Freesurfer July 2009

freesurfer@nmr.mgh.harvard.edu

85 participants
135 discussions

Fornix Creating Surface
by True Price 13 Jul '09

13 Jul '09

So we're having a problem with the segmentation of the fornix. It is currently creating a cortical surface (i.e. a separation between the main and pial surfaces) despite removing the fornix from the brainmask. We have also tried filling in the white matter mask without any improvement. Do you have any suggestions on how to fix this? We have included a couple of pictures for your reference- the brainmask (with the deleted fornix) with the main and pial surfaces visible, and the corresponding white matter mask. Thanks for your response, True Price Summer Intern- King Lab

2 1

recon-all taking long time
by Barnali Basu 13 Jul '09

13 Jul '09

Experts, I have been working on Freesurfer last 5 months and initially the recon processes were within normal time limits. However, I am working on a different set of subjects now and its consuming days together. For my current data,autorecon1 is fine, it stops at a particular point in autorecon2 for infinity. Any suggestions would be helpful. I am pasting the autorecon2 process here: recon-all -autorecon2 -subjid ja Subject Stamp: freesurfer-Linux-centos4-stable-pub-v4.1.0 Current Stamp: freesurfer-Linux-centos4-stable-pub-v4.1.0 INFO: SUBJECTS_DIR is /usr/local/freesurfer/subjects Actual FREESURFER_HOME /usr/local/freesurfer -rw-rw-r-- 1 barnali barnali 257784 Jul 9 10:32 /usr/local/freesurfer/subjects/ja/scripts/recon-all.log Linux barnali-work 2.6.24-16-server #1 SMP Thu Apr 10 13:58:00 UTC 2008 i686 GNU/Linux #------------------------------------- #@# EM Registration Thu Jul 9 10:35:21 PDT 2009 /usr/local/freesurfer/subjects/ja/mri mri_em_register -mask brainmask.mgz nu.mgz /usr/local/freesurfer/average/RB_all_2008-03-26.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/freesurfer/average/RB_all_2008-03-26.gca'... average std = 6.9 using min determinant for regularization = 4.7 0 singular and 1812 ill-conditioned covariance matrices regularized reading 'nu.mgz'... freeing gibbs priors...done. bounding unknown intensity as < 14.9 or > 790.2 total sample mean = 84.0 (478 zeros) ************************************************ spacing=8, using 2185 sample points, tol=1.00e-05... ************************************************ register_mri: find_optimal_transform find_optimal_transform: nsamples 2185, passno 0, spacing 8 resetting wm mean[0]: 102 --> 107 resetting gm mean[0]: 64 --> 64 input volume #1 is the most T1-like using real data threshold=8.0 using (107, 91, 112) as brain centroid... mean wm in atlas = 107, using box (90,77,91) --> (123, 104,132) to find MRI wm before smoothing, mri peak at 137 after smoothing, mri peak at 151, scaling input intensities by 0.709 scaling channel 0 by 0.708609 initial log_p = -92132.8 ************************************************ First Search limited to translation only. ************************************************ Found translation: (-2.8, -1.7, 1.7): log p = -39503.6 **************************************** Nine parameter search. iteration 0 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-37619.9, old_max_log_p =-39503.6 (thresh=-39464.1) 1.062 0.000 0.000 -10.944; 0.000 1.053 0.139 -22.515; 0.000 -0.131 0.991 18.328; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 1 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-35219.5, old_max_log_p =-37619.9 (thresh=-37582.3) 1.062 0.000 0.000 -10.944; 0.000 1.266 0.027 -29.117; 0.000 0.025 1.003 -1.688; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 2 nscales = 0 ... **************************************** Result so far: scale 1.000: max_log_p=-35219.5, old_max_log_p =-35219.5 (thresh=-35184.3) 1.062 0.000 0.000 -10.944; 0.000 1.266 0.027 -29.117; 0.000 0.025 1.003 -1.688; 0.000 0.000 0.000 1.000; reducing scale to 0.2500 **************************************** Nine parameter search. iteration 3 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-32338.2, old_max_log_p =-35219.5 (thresh=-35184.3) 1.096 0.000 0.000 -15.249; 0.000 1.208 0.026 -25.944; 0.000 0.026 1.035 -8.782; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 4 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-31970.6, old_max_log_p =-32338.2 (thresh=-32305.8) 1.096 0.000 0.000 -17.124; 0.000 1.189 0.025 -21.806; 0.000 0.026 1.035 -6.907; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 5 nscales = 1 ... **************************************** Result so far: scale 0.250: max_log_p=-31970.6, old_max_log_p =-31970.6 (thresh=-31938.6) 1.096 0.000 0.000 -17.124; 0.000 1.189 0.025 -21.806; 0.000 0.026 1.035 -6.907; 0.000 0.000 0.000 1.000; reducing scale to 0.0625 **************************************** Nine parameter search. iteration 6 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-31028.4, old_max_log_p =-31970.6 (thresh=-31938.6) 1.083 0.000 0.000 -14.039; 0.000 1.194 0.025 -22.828; 0.000 0.026 1.039 -7.345; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 7 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-30899.3, old_max_log_p =-31028.4 (thresh=-30997.4) 1.079 0.000 0.000 -13.957; 0.000 1.194 0.025 -22.828; 0.000 0.026 1.043 -7.785; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 8 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-30683.0, old_max_log_p =-30899.3 (thresh=-30868.4) 1.070 0.000 0.000 -12.858; 0.000 1.194 0.025 -22.828; 0.000 0.026 1.039 -7.344; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 9 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-30108.0, old_max_log_p =-30683.0 (thresh=-30652.3) 1.058 0.000 0.000 -11.225; 0.000 1.194 0.025 -22.828; 0.000 0.026 1.051 -9.140; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 10 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-29914.1, old_max_log_p =-30108.0 (thresh=-30077.9) 1.058 0.000 0.000 -11.225; 0.000 1.180 0.025 -21.172; 0.000 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000; **************************************** Nine parameter search. iteration 11 nscales = 2 ... **************************************** Result so far: scale 0.062: max_log_p=-29914.1, old_max_log_p =-29914.1 (thresh=-29884.1) 1.058 0.000 0.000 -11.225; 0.000 1.180 0.025 -21.172; 0.000 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000; min search scale 0.025000 reached *********************************************** Computing MAP estimate using 2185 samples... *********************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-05 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.05773 0.00000 0.00000 -11.22528; 0.00000 1.17956 0.02501 -21.17203; 0.00000 0.02668 1.05912 -10.03400; 0.00000 0.00000 0.00000 1.00000; nsamples 2185 Quasinewton: input matrix 1.05773 0.00000 0.00000 -11.22528; 0.00000 1.17956 0.02501 -21.17203; 0.00000 0.02668 1.05912 -10.03400; 0.00000 0.00000 0.00000 1.00000; v3p/netlib/opt/lbfgs.c: lb3_1.lp > 0 outof QuasiNewtonEMA: 012: -log(p) = 29914.1 tol 0.000010 Resulting transform: 1.058 0.000 0.000 -11.225; 0.000 1.180 0.025 -21.172; 0.000 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000; pass 1, spacing 8: log(p) = -29914.1 (old=-92132.8) transform before final EM align: 1.058 0.000 0.000 -11.225; 0.000 1.180 0.025 -21.172; 0.000 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000; ************************************************** EM alignment process ... Computing final MAP estimate using 244171 samples. ************************************************** dt = 5.00e-06, momentum=0.80, tol=1.00e-07 l_intensity = 1.0000 Aligning input volume to GCA... Transform matrix 1.05773 0.00000 0.00000 -11.22528; 0.00000 1.17956 0.02501 -21.17203; 0.00000 0.02668 1.05912 -10.03400; 0.00000 0.00000 0.00000 1.00000; nsamples 244171 Quasinewton: input matrix 1.05773 0.00000 0.00000 -11.22528; 0.00000 1.17956 0.02501 -21.17203; 0.00000 0.02668 1.05912 -10.03400; 0.00000 0.00000 0.00000 1.00000; dfp_em_step_func: 011: -log(p) = 3772889.8 after pass:transform: ( 1.07, 0.01, 0.01, -11.23) ( 0.00, 1.18, 0.03, -21.17) ( 0.00, 0.03, 1.06, -10.03) v3p/netlib/opt/lbfgs.c: lb3_1.lp > 0 pass 2 through quasi-newton minimization... v3p/netlib/opt/lbfgs.c: lb3_1.lp > 0 outof QuasiNewtonEMA: 013: -log(p) = 3772889.8 tol 0.000000 final transform: 1.067 0.008 0.009 -11.225; 0.001 1.180 0.027 -21.172; 0.001 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000; writing output transformation to transforms/talairach.lta... registration took 33 minutes and 19 seconds. #-------------------------------------- #@# CA Normalize Thu Jul 9 11:08:40 PDT 2009 /usr/local/freesurfer/subjects/ja/mri mri_ca_normalize -mask brainmask.mgz nu.mgz /usr/local/freesurfer/average/RB_all_2008-03-26.gca transforms/talairach.lta norm.mgz using MR volume brainmask.mgz to mask input volume... reading 1 input volumes reading atlas from '/usr/local/freesurfer/average/RB_all_2008-03-26.gca'... reading transform from 'transforms/talairach.lta'... reading input volume from nu.mgz... resetting wm mean[0]: 102 --> 107 resetting gm mean[0]: 64 --> 64 input volume #1 is the most T1-like using real data threshold=8.0 using (107, 91, 112) as brain centroid... mean wm in atlas = 107, using box (90,77,91) --> (123, 104,132) to find MRI wm before smoothing, mri peak at 137 after smoothing, mri peak at 151, scaling input intensities by 0.709 scaling channel 0 by 0.708609 using 244171 sample points... INFO: compute sample coordinates transform 1.067 0.008 0.009 -11.225; 0.001 1.180 0.027 -21.172; 0.001 0.027 1.059 -10.034; 0.000 0.000 0.000 1.000; INFO: transform used finding control points in Left_Cerebral_White_Matter.... found 41584 control points for structure... bounding box (125, 59, 30) --> (189, 159, 190) It stops here and doesnt process any further. I tried twice, with same result. I work on a dual core Ubuntu 5GB machine Thanks Barnali

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Fornix Creating Surface
by True Price 13 Jul '09

13 Jul '09

1 0

Sobel Filter
by jentis＠nmr.mgh.harvard.edu 13 Jul '09

13 Jul '09

Hi Team, Does Freeview have a gradient filter (such as a sobel filter) or any kind of operator? Thanks! Best, Jon

1 0

graymid surfaces
by Jakob Heinzle 13 Jul '09

13 Jul '09

Dear freesurfer experts, I would like to create (in addition to the pial and white surfaces) the middle between the two. I know that I can do it by using the -graymid options. There are 2 questions I have with respect to that option. How can I do it without having to recalculate the other surfaces, which seems to happen, if I run e.g. " mris_make_surfaces -graymid -MGZ subject_id lh/rh"? Is there a away of telling recon-all to directly calculate the graymid surface? Thanks, Jakob -- ---------------------------------- Jakob Heinzle, Dr. sc. nat. (PhD) Bernstein Center for Computational Neuroscience Philippstrasse 13 Haus 6 D - 10115 Berlin e-mail: jakob.heinzle(a)bccn-berlin.de Phone: +49 (0) 30 2093 6767 Fax: +49 (0) 30 2093 6771 ----------------------------------

2 1

A question about parcellation of a new template
by Xing Wang 13 Jul '09

13 Jul '09

Dear freesurfer experts, I have several new templates established based on 1,000 MRI images of Chinese volunteers. I want to use freesurfer to do parcellation on these templates. Could you tell me how to do the processing or if you could give me some advices. Thanks a lot! Cindy Department of Radiology, XuanWu Hospital, Beijing ___________________________________________________________ 好玩贺卡等你发，邮箱贺卡全新上线！ http://card.mail.cn.yahoo.com/

2 1

parcellation questions
by Xing Wang 13 Jul '09

13 Jul '09

1 0

autorecon2-error
by whblqh＠mail.ustc.edu.cn 12 Jul '09

12 Jul '09

Hello,everyone I test freesurfer on my computer and demand "recon-all -s bert -autorecon2",but there were some errors,as follows: ........ #-------------------------------------------- #@# Cortical ribbon mask Mon Jul 13 02:59:19 CST 2009 /home/whb/soft/freesurfer/subjects/bert/mri mris_volmask --label_left_white 2 --label_left_ribbon 3 --label_right_white 41 --label_right_ribbon 42 --save_ribbon --save_distance bert mris_volmask.bin: error while loading shared libraries: /home/whb/soft/freesurfer/lib/vtk/lib/vtk-5.2/libvtkRendering.so.5.2: cannot restore segment prot after reloc: Permission denied Linux localhost.localdomain 2.6.21-1.3194.fc7 #1 SMP Wed May 23 22:35:01 EDT 2007 i686 i686 i386 GNU/Linux recon-all exited with ERRORS at Mon Jul 13 02:59:20 CST 2009 I don't know what's the reason. would you like to help me£¿ Thanks in advance. Hai-bao Wang Jul-12, 2009

1 0

functional ROI
by Krajcsi Attila 11 Jul '09

11 Jul '09

Hi, If I want to run a functional ROI according to this: http://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/MultiModalFmriGroup#Gro… , is it possible to have the subjects' name in the output file? Is it possible that the volume of an unmasked region (region defined only by anatomical label and not by functional statistics) is larger than the volume of the structure itself (found in aseg.stat)? Did I miss something? Thank you, Attila

2 1

cannot read file as type 3
by Lars M. Rimol 11 Jul '09

11 Jul '09

Hi, What does it mean when mris_surf2surf produces the error message that the input file cannot be read "as type 3" ? The file is an .mgh which I converted from an .mgz using mri_convert (mri_convert oldfile.mgz newfile.mgh). I get the same error when I use the .mgz file. Thank you! yours, LMR

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Freesurfer July 2009