 30400 ProgramName: mris_smooth ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mris_smooth.c,v 1.13.2.1 2006/05/02 21:41:01 nicks Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5 CompilerName: GCC CompilerVersion: 30400 ProgramName: mris_inflate ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mris_inflate.c,v 1.30.2.1 2006/05/02 21:41:01 nicks Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5 CompilerName: GCC CompilerVersion: 30400 ProgramName: mris_sphere ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mris_sphere.c,v 1.382006/02/08 00:17:40 nicks Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5 CompilerName: GCC CompilerVersion: 30400 ProgramName: mris_fix_topology ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mris_fix_topology.c,v 1.41 2006/02/06 19:41:59 nicks Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5CompilerName: GCC CompilerVersion: 30400 ProgramName: mris_euler_number ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mris_euler_number.c,v 1.4 2003/09/05 04:45:41 kteich Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5CompilerName: GCC CompilerVersion: 30400 ProgramName: mris_make_surfaces ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mris_make_surfaces.c,v 1.70.2.5 2006/07/18 18:26:01 nicks Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5CompilerName: GCC CompilerVersion: 30400 ProgramName: mris_register ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mris_register.c,v 1.32 2005/12/07 14:22:57 fischl Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5 CompilerName: GCC CompilerVersion: 30400 ProgramName: mrisp_paint ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mrisp_paint.c,v 1.62005/02/14 04:30:19 segonne Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5 CompilerName: GCC CompilerVersion: 30400 ProgramName: mri_label2label ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mri_label2label.c,v 1.23.2.1 2006/09/20 20:42:55 greve Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5CompilerName: GCC CompilerVersion: 30400 ProgramName: mri_em_register ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mri_em_register.c,v 1.52 2006/01/11 21:01:40 greve Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5 CompilerName: GCC CompilerVersion: 30400 ProgramName: mri_ca_normalize ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mri_ca_normalize.c,v 1.31 2005/08/15 14:14:22 fischl Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5CompilerName: GCC CompilerVersion: 30400 ProgramName: mri_ca_register ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mri_ca_register.c,v 1.48.2.7 2006/10/24 01:48:52 nicks Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5CompilerName: GCC CompilerVersion: 30400 ProgramName: mri_ca_label ProgramArguments: -all-info ProgramVersion: $Name: stable3 $ TimeStamp: 07/02/22-20:13:40-GMT CVS: $Id: mri_ca_label.c,v 1.68.2.2 2006/08/16 00:18:42 nicks Exp $ User: root Machine: lab1 Platform: Linux PlatformVersion: 2.6.17-1.2519.4.21.el5 CompilerName: GCC CompilerVersion: 30400 ####################################### GCA RB_all_2006-02-15.gca GCASkull RB_all_withskull_2006-02-15.gca AvgCurvTif average.curvature.filled.buckner40.tif GCS curvature.buckner40.filled.desikan_killiany.gcs ####################################### #------------------------------------- #@# EM Registration Thu Feb 22 15:13:40 EST 2007 /usr/local/bin/freesurfer/subjects/GK1/mri mri_em_register -mask brainmask.mgz nu.mgz/usr/local/bin/freesurfer/average/RB_all_2006- 02-15.gca transforms/talairach.lta using MR volume brainmask.mgz to mask input volume... reading 1 input volumes... logging results to talairach.log reading '/usr/local/bin/freesurfer/average/RB_all_2006-02-15.gca'... setting gca type = Normal gca type average std = 7.4 using min determinant for regularization = 5.5 0 singular and 1662 ill-conditioned covariance matrices regularized reading 'nu.mgz'... total sample mean = 87.9 (483 zeros) register_mri: find_optimal_transform find_optimal_transform: nsamples 2242, passno 0, spacing 8 ************************************************ First Search limited to translation only. ************************************************ Found translation: (-2.0, 32.7, 2.0): log p = -23693.6 Result so far: scale 1.000: max_log_p=-14331.5, old_max_log_p =-23693.6(thresh=- 23669.9) 1.125 0.000 0.000 -18.071; 0.000 1.179 0.184 -1.635; 0.000 -0.122 0.995 13.008; 0.000 0.000 0.000 1.000; Result so far: scale 1.000: max_log_p=-14331.5, old_max_log_p =-14331.5(thresh=- 14317.2) 1.125 0.000 0.000 -18.071; 0.000 1.179 0.184 -1.635; 0.000 -0.122 0.995 13.008; 0.000 0.000 0.000 1.000; dt = 5.00e-06, momentum=0.80, tol=1.00e-05 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.12500 0.00000 0.00000 -18.07085; 0.00000 1.17858 0.18412 -1.63507; 0.00000 -0.12245 0.99506 13.00834; 0.00000 0.00000 0.00000 1.00000; nsamples 2242 Quasinewton: input matrix 1.12500 0.00000 0.00000 -18.07085; 0.00000 1.17858 0.18412 -1.63507; 0.00000 -0.12245 0.99506 13.00834; 0.00000 0.00000 0.00000 1.00000; pass 2 through quasi-newton minimization... pass 3 through quasi-newton minimization... pass 4 through quasi-newton minimization... fp = 14318.311523, fret = 14318.311523, ftol = 0.000010, EPS = 0.000000 dt = 5.00e-06, momentum=0.80, tol=1.00e-07 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.12500 0.00000 0.00000 -18.07085; 0.00000 1.17858 0.18412 -1.63507; 0.00000 -0.12245 0.99506 13.00834; 0.00000 0.00000 0.00000 1.00000; nsamples 242283 Quasinewton: input matrix 1.12500 0.00000 0.00000 -18.07085; 0.00000 1.17858 0.18412 -1.63507; 0.00000 -0.12245 0.99506 13.00834; 0.00000 0.00000 0.00000 1.00000; pass 2 through quasi-newton minimization... pass 3 through quasi-newton minimization... pass 4 through quasi-newton minimization... pass 5 through quasi-newton minimization... pass 6 through quasi-newton minimization... freeing gibbs priors...done. bounding unknown intensity as < 16.0 or > 799.3 ************************************************ spacing=8, using 2242 sample points, tol=1.00e-05... ************************************************ resetting wm mean[0]: 109 --> 116 resetting gm mean[0]: 68 --> 68 input volume #1 is the most T1-like using real data threshold=24.0 using (106, 68, 109) as brain centroid... mean wm in atlas = 116, using box (90,52,88) --> (121, 83,129) to find MRI wm before smoothing, mri peak at 98 after smoothing, mri peak at 96, scaling input intensities by 1.208 scaling channel 0 by 1.20833 initial log_p = -93140.0 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** **************************************** Nine parameter search. iteration 1 nscales = 1 ... **************************************** reducing scale to 0.2500 *********************************************** Computing MAP estimate using 2242 samples... *********************************************** dfp_em_step_func: 002: -log(p) = 14331.5 after pass:transform: ( 1.12, 0.00, 0.00, -18.07) ( 0.00, 1.18, 0.18, -1.64) ( 0.00, -0.12, 1.00, 13.01) dfp_em_step_func: 003: -log(p) = 14324.0 after pass:transform: ( 1.12, -0.00, -0.00, -18.07) ( 0.00, 1.18, 0.18, -1.64) ( 0.00, -0.12, 1.00, 13.01) dfp_em_step_func: 004: -log(p) = 14319.0 after pass:transform: ( 1.12, -0.00, -0.00, -18.07) ( 0.00, 1.18, 0.18, -1.64) ( 0.00, -0.12, 1.00, 13.01) dfp_em_step_func: 005: -log(p) = 14318.3 after pass:transform: ( 1.12, -0.00, -0.00, -18.07) ( 0.00, 1.18, 0.18, -1.64) ( 0.00, -0.12, 1.00, 13.01) outof QuasiNewtonEMA: 007: -log(p) = 14318.3 tol 0.000010 Resulting transform: 1.125 0.000 0.000 -18.071; 0.000 1.179 0.184 -1.635; 0.000 -0.122 0.995 13.008; 0.000 0.000 0.000 1.000; pass 1, spacing 8: log(p) = -14331.5 (old=-93140.0) transform before final EM align: 1.125 0.000 0.000 -18.071; 0.000 1.179 0.184 -1.635; 0.000 -0.122 0.995 13.008; 0.000 0.000 0.000 1.000; ************************************************** EM alignment process ... Computing final MAP estimate using 242283 samples. ************************************************** dfp_em_step_func: 006: -log(p) = 1813558.6 after pass:transform: ( 1.12, 0.00, 0.00, -18.07) ( 0.00, 1.18, 0.18, -1.64) ( 0.00, -0.12, 1.00, 13.01) dfp_em_step_func: 007: -log(p) = 1806361.1 after pass:transform: ( 1.12, -0.00, 0.00, -18.07) ( 0.00, 1.18, 0.19, -1.64) ( 0.00, -0.12, 0.99, 13.01) dfp_em_step_func: 008: -log(p) = 1801868.9 after pass:transform: ( 1.12, 0.00, 0.00, -18.07) ( 0.00, 1.18, 0.19, -1.64) ( -0.00, -0.12, 0.99, 13.01) dfp_em_step_func: 009: -log(p) = 1797434.0 after pass:transform: ( 1.12, 0.00, 0.00, -18.07) ( 0.00, 1.18, 0.19, -1.64) ( 0.00, -0.12, 0.99, 13.01) dfp_em_step_func: 010: -log(p) = 1796255.2 after pass:transform: ( 1.12, 0.00, 0.01, -18.07) ( 0.00, 1.17, 0.19, -1.64) ( -0.00, -0.12, 0.99, 13.01) dfp_em_step_func: 011: -log(p) = 1791314.1 after pass:transform: ( 1.12, 0.00, 0.01, -18.07) ( 0.00, 1.17, 0.19, -1.64) ( 0.00, -0.12, 0.99, 13.01) outof QuasiNewtonEMA: 013: -log(p) = 1789155.6 tol 0.000000 final transform: 1.117 0.001 0.009 -18.071; 0.001 1.172 0.192 -1.635; 0.000 -0.125 0.991 13.008; 0.000 0.000 0.000 1.000; writing output transformation to transforms/talairach.lta... registration took 3 minutes and 36 seconds. #-------------------------------------- #@# CA Normalize Thu Feb 22 15:17:16 EST 2007 /usr/local/bin/freesurfer/subjects/GK1/mri mri_ca_normalize -mask brainmask.mgz nu.mgz/usr/local/bin/freesurfer/average/RB_all_2006- 02-15.gca transforms/talairach.lta norm.mgz using MR volume brainmask.mgz to mask input volume... reading 1 input volumes reading atlas from '/usr/local/bin/freesurfer/average/RB_all_2006-02-15.gca '... setting gca type = Normal gca type reading transform from 'transforms/talairach.lta'... INFO: compute sample coordinates transform 1.117 0.001 0.009 -18.071; 0.001 1.172 0.192 -1.635; 0.000 -0.125 0.991 13.008; 0.000 0.000 0.000 1.000; INFO: transform used reading input volume from nu.mgz... resetting wm mean[0]: 109 --> 116 resetting gm mean[0]: 68 --> 68 input volume #1 is the most T1-like using real data threshold=24.0 using (106, 68, 109) as brain centroid... mean wm in atlas = 116, using box (90,52,88) --> (121, 83,129) to find MRI wm before smoothing, mri peak at 98 after smoothing, mri peak at 96, scaling input intensities by 1.208 scaling channel 0 by 1.20833 using 242283 sample points... finding control points in Left_Cerebral_White_Matter.... found 38929 control points for structure... bounding box (128, 31, 26) --> (187, 125, 191) Left_Cerebral_White_Matter: limiting intensities to 106.0 --> 124.0 195 of 724 (26.9%) samples deleted finding control points in Right_Cerebral_White_Matter.... found 38531 control points for structure... bounding box (71, 32, 26) --> (128, 125, 192) Right_Cerebral_White_Matter: limiting intensities to 107.0 --> 122.0 330 of 725 (45.5%) samples deleted finding control points in Left_Cerebellum_White_Matter.... found 3087 control points for structure... bounding box (130, 107, 52) --> (171, 142, 105) Left_Cerebellum_White_Matter: limiting intensities to 113.0 --> 118.0 59 of 110 (53.6%) samples deleted finding control points in Right_Cerebellum_White_Matter.... found 2900 control points for structure... bounding box (87, 108, 49) --> (128, 142, 105) Right_Cerebellum_White_Matter: limiting intensities to 95.0 --> 109.0 30 of 93 (32.3%) samples deleted finding control points in Brain_Stem.... found 3414 control points for structure... bounding box (112, 94, 86) --> (142, 155, 116) Brain_Stem: limiting intensities to 91.0 --> 106.0 64 of 186 (34.4%) samples deleted using 1838 total control points for intensity normalization... bias field = 0.962 +- 0.069 0 of 1160 control points discarded writing normalized volume to norm.mgz... freeing GCA...done. normalization took 0 minutes and 44 seconds. #-------------------------------------- #@# CA Reg Thu Feb 22 15:18:01 EST 2007 /usr/local/bin/freesurfer/subjects/GK1/mri mri_ca_register -align -nobigventricles -mask brainmask.mgz -T transforms/talairach.lta norm.mgz/usr/local/bin/freesurfer/average/RB_all_2006- 02-15.gca transforms/talairach.m3z renormalizing sequences with structure alignment, equivalent to: -renormalize -regularize_mean 0.500 -regularize 0.500 not handling expanded ventricles... using MR volume brainmask.mgz to mask input volume... using previously computed transform transforms/talairach.lta reading 1 input volumes... logging results to talairach.log reading input volume 'norm.mgz'... reading GCA '/usr/local/bin/freesurfer/average/RB_all_2006-02-15.gca'... setting gca type = Normal gca type freeing gibbs priors...done. average std[0] = 5.0 label assignment complete, 0 changed (0.00%) renormalizing by structure alignment.... renormalizing input #0 aligning Left_Pallidum... gca peak = 0.11705 (100) mri peak = 0.13848 (105) Left_Pallidum (13): linear fit = 0.98 x + 0.0 (433 voxels, overlap=0.650) aligning Right_Pallidum... gca peak = 0.13501 (106) mri peak = 0.12646 (103) Right_Pallidum (52): linear fit = 0.98 x + 0.0 (703 voxels, overlap=0.936) aligning Right_Cerebral_White_Matter... gca peak = 0.05652 (116) mri peak = 0.07903 (114) Right_Cerebral_White_Matter (41): linear fit = 1.00 x + 0.0 (87439 voxels, overlap=0.552) aligning Left_Cerebral_White_Matter... gca peak = 0.05682 (116) mri peak = 0.06608 (112) Left_Cerebral_White_Matter (2): linear fit = 1.00 x + 0.0 (98179 voxels, overlap=0.498) aligning Left_Lateral_Ventricle... gca peak = 0.05153 (25) mri peak = 0.03578 (36) unreasonable value (46.9), not in range [ 0, 40] - rejecting aligning Right_Lateral_Ventricle... gca peak = 0.07557 (30) mri peak = 0.04038 (37) unreasonable value (46.5), not in range [ 0, 40] - rejecting aligning Right_Hippocampus... gca peak = 0.14805 (66) mri peak = 0.08220 (79) Right_Hippocampus (53): linear fit = 1.23 x + 0.0 (1589 voxels, overlap= 0.091) aligning Left_Hippocampus... gca peak = 0.17297 (66) mri peak = 0.07333 (81) Left_Hippocampus (17): linear fit = 1.23 x + 0.0 (1080 voxels, overlap=0.100 ) aligning Left_Cerebral_Cortex... gca peak = 0.05671 (68) mri peak = 0.02650 (89) uniform distribution in MR - rejecting arbitrary fit aligning Right_Cerebral_Cortex... gca peak = 0.06581 (70) mri peak = 0.02773 (91) uniform distribution in MR - rejecting arbitrary fit aligning Right_Caudate... gca peak = 0.07786 (81) mri peak = 0.16928 (95) Right_Caudate (50): linear fit = 1.20 x + 0.0 (1008 voxels, overlap=0.048) aligning Left_Caudate... gca peak = 0.07814 (81) mri peak = 0.11708 (92) Left_Caudate (11): linear fit = 1.12 x + 0.0 (876 voxels, overlap=0.330) aligning Left_Cerebellum_Cortex... gca peak = 0.06033 (63) mri peak = 0.04440 (84) Left_Cerebellum_Cortex (8): linear fit = 1.35 x + 0.0 (34294 voxels, overlap=0.004) aligning Right_Cerebellum_Cortex... gca peak = 0.05990 (64) mri peak = 0.04660 (85) Right_Cerebellum_Cortex (47): linear fit = 1.33 x + 0.0 (40922 voxels, overlap=0.022) aligning Left_Cerebellum_White_Matter... gca peak = 0.09299 (92) mri peak = 0.09884 (97) Left_Cerebellum_White_Matter (7): linear fit = 1.08 x + 0.0 (7197 voxels, overlap=0.337) aligning Right_Cerebellum_White_Matter... gca peak = 0.08899 (93) mri peak = 0.10077 (95) Right_Cerebellum_White_Matter (46): linear fit = 1.05 x + 0.0 (9203 voxels, overlap=0.538) aligning Left_Amygdala... gca peak = 0.13911 (71) mri peak = 0.13021 (81) Left_Amygdala (18): linear fit = 1.15 x + 0.0 (466 voxels, overlap=0.093) aligning Right_Amygdala... gca peak = 0.15703 (65) mri peak = 0.07413 (82) Right_Amygdala (54): linear fit = 1.27 x + 0.0 (621 voxels, overlap=0.009) aligning Left_Thalamus_Proper... gca peak = 0.06537 (100) mri peak = 0.11424 (101) Left_Thalamus_Proper (10): linear fit = 0.98 x + 0.0 (3570 voxels, overlap= 0.500) aligning Right_Thalamus_Proper... gca peak = 0.07019 (99) mri peak = 0.09504 (98) Right_Thalamus_Proper (49): linear fit = 0.98 x + 0.0 (3540 voxels, overlap= 0.526) aligning Left_Putamen... gca peak = 0.09468 (90) mri peak = 0.11889 (97) Left_Putamen (12): linear fit = 1.08 x + 0.0 (2476 voxels, overlap=0.278) aligning Right_Putamen... gca peak = 0.07184 (82) mri peak = 0.10599 (95) Right_Putamen (51): linear fit = 1.08 x + 0.0 (2916 voxels, overlap=0.391) aligning Brain_Stem... gca peak = 0.05597 (80) mri peak = 0.09565 (93) Brain_Stem (16): linear fit = 1.00 x + 0.0 (14224 voxels, overlap=0.438) aligning Right_VentralDC... gca peak = 0.05879 (97) mri peak = 0.08609 (102) Right_VentralDC (60): linear fit = 1.00 x + 0.0 (2104 voxels, overlap=0.454) aligning Left_VentralDC... gca peak = 0.06932 (98) mri peak = 0.10655 (100) Left_VentralDC (28): linear fit = 0.95 x + 0.0 (1910 voxels, overlap=0.480) aligning Third_Ventricle... gca peak = 0.10105 (38) mri peak = 0.07224 (35) unreasonable value (47.5), not in range [ 0, 40] - rejecting aligning Fourth_Ventricle... gca peak = 0.10426 (32) mri peak = 0.11254 (32) not using caudate to estimate GM means Fourth_Ventricle (15): linear fit = 1.12 x + 0.0 (847 voxels, overlap=0.417) estimating mean gm scale to be 1.22 x + 0.0 estimating mean wm scale to be 1.00 x + 0.0 estimating mean csf scale to be 1.12 x + 0.0 22 transforms computed 0000: dt=0.000000, rms=0.793 (0.168%), neg=0, invalid=1511, LIN 0001: dt=0.000000, rms=0.793 (0.017%), neg=0, invalid=1511, LIN rms increased - undoing step... 0002: dt=0.000000, rms=0.793 (-0.154%), neg=0, invalid=1511, LIN rms increased - undoing step... 0004: dt=0.000000, rms=0.793 (-0.176%), neg=0, invalid=1511, LIN rms increased - undoing step... 0006: dt=0.000000, rms=0.789 (-0.250%), neg=0, invalid=1511, LIN rms increased - undoing step... 0008: dt=0.000000, rms=0.789 (-0.236%), neg=0, invalid=1511, LIN rms increased - undoing step... 0010: dt=0.000000, rms=0.789 (-0.226%), neg=0, invalid=1511, LIN rms increased - undoing step... 0012: dt=0.000000, rms=0.787 (-0.184%), neg=0, invalid=1511, LIN rms increased - undoing step... 0014: dt=0.000000, rms=0.787 (-0.180%), neg=0, invalid=1511, LIN rms increased - undoing step... 0016: dt=0.000000, rms=0.787 (-0.171%), neg=0, invalid=1511, LIN rms increased - undoing step... 0018: dt=0.000000, rms=0.787 (-0.169%), neg=0, invalid=1511, LIN label assignment complete, 0 changed (0.00%) **************** pass 1 of 1 ************************ setting smoothness coefficient to 0.01 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.782, neg=0, invalid=1511 0021: dt=185.732255, rms=0.718 (8.200%), neg=0, invalid=1511 0022: dt=197.104294, rms=0.696 (3.066%), neg=0, invalid=1511 0023: dt=113.120000, rms=0.686 (1.357%), neg=0, invalid=1511 0024: dt=443.904007, rms=0.674 (1.792%), neg=0, invalid=1511 0025: dt=84.339726, rms=0.668 (0.793%), neg=0, invalid=1511 0026: dt=517.888008, rms=0.663 (0.805%), neg=0, invalid=1511 0027: dt=129.472002, rms=0.660 (0.444%), neg=0, invalid=1511 0028: dt=110.976002, rms=0.660 (0.072%), neg=0, invalid=1511 0029: dt=887.808013, rms=0.654 (0.900%), neg=0, invalid=1511 0030: dt=80.000000, rms=0.653 (0.182%), neg=0, invalid=1511 0031: dt=517.888008, rms=0.651 (0.166%), neg=0, invalid=1511 0032: dt=129.472002, rms=0.649 (0.329%), neg=0, invalid=1511 0033: dt=32.368000, rms=0.649 (0.004%), neg=0, invalid=1511 0034: dt=32.368000, rms=0.649 (0.005%), neg=0, invalid=1511 0035: dt=32.368000, rms=0.649 (0.010%), neg=0, invalid=1511 0036: dt=32.368000, rms=0.649 (0.024%), neg=0, invalid=1511 0037: dt=32.368000, rms=0.649 (0.053%), neg=0, invalid=1511 0038: dt=32.368000, rms=0.648 (0.092%), neg=0, invalid=1511 0039: dt=32.368000, rms=0.647 (0.118%), neg=0, invalid=1511 0040: dt=32.368000, rms=0.647 (0.124%), neg=0, invalid=1511 0041: dt=32.368000, rms=0.646 (0.118%), neg=0, invalid=1511 0042: dt=32.368000, rms=0.645 (0.104%), neg=0, invalid=1511 0043: dt=32.368000, rms=0.645 (0.093%), neg=0, invalid=1511 0044: dt=32.368000, rms=0.644 (0.089%), neg=0, invalid=1511 0045: dt=32.368000, rms=0.643 (0.095%), neg=0, invalid=1511 0046: dt=32.368000, rms=0.643 (0.098%), neg=0, invalid=1511 0047: dt=32.368000, rms=0.642 (0.104%), neg=0, invalid=1511 0048: dt=32.368000, rms=0.641 (0.110%), neg=0, invalid=1511 0049: dt=32.368000, rms=0.641 (0.109%), neg=0, invalid=1511 0050: dt=32.368000, rms=0.640 (0.101%), neg=0, invalid=1511 0051: dt=32.368000, rms=0.639 (0.089%), neg=0, invalid=1511 0052: dt=32.368000, rms=0.639 (0.078%), neg=0, invalid=1511 0053: dt=32.368000, rms=0.638 (0.074%), neg=0, invalid=1511 0054: dt=32.368000, rms=0.638 (0.067%), neg=0, invalid=1511 0055: dt=32.368000, rms=0.638 (0.063%), neg=0, invalid=1511 0056: dt=32.368000, rms=0.637 (0.063%), neg=0, invalid=1511 0057: dt=32.368000, rms=0.637 (0.075%), neg=0, invalid=1511 0058: dt=32.368000, rms=0.636 (0.084%), neg=0, invalid=1511 0059: dt=32.368000, rms=0.636 (0.085%), neg=0, invalid=1511 0060: dt=32.368000, rms=0.635 (0.077%), neg=0, invalid=1511 0061: dt=32.368000, rms=0.635 (0.071%), neg=0, invalid=1511 0062: dt=32.368000, rms=0.634 (0.059%), neg=0, invalid=1511 0063: dt=32.368000, rms=0.634 (0.057%), neg=0, invalid=1511 0064: dt=32.368000, rms=0.634 (0.051%), neg=0, invalid=1511 0065: dt=32.368000, rms=0.633 (0.048%), neg=0, invalid=1511 0066: dt=32.368000, rms=0.633 (0.048%), neg=0, invalid=1511 0067: dt=32.368000, rms=0.633 (0.046%), neg=0, invalid=1511 0068: dt=32.368000, rms=0.632 (0.045%), neg=0, invalid=1511 0069: dt=32.368000, rms=0.632 (0.048%), neg=0, invalid=1511 0070: dt=32.368000, rms=0.632 (0.049%), neg=0, invalid=1511 0071: dt=32.368000, rms=0.632 (0.048%), neg=0, invalid=1511 0072: dt=32.368000, rms=0.631 (0.047%), neg=0, invalid=1511 0073: dt=32.368000, rms=0.631 (0.045%), neg=0, invalid=1511 0074: dt=32.368000, rms=0.631 (0.043%), neg=0, invalid=1511 0075: dt=32.368000, rms=0.630 (0.039%), neg=0, invalid=1511 0076: dt=32.368000, rms=0.630 (0.034%), neg=0, invalid=1511 0077: dt=32.368000, rms=0.630 (0.030%), neg=0, invalid=1511 0078: dt=32.368000, rms=0.630 (0.027%), neg=0, invalid=1511 0079: dt=32.368000, rms=0.630 (0.025%), neg=0, invalid=1511 0080: dt=32.368000, rms=0.630 (0.027%), neg=0, invalid=1511 0081: dt=32.368000, rms=0.629 (0.032%), neg=0, invalid=1511 0082: dt=32.368000, rms=0.629 (0.034%), neg=0, invalid=1511 0083: dt=32.368000, rms=0.629 (0.034%), neg=0, invalid=1511 0084: dt=32.368000, rms=0.629 (0.032%), neg=0, invalid=1511 0085: dt=32.368000, rms=0.629 (0.027%), neg=0, invalid=1511 0086: dt=32.368000, rms=0.628 (0.027%), neg=0, invalid=1511 0087: dt=32.368000, rms=0.628 (0.026%), neg=0, invalid=1511 0088: dt=32.368000, rms=0.628 (0.027%), neg=0, invalid=1511 0089: dt=32.368000, rms=0.628 (0.027%), neg=0, invalid=1511 0090: dt=32.368000, rms=0.628 (0.028%), neg=0, invalid=1511 0091: dt=32.368000, rms=0.628 (0.029%), neg=0, invalid=1511 0092: dt=32.368000, rms=0.627 (0.030%), neg=0, invalid=1511 0093: dt=32.368000, rms=0.627 (0.022%), neg=0, invalid=1511 0094: dt=32.368000, rms=0.627 (0.016%), neg=0, invalid=1511 0095: dt=3551.232053, rms=0.626 (0.239%), neg=0, invalid=1511 0096: dt=32.368000, rms=0.626 (0.015%), neg=0, invalid=1511 0097: dt=32.368000, rms=0.625 (0.005%), neg=0, invalid=1511 0098: dt=32.368000, rms=0.625 (-0.010%), neg=0, invalid=1511 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=0.625, neg=0, invalid=1511 0099: dt=129.472002, rms=0.623 (0.348%), neg=0, invalid=1511 0100: dt=221.952003, rms=0.623 (0.107%), neg=0, invalid=1511 0101: dt=129.472002, rms=0.622 (0.101%), neg=0, invalid=1511 0102: dt=295.936004, rms=0.622 (0.056%), neg=0, invalid=1511 0103: dt=129.472002, rms=0.621 (0.072%), neg=0, invalid=1511 0104: dt=73.984001, rms=0.621 (0.007%), neg=0, invalid=1511 0105: dt=73.984001, rms=0.621 (0.013%), neg=0, invalid=1511 0106: dt=73.984001, rms=0.621 (0.023%), neg=0, invalid=1511 0107: dt=73.984001, rms=0.621 (0.036%), neg=0, invalid=1511 0108: dt=73.984001, rms=0.620 (0.046%), neg=0, invalid=1511 0109: dt=73.984001, rms=0.620 (0.049%), neg=0, invalid=1511 0110: dt=73.984001, rms=0.620 (0.049%), neg=0, invalid=1511 0111: dt=73.984001, rms=0.620 (0.047%), neg=0, invalid=1511 0112: dt=73.984001, rms=0.619 (0.044%), neg=0, invalid=1511 0113: dt=73.984001, rms=0.619 (0.044%), neg=0, invalid=1511 0114: dt=73.984001, rms=0.619 (0.047%), neg=0, invalid=1511 0115: dt=73.984001, rms=0.618 (0.047%), neg=0, invalid=1511 0116: dt=73.984001, rms=0.618 (0.051%), neg=0, invalid=1511 0117: dt=73.984001, rms=0.618 (0.052%), neg=0, invalid=1511 0118: dt=73.984001, rms=0.617 (0.048%), neg=0, invalid=1511 0119: dt=73.984001, rms=0.617 (0.042%), neg=0, invalid=1511 0120: dt=73.984001, rms=0.617 (0.041%), neg=0, invalid=1511 0121: dt=73.984001, rms=0.617 (0.037%), neg=0, invalid=1511 0122: dt=73.984001, rms=0.617 (0.034%), neg=0, invalid=1511 0123: dt=73.984001, rms=0.616 (0.038%), neg=0, invalid=1511 0124: dt=73.984001, rms=0.616 (0.037%), neg=0, invalid=1511 0125: dt=73.984001, rms=0.616 (0.040%), neg=0, invalid=1511 0126: dt=73.984001, rms=0.616 (0.041%), neg=0, invalid=1511 0127: dt=73.984001, rms=0.615 (0.037%), neg=0, invalid=1511 0128: dt=73.984001, rms=0.615 (0.035%), neg=0, invalid=1511 0129: dt=73.984001, rms=0.615 (0.035%), neg=0, invalid=1511 0130: dt=73.984001, rms=0.615 (0.037%), neg=0, invalid=1511 0131: dt=73.984001, rms=0.614 (0.035%), neg=0, invalid=1511 0132: dt=73.984001, rms=0.614 (0.031%), neg=0, invalid=1511 0133: dt=73.984001, rms=0.614 (0.029%), neg=0, invalid=1511 0134: dt=73.984001, rms=0.614 (0.031%), neg=0, invalid=1511 0135: dt=73.984001, rms=0.614 (0.032%), neg=0, invalid=1511 0136: dt=73.984001, rms=0.614 (0.027%), neg=0, invalid=1511 0137: dt=73.984001, rms=0.613 (0.020%), neg=0, invalid=1511 0138: dt=73.984001, rms=0.613 (0.027%), neg=0, invalid=1511 0139: dt=73.984001, rms=0.613 (0.028%), neg=0, invalid=1511 0140: dt=73.984001, rms=0.613 (0.031%), neg=0, invalid=1511 0141: dt=73.984001, rms=0.613 (0.034%), neg=0, invalid=1511 0142: dt=73.984001, rms=0.613 (0.024%), neg=0, invalid=1511 0143: dt=73.984001, rms=0.612 (0.020%), neg=0, invalid=1511 0144: dt=369.920006, rms=0.612 (0.023%), neg=0, invalid=1511 0145: dt=32.368000, rms=0.612 (0.002%), neg=0, invalid=1511 0146: dt=32.368000, rms=0.612 (-0.002%), neg=0, invalid=1511 setting smoothness coefficient to 0.03 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.614, neg=0, invalid=1511 0147: dt=29.460317, rms=0.613 (0.119%), neg=0, invalid=1511 0148: dt=70.660633, rms=0.612 (0.163%), neg=0, invalid=1511 0149: dt=331.776005, rms=0.606 (0.988%), neg=0, invalid=1511 0150: dt=39.529412, rms=0.604 (0.369%), neg=0, invalid=1511 0151: dt=145.152002, rms=0.601 (0.532%), neg=0, invalid=1511 0152: dt=41.882353, rms=0.599 (0.274%), neg=0, invalid=1511 0153: dt=89.854305, rms=0.598 (0.199%), neg=0, invalid=1511 0154: dt=42.400000, rms=0.597 (0.167%), neg=0, invalid=1511 0155: dt=91.586207, rms=0.596 (0.155%), neg=0, invalid=1511 0156: dt=42.378378, rms=0.595 (0.152%), neg=0, invalid=1511 0157: dt=74.507463, rms=0.595 (0.115%), neg=0, invalid=1511 0158: dt=48.000000, rms=0.594 (0.124%), neg=0, invalid=1511 0159: dt=61.866667, rms=0.593 (0.114%), neg=0, invalid=1511 0160: dt=48.000000, rms=0.593 (0.098%), neg=0, invalid=1511 0161: dt=66.666667, rms=0.592 (0.092%), neg=0, invalid=1511 0162: dt=39.225806, rms=0.591 (0.111%), neg=0, invalid=1511 0163: dt=82.944001, rms=0.591 (0.088%), neg=0, invalid=1511 0164: dt=45.913043, rms=0.590 (0.121%), neg=0, invalid=1511 0165: dt=73.600000, rms=0.590 (0.080%), neg=0, invalid=1511 0166: dt=36.288001, rms=0.589 (0.097%), neg=0, invalid=1511 0167: dt=124.416002, rms=0.588 (0.140%), neg=0, invalid=1511 0168: dt=25.920000, rms=0.588 (0.089%), neg=0, invalid=1511 0169: dt=580.608009, rms=0.585 (0.512%), neg=0, invalid=1511 0170: dt=34.051282, rms=0.583 (0.222%), neg=0, invalid=1511 0171: dt=37.333333, rms=0.583 (0.039%), neg=0, invalid=1511 0172: dt=37.333333, rms=0.583 (0.068%), neg=0, invalid=1511 0173: dt=37.333333, rms=0.582 (0.065%), neg=0, invalid=1511 0174: dt=37.333333, rms=0.582 (0.083%), neg=0, invalid=1511 0175: dt=37.333333, rms=0.581 (0.103%), neg=0, invalid=1511 0176: dt=37.333333, rms=0.580 (0.148%), neg=0, invalid=1511 0177: dt=37.333333, rms=0.579 (0.163%), neg=0, invalid=1511 0178: dt=37.333333, rms=0.579 (0.161%), neg=0, invalid=1511 0179: dt=37.333333, rms=0.578 (0.152%), neg=0, invalid=1511 0180: dt=37.333333, rms=0.577 (0.159%), neg=0, invalid=1511 0181: dt=37.333333, rms=0.576 (0.169%), neg=0, invalid=1511 0182: dt=37.333333, rms=0.575 (0.172%), neg=0, invalid=1511 0183: dt=37.333333, rms=0.574 (0.158%), neg=0, invalid=1511 0184: dt=37.333333, rms=0.573 (0.143%), neg=0, invalid=1511 0185: dt=37.333333, rms=0.572 (0.125%), neg=0, invalid=1511 0186: dt=37.333333, rms=0.572 (0.111%), neg=0, invalid=1511 0187: dt=37.333333, rms=0.571 (0.122%), neg=0, invalid=1511 0188: dt=37.333333, rms=0.570 (0.108%), neg=0, invalid=1511 0189: dt=37.333333, rms=0.570 (0.115%), neg=0, invalid=1511 0190: dt=37.333333, rms=0.569 (0.114%), neg=0, invalid=1511 0191: dt=37.333333, rms=0.568 (0.112%), neg=0, invalid=1511 0192: dt=37.333333, rms=0.568 (0.122%), neg=0, invalid=1511 0193: dt=37.333333, rms=0.567 (0.096%), neg=0, invalid=1511 0194: dt=37.333333, rms=0.567 (0.076%), neg=0, invalid=1511 0195: dt=37.333333, rms=0.566 (0.080%), neg=0, invalid=1511 0196: dt=37.333333, rms=0.566 (0.096%), neg=0, invalid=1511 0197: dt=37.333333, rms=0.565 (0.089%), neg=0, invalid=1511 0198: dt=37.333333, rms=0.565 (0.092%), neg=0, invalid=1511 0199: dt=37.333333, rms=0.564 (0.087%), neg=0, invalid=1511 0200: dt=37.333333, rms=0.564 (0.073%), neg=0, invalid=1511 0201: dt=37.333333, rms=0.563 (0.071%), neg=0, invalid=1511 0202: dt=37.333333, rms=0.563 (0.086%), neg=0, invalid=1511 0203: dt=37.333333, rms=0.563 (0.086%), neg=0, invalid=1511 0204: dt=37.333333, rms=0.562 (0.063%), neg=0, invalid=1511 0205: dt=37.333333, rms=0.562 (0.044%), neg=0, invalid=1511 0206: dt=37.333333, rms=0.562 (0.061%), neg=0, invalid=1511 0207: dt=37.333333, rms=0.561 (0.063%), neg=0, invalid=1511 0208: dt=37.333333, rms=0.561 (0.067%), neg=0, invalid=1511 0209: dt=37.333333, rms=0.560 (0.071%), neg=0, invalid=1511 0210: dt=37.333333, rms=0.560 (0.054%), neg=0, invalid=1511 0211: dt=37.333333, rms=0.560 (0.043%), neg=0, invalid=1511 0212: dt=37.333333, rms=0.560 (0.039%), neg=0, invalid=1511 0213: dt=37.333333, rms=0.559 (0.043%), neg=0, invalid=1511 0214: dt=37.333333, rms=0.559 (0.046%), neg=0, invalid=1511 0215: dt=37.333333, rms=0.559 (0.039%), neg=0, invalid=1511 0216: dt=37.333333, rms=0.559 (0.043%), neg=0, invalid=1511 0217: dt=37.333333, rms=0.558 (0.052%), neg=0, invalid=1511 0218: dt=37.333333, rms=0.558 (0.043%), neg=0, invalid=1511 0219: dt=37.333333, rms=0.558 (0.021%), neg=0, invalid=1511 0220: dt=37.333333, rms=0.558 (0.019%), neg=0, invalid=1511 0221: dt=248.832004, rms=0.558 (0.064%), neg=0, invalid=1511 0222: dt=1.944000, rms=0.558 (0.001%), neg=0, invalid=1511 0223: dt=1.944000, rms=0.558 (-0.000%), neg=0, invalid=1511 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=0.558, neg=0, invalid=1511 0224: dt=109.782772, rms=0.555 (0.541%), neg=0, invalid=1511 0225: dt=41.846154, rms=0.553 (0.217%), neg=0, invalid=1511 0226: dt=74.666667, rms=0.553 (0.115%), neg=0, invalid=1511 0227: dt=36.288001, rms=0.552 (0.079%), neg=0, invalid=1511 0228: dt=145.152002, rms=0.551 (0.155%), neg=0, invalid=1511 0229: dt=36.288001, rms=0.551 (0.059%), neg=0, invalid=1511 0230: dt=145.152002, rms=0.550 (0.121%), neg=0, invalid=1511 0231: dt=25.920000, rms=0.550 (0.023%), neg=0, invalid=1511 0232: dt=25.920000, rms=0.550 (0.022%), neg=0, invalid=1511 0233: dt=25.920000, rms=0.550 (0.035%), neg=0, invalid=1511 0234: dt=25.920000, rms=0.550 (0.048%), neg=0, invalid=1511 0235: dt=25.920000, rms=0.549 (0.054%), neg=0, invalid=1511 0236: dt=25.920000, rms=0.549 (0.063%), neg=0, invalid=1511 0237: dt=25.920000, rms=0.549 (0.070%), neg=0, invalid=1511 0238: dt=25.920000, rms=0.548 (0.074%), neg=0, invalid=1511 0239: dt=25.920000, rms=0.548 (0.078%), neg=0, invalid=1511 0240: dt=25.920000, rms=0.548 (0.070%), neg=0, invalid=1511 0241: dt=25.920000, rms=0.547 (0.068%), neg=0, invalid=1511 0242: dt=25.920000, rms=0.547 (0.061%), neg=0, invalid=1511 0243: dt=25.920000, rms=0.547 (0.055%), neg=0, invalid=1511 0244: dt=25.920000, rms=0.546 (0.061%), neg=0, invalid=1511 0245: dt=25.920000, rms=0.546 (0.060%), neg=0, invalid=1511 0246: dt=25.920000, rms=0.546 (0.054%), neg=0, invalid=1511 0247: dt=25.920000, rms=0.545 (0.048%), neg=0, invalid=1511 0248: dt=25.920000, rms=0.545 (0.042%), neg=0, invalid=1511 0249: dt=25.920000, rms=0.545 (0.043%), neg=0, invalid=1511 0250: dt=25.920000, rms=0.545 (0.046%), neg=0, invalid=1511 0251: dt=25.920000, rms=0.544 (0.049%), neg=0, invalid=1511 0252: dt=25.920000, rms=0.544 (0.047%), neg=0, invalid=1511 0253: dt=25.920000, rms=0.544 (0.038%), neg=0, invalid=1511 0254: dt=25.920000, rms=0.544 (0.037%), neg=0, invalid=1511 0255: dt=25.920000, rms=0.543 (0.038%), neg=0, invalid=1511 0256: dt=25.920000, rms=0.543 (0.035%), neg=0, invalid=1511 0257: dt=25.920000, rms=0.543 (0.030%), neg=0, invalid=1511 0258: dt=25.920000, rms=0.543 (0.031%), neg=0, invalid=1511 0259: dt=25.920000, rms=0.543 (0.038%), neg=0, invalid=1511 0260: dt=25.920000, rms=0.543 (0.038%), neg=0, invalid=1511 0261: dt=25.920000, rms=0.542 (0.031%), neg=0, invalid=1511 0262: dt=25.920000, rms=0.542 (0.032%), neg=0, invalid=1511 0263: dt=25.920000, rms=0.542 (0.029%), neg=0, invalid=1511 0264: dt=25.920000, rms=0.542 (0.032%), neg=0, invalid=1511 0265: dt=25.920000, rms=0.542 (0.030%), neg=0, invalid=1511 0266: dt=25.920000, rms=0.542 (0.034%), neg=0, invalid=1511 0267: dt=25.920000, rms=0.541 (0.025%), neg=0, invalid=1511 0268: dt=25.920000, rms=0.541 (0.022%), neg=0, invalid=1511 0269: dt=25.920000, rms=0.541 (0.023%), neg=0, invalid=1511 0270: dt=25.920000, rms=0.541 (0.032%), neg=0, invalid=1511 0271: dt=25.920000, rms=0.541 (0.030%), neg=0, invalid=1511 0272: dt=25.920000, rms=0.541 (0.024%), neg=0, invalid=1511 0273: dt=25.920000, rms=0.541 (0.022%), neg=0, invalid=1511 0274: dt=103.680002, rms=0.540 (0.007%), neg=0, invalid=1511 0275: dt=103.680002, rms=0.540 (0.008%), neg=0, invalid=1511 0276: dt=103.680002, rms=0.540 (-0.067%), neg=0, invalid=1511 setting smoothness coefficient to 0.12 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.552, neg=0, invalid=1511 0277: dt=8.851064, rms=0.551 (0.074%), neg=0, invalid=1511 0278: dt=8.000000, rms=0.551 (0.024%), neg=0, invalid=1511 0279: dt=8.000000, rms=0.551 (0.016%), neg=0, invalid=1511 0280: dt=8.000000, rms=0.551 (-0.007%), neg=0, invalid=1511 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=0.551, neg=0, invalid=1511 0281: dt=52.411523, rms=0.546 (0.892%), neg=0, invalid=1511 0282: dt=18.418605, rms=0.544 (0.328%), neg=0, invalid=1511 0283: dt=33.446328, rms=0.543 (0.317%), neg=0, invalid=1511 0284: dt=12.745763, rms=0.542 (0.188%), neg=0, invalid=1511 0285: dt=62.745098, rms=0.540 (0.347%), neg=0, invalid=1511 0286: dt=21.034268, rms=0.538 (0.259%), neg=0, invalid=1511 0287: dt=19.200000, rms=0.537 (0.248%), neg=0, invalid=1511 0288: dt=11.200000, rms=0.537 (0.075%), neg=0, invalid=1511 0289: dt=128.000002, rms=0.534 (0.491%), neg=0, invalid=1511 0290: dt=16.000000, rms=0.533 (0.150%), neg=0, invalid=1511 0291: dt=39.360000, rms=0.532 (0.244%), neg=0, invalid=1511 0292: dt=15.703704, rms=0.531 (0.146%), neg=0, invalid=1511 0293: dt=44.800001, rms=0.530 (0.195%), neg=0, invalid=1511 0294: dt=11.466667, rms=0.530 (0.090%), neg=0, invalid=1511 0295: dt=179.200003, rms=0.527 (0.478%), neg=0, invalid=1511 0296: dt=19.047619, rms=0.526 (0.124%), neg=0, invalid=1511 0297: dt=31.020408, rms=0.525 (0.161%), neg=0, invalid=1511 0298: dt=15.000000, rms=0.525 (0.101%), neg=0, invalid=1511 0299: dt=20.740741, rms=0.525 (0.049%), neg=0, invalid=1511 0300: dt=20.740741, rms=0.525 (0.031%), neg=0, invalid=1511 0301: dt=20.740741, rms=0.524 (0.144%), neg=0, invalid=1511 0302: dt=20.740741, rms=0.523 (0.121%), neg=0, invalid=1511 0303: dt=20.740741, rms=0.522 (0.182%), neg=0, invalid=1511 0304: dt=20.740741, rms=0.521 (0.164%), neg=0, invalid=1511 0305: dt=20.740741, rms=0.520 (0.222%), neg=0, invalid=1511 0306: dt=20.740741, rms=0.519 (0.232%), neg=0, invalid=1511 0307: dt=20.740741, rms=0.518 (0.170%), neg=0, invalid=1511 0308: dt=20.740741, rms=0.517 (0.216%), neg=0, invalid=1511 0309: dt=20.740741, rms=0.516 (0.232%), neg=0, invalid=1511 0310: dt=20.740741, rms=0.515 (0.160%), neg=0, invalid=1511 0311: dt=20.740741, rms=0.514 (0.194%), neg=0, invalid=1511 0312: dt=20.740741, rms=0.513 (0.186%), neg=0, invalid=1511 0313: dt=20.740741, rms=0.512 (0.119%), neg=0, invalid=1511 0314: dt=20.740741, rms=0.511 (0.183%), neg=0, invalid=1511 0315: dt=20.740741, rms=0.511 (0.149%), neg=0, invalid=1511 0316: dt=20.740741, rms=0.510 (0.095%), neg=0, invalid=1511 0317: dt=20.740741, rms=0.510 (0.133%), neg=0, invalid=1511 0318: dt=20.740741, rms=0.509 (0.108%), neg=0, invalid=1511 0319: dt=20.740741, rms=0.509 (0.072%), neg=0, invalid=1511 0320: dt=20.740741, rms=0.508 (0.100%), neg=0, invalid=1511 0321: dt=20.740741, rms=0.508 (0.064%), neg=0, invalid=1511 0322: dt=20.740741, rms=0.507 (0.061%), neg=0, invalid=1511 0323: dt=20.740741, rms=0.507 (0.079%), neg=0, invalid=1511 0324: dt=20.740741, rms=0.507 (0.047%), neg=0, invalid=1511 0325: dt=20.740741, rms=0.507 (0.056%), neg=0, invalid=1511 0326: dt=20.740741, rms=0.506 (0.068%), neg=0, invalid=1511 0327: dt=20.740741, rms=0.506 (0.044%), neg=0, invalid=1511 0328: dt=20.740741, rms=0.506 (0.049%), neg=0, invalid=1511 0329: dt=20.740741, rms=0.506 (0.040%), neg=0, invalid=1511 0330: dt=20.740741, rms=0.505 (0.035%), neg=0, invalid=1511 0331: dt=20.740741, rms=0.505 (0.062%), neg=0, invalid=1511 0332: dt=20.740741, rms=0.505 (0.048%), neg=0, invalid=1511 0333: dt=20.740741, rms=0.505 (0.034%), neg=0, invalid=1511 0334: dt=20.740741, rms=0.504 (0.054%), neg=0, invalid=1511 0335: dt=20.740741, rms=0.504 (0.021%), neg=0, invalid=1511 0336: dt=20.740741, rms=0.504 (0.044%), neg=0, invalid=1511 0337: dt=20.740741, rms=0.504 (0.033%), neg=0, invalid=1511 0338: dt=20.740741, rms=0.504 (0.018%), neg=0, invalid=1511 0339: dt=20.740741, rms=0.504 (0.035%), neg=0, invalid=1511 0340: dt=20.740741, rms=0.503 (0.030%), neg=0, invalid=1511 0341: dt=20.740741, rms=0.503 (0.022%), neg=0, invalid=1511 0342: dt=20.740741, rms=0.503 (0.043%), neg=0, invalid=1511 0343: dt=20.740741, rms=0.503 (0.020%), neg=0, invalid=1511 0344: dt=20.740741, rms=0.503 (0.025%), neg=0, invalid=1511 0345: dt=20.740741, rms=0.503 (0.032%), neg=0, invalid=1511 0346: dt=20.740741, rms=0.503 (0.010%), neg=0, invalid=1511 0347: dt=20.740741, rms=0.503 (0.009%), neg=0, invalid=1511 0348: dt=14.400000, rms=0.503 (0.020%), neg=0, invalid=1511 0349: dt=6.400000, rms=0.503 (0.005%), neg=0, invalid=1511 0350: dt=6.400000, rms=0.502 (0.003%), neg=0, invalid=1511 0351: dt=6.400000, rms=0.502 (0.002%), neg=0, invalid=1511 0352: dt=6.400000, rms=0.502 (0.004%), neg=0, invalid=1511 0353: dt=6.400000, rms=0.502 (0.006%), neg=0, invalid=1511 0354: dt=6.400000, rms=0.502 (0.006%), neg=0, invalid=1511 setting smoothness coefficient to 0.40 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.525, neg=0, invalid=1511 0355: dt=0.000000, rms=0.525 (-0.001%), neg=0, invalid=1511 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=0.525, neg=0, invalid=1511 0356: dt=0.000000, rms=0.525 (0.000%), neg=0, invalid=1511 setting smoothness coefficient to 1.00 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.571, neg=0, invalid=1511 0357: dt=1.096026, rms=0.568 (0.531%), neg=0, invalid=1511 0358: dt=0.256000, rms=0.568 (0.012%), neg=0, invalid=1511 0359: dt=0.256000, rms=0.568 (-0.010%), neg=0, invalid=1511 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=0.568, neg=0, invalid=1511 0360: dt=0.820896, rms=0.567 (0.131%), neg=0, invalid=1511 0361: dt=0.256000, rms=0.567 (0.007%), neg=0, invalid=1511 0362: dt=0.256000, rms=0.567 (-0.004%), neg=0, invalid=1511 resetting metric properties... setting smoothness coefficient to 2.00 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.512, neg=0, invalid=1511 0363: dt=0.448000, rms=0.497 (2.912%), neg=0, invalid=1511 0364: dt=0.448000, rms=0.495 (0.412%), neg=0, invalid=1511 0365: dt=0.448000, rms=0.493 (0.311%), neg=0, invalid=1511 0366: dt=0.320000, rms=0.493 (0.134%), neg=0, invalid=1511 0367: dt=0.028000, rms=0.493 (0.009%), neg=0, invalid=1511 0368: dt=0.028000, rms=0.493 (0.010%), neg=0, invalid=1511 0369: dt=0.028000, rms=0.493 (0.017%), neg=0, invalid=1511 0370: dt=0.028000, rms=0.492 (0.024%), neg=0, invalid=1511 0371: dt=0.028000, rms=0.492 (0.031%), neg=0, invalid=1511 0372: dt=0.028000, rms=0.492 (0.035%), neg=0, invalid=1511 0373: dt=0.028000, rms=0.492 (0.039%), neg=0, invalid=1511 0374: dt=0.028000, rms=0.492 (0.042%), neg=0, invalid=1511 0375: dt=0.028000, rms=0.491 (0.042%), neg=0, invalid=1511 0376: dt=0.028000, rms=0.491 (0.043%), neg=0, invalid=1511 0377: dt=0.028000, rms=0.491 (0.042%), neg=0, invalid=1511 0378: dt=0.028000, rms=0.491 (0.042%), neg=0, invalid=1511 0379: dt=0.028000, rms=0.491 (0.040%), neg=0, invalid=1511 0380: dt=0.028000, rms=0.490 (0.040%), neg=0, invalid=1511 0381: dt=0.028000, rms=0.490 (0.035%), neg=0, invalid=1511 0382: dt=0.028000, rms=0.490 (0.034%), neg=0, invalid=1511 0383: dt=0.028000, rms=0.490 (0.029%), neg=0, invalid=1511 0384: dt=0.028000, rms=0.490 (0.029%), neg=0, invalid=1511 0385: dt=0.028000, rms=0.490 (0.026%), neg=0, invalid=1511 0386: dt=0.028000, rms=0.490 (0.023%), neg=0, invalid=1511 0387: dt=0.028000, rms=0.489 (0.020%), neg=0, invalid=1511 0388: dt=0.000015, rms=0.489 (-0.001%), neg=0, invalid=1511 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=0.489, neg=0, invalid=1511 0389: dt=0.448000, rms=0.483 (1.320%), neg=0, invalid=1511 0390: dt=0.320000, rms=0.483 (0.058%), neg=0, invalid=1511 0391: dt=0.031250, rms=0.483 (0.005%), neg=0, invalid=1511 0392: dt=0.031250, rms=0.483 (0.004%), neg=0, invalid=1511 0393: dt=0.031250, rms=0.483 (0.009%), neg=0, invalid=1511 0394: dt=0.031250, rms=0.483 (0.012%), neg=0, invalid=1511 0395: dt=0.031250, rms=0.483 (0.004%), neg=0, invalid=1511 0396: dt=0.031250, rms=0.483 (0.008%), neg=0, invalid=1511 0397: dt=0.031250, rms=0.482 (0.010%), neg=0, invalid=1511 0398: dt=0.031250, rms=0.482 (0.014%), neg=0, invalid=1511 0399: dt=0.031250, rms=0.482 (0.015%), neg=0, invalid=1511 0400: dt=0.031250, rms=0.482 (0.017%), neg=0, invalid=1511 0401: dt=0.015625, rms=0.482 (0.001%), neg=0, invalid=1511 0402: dt=0.096000, rms=0.482 (0.008%), neg=0, invalid=1511 0403: dt=0.020000, rms=0.482 (0.003%), neg=0, invalid=1511 label assignment complete, 0 changed (0.00%) ***************** morphing with label term set to 0 ******************************* label assignment complete, 0 changed (0.00%) **************** pass 1 of 1 ************************ setting smoothness coefficient to 0.01 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.480, neg=0, invalid=1511 0404: dt=2.023000, rms=0.480 (-0.069%), neg=0, invalid=1511 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=0.480, neg=0, invalid=1511 0405: dt=129.472002, rms=0.480 (0.189%), neg=0, invalid=1511 0406: dt=110.976002, rms=0.479 (0.045%), neg=0, invalid=1511 0407: dt=110.976002, rms=0.479 (0.039%), neg=0, invalid=1511 0408: dt=110.976002, rms=0.479 (0.044%), neg=0, invalid=1511 0409: dt=110.976002, rms=0.479 (0.041%), neg=0, invalid=1511 0410: dt=110.976002, rms=0.479 (0.020%), neg=0, invalid=1511 0411: dt=110.976002, rms=0.479 (0.015%), neg=0, invalid=1511 0412: dt=129.472002, rms=0.478 (0.024%), neg=0, invalid=1511 0413: dt=73.984001, rms=0.478 (0.006%), neg=0, invalid=1511 0414: dt=73.984001, rms=0.478 (0.007%), neg=0, invalid=1511 0415: dt=73.984001, rms=0.478 (0.010%), neg=0, invalid=1511 0416: dt=73.984001, rms=0.478 (0.012%), neg=0, invalid=1511 0417: dt=73.984001, rms=0.478 (0.011%), neg=0, invalid=1511 0418: dt=73.984001, rms=0.478 (0.011%), neg=0, invalid=1511 setting smoothness coefficient to 0.03 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.478, neg=0, invalid=1511 0419: dt=0.000000, rms=0.478 (0.000%), neg=0, invalid=1511 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=0.478, neg=0, invalid=1511 0420: dt=145.152002, rms=0.476 (0.360%), neg=0, invalid=1511 0421: dt=82.944001, rms=0.476 (0.107%), neg=0, invalid=1511 0422: dt=46.545455, rms=0.476 (0.067%), neg=0, invalid=1511 0423: dt=82.944001, rms=0.475 (0.074%), neg=0, invalid=1511 0424: dt=44.800000, rms=0.475 (0.046%), neg=0, invalid=1511 0425: dt=44.800000, rms=0.475 (0.046%), neg=0, invalid=1511 0426: dt=44.800000, rms=0.475 (0.056%), neg=0, invalid=1511 0427: dt=44.800000, rms=0.474 (0.077%), neg=0, invalid=1511 0428: dt=44.800000, rms=0.474 (0.072%), neg=0, invalid=1511 0429: dt=22.400000, rms=0.474 (0.019%), neg=0, invalid=1511 0430: dt=11.200000, rms=0.474 (0.005%), neg=0, invalid=1511 0431: dt=9.072000, rms=0.474 (0.004%), neg=0, invalid=1511 0432: dt=1.134000, rms=0.474 (0.000%), neg=0, invalid=1511 0433: dt=0.567000, rms=0.474 (0.000%), neg=0, invalid=1511 0434: dt=0.283500, rms=0.474 (0.000%), neg=0, invalid=1511 setting smoothness coefficient to 0.12 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.474, neg=0, invalid=1511 0435: dt=0.002734, rms=0.474 (0.000%), neg=0, invalid=1511 0436: dt=0.000488, rms=0.474 (0.000%), neg=0, invalid=1511 0437: dt=0.000031, rms=0.474 (0.000%), neg=0, invalid=1511 0438: dt=0.000015, rms=0.474 (0.000%), neg=0, invalid=1511 0439: dt=0.000004, rms=0.474 (-0.000%), neg=0, invalid=1511 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=0.474, neg=0, invalid=1511 0440: dt=0.000000, rms=0.474 (0.000%), neg=0, invalid=1511 setting smoothness coefficient to 0.40 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.475, neg=0, invalid=1511 0441: dt=0.000000, rms=0.475 (0.000%), neg=0, invalid=1511 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=0.475, neg=0, invalid=1511 0442: dt=0.000000, rms=0.475 (0.000%), neg=0, invalid=1511 setting smoothness coefficient to 1.00 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.476, neg=0, invalid=1511 0443: dt=0.000000, rms=0.476 (0.000%), neg=0, invalid=1511 blurring input image with Gaussian with sigma=0.500... 0000: dt=0.000, rms=0.476, neg=0, invalid=1511 0444: dt=0.000000, rms=0.476 (0.000%), neg=0, invalid=1511 resetting metric properties... setting smoothness coefficient to 2.00 blurring input image with Gaussian with sigma=2.000... 0000: dt=0.000, rms=0.474, neg=0, invalid=1511 0445: dt=0.080000, rms=0.471 (0.499%), neg=0, invalid=1Segmentation fault ERROR: mri_ca_register with non-zero status

3 2

mri_convert fails for enhanced MR IOD DICOMs
by Martin Kavec 23 Feb '07

23 Feb '07

Hi, our MR scanner produces DICOMs with enhance MR IOD, i.e. multiframe image file. When I try to use this format with FreeSurfer mri_convert fails. I assume it is not designed for this type of images, isn't it? Anybody knows a workaround? Thanks in advance for help (and great software), Martin

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Freesurfer February 2007