Freesurfer May 2009

freesurfer@nmr.mgh.harvard.edu

81 participants
126 discussions

mri_convert: correspondence between structure values when I convert to minc file
by Paula Diniz 15 May '09

15 May '09

Hello! I have to convert the aseg.mgz file to a minc file, but I lost the structure values reference. Where I can find a convertion table or how I make to recover the structures values reference? Thanks! -- ================================== Paula Rejane Beserra Diniz Bacharel em Informática Biomédica Mestre em Neurociências Doutoranda em Neurociências Universidade de São Paulo Hospital das Clínicas - Ribeirão Preto ==================================

3 4

mri_ca_train on primate data
by Kevin Scheibel 15 May '09

15 May '09

Hello, I am preparing a dataset of 42 segmented vervet brains to make an aseg atlas using mri_ca_train. A couple of quick questions: 1. Does every voxel need to be labeled for mri_ca_train to create a robust atlas? E.g., we have not manually segmented out white matter or cerebral cortex in our brains. Is this necessary? 2. Can I create a .gca file with mri_ca_train that can then be used by mri_ca_label to label a novel dataset without having to use rebuild_gca_atlas.csh? Thanks! Kevin Scheibel k.scheibel(a)ucla.edu

1 0

Re: zero determinant error in using mri_convert rawavg.mgz orig.mgz --conform
by John Drozd 15 May '09

15 May '09

> Hello, > > Here is the ouput from bugr: > > FREESURFER_HOME: /trumpet/downloads/FreeSurfer/freesurfer > > Build stamp: freesurfer-Linux-centos4_x86_64-stable-pub-v4.3.0 > > RedHat release: Fedora release 10 (Cambridge) > > Kernel info: Linux 2.6.27.21-170.2.56.fc10.x86_64 x86_64 > > I have sets of dicom files such as: > > [jdrozd@trumpet orig]$ ls > 70731171155016_S32496_I63397_0.dcm* 70731171155016_S32496_I63397_25.dcm* > 70731171155016_S32496_I63397_100.dcm* 70731171155016_S32496_I63397_26.dcm* > 70731171155016_S32496_I63397_101.dcm* 70731171155016_S32496_I63397_27.dcm* > 70731171155016_S32496_I63397_102.dcm* 70731171155016_S32496_I63397_28.dcm* > 70731171155016_S32496_I63397_103.dcm* 70731171155016_S32496_I63397_29.dcm* > 70731171155016_S32496_I63397_104.dcm* 70731171155016_S32496_I63397_2.dcm* > 70731171155016_S32496_I63397_105.dcm* 70731171155016_S32496_I63397_30.dcm* > 70731171155016_S32496_I63397_106.dcm* 70731171155016_S32496_I63397_31.dcm* > 70731171155016_S32496_I63397_107.dcm* 70731171155016_S32496_I63397_32.dcm* > 70731171155016_S32496_I63397_108.dcm* 70731171155016_S32496_I63397_33.dcm* > 70731171155016_S32496_I63397_109.dcm* 70731171155016_S32496_I63397_34.dcm* > 70731171155016_S32496_I63397_10.dcm* 70731171155016_S32496_I63397_35.dcm* > 70731171155016_S32496_I63397_110.dcm* 70731171155016_S32496_I63397_36.dcm* > 70731171155016_S32496_I63397_111.dcm* 70731171155016_S32496_I63397_37.dcm* > 70731171155016_S32496_I63397_112.dcm* 70731171155016_S32496_I63397_38.dcm* > 70731171155016_S32496_I63397_113.dcm* 70731171155016_S32496_I63397_39.dcm* > 70731171155016_S32496_I63397_114.dcm* 70731171155016_S32496_I63397_3.dcm* > 70731171155016_S32496_I63397_115.dcm* 70731171155016_S32496_I63397_40.dcm* > 70731171155016_S32496_I63397_116.dcm* 70731171155016_S32496_I63397_41.dcm* > 70731171155016_S32496_I63397_117.dcm* 70731171155016_S32496_I63397_42.dcm* > 70731171155016_S32496_I63397_118.dcm* 70731171155016_S32496_I63397_43.dcm* > 70731171155016_S32496_I63397_119.dcm* 70731171155016_S32496_I63397_44.dcm* > 70731171155016_S32496_I63397_11.dcm* 70731171155016_S32496_I63397_45.dcm* > 70731171155016_S32496_I63397_120.dcm* 70731171155016_S32496_I63397_46.dcm* > 70731171155016_S32496_I63397_121.dcm* 70731171155016_S32496_I63397_47.dcm* > 70731171155016_S32496_I63397_122.dcm* 70731171155016_S32496_I63397_48.dcm* > 70731171155016_S32496_I63397_123.dcm* 70731171155016_S32496_I63397_49.dcm* > 70731171155016_S32496_I63397_124.dcm* 70731171155016_S32496_I63397_4.dcm* > 70731171155016_S32496_I63397_125.dcm* 70731171155016_S32496_I63397_50.dcm* > 70731171155016_S32496_I63397_126.dcm* 70731171155016_S32496_I63397_51.dcm* > 70731171155016_S32496_I63397_127.dcm* 70731171155016_S32496_I63397_52.dcm* > 70731171155016_S32496_I63397_128.dcm* 70731171155016_S32496_I63397_53.dcm* > 70731171155016_S32496_I63397_129.dcm* 70731171155016_S32496_I63397_54.dcm* > 70731171155016_S32496_I63397_12.dcm* 70731171155016_S32496_I63397_55.dcm* > 70731171155016_S32496_I63397_130.dcm* 70731171155016_S32496_I63397_56.dcm* > 70731171155016_S32496_I63397_131.dcm* 70731171155016_S32496_I63397_57.dcm* > 70731171155016_S32496_I63397_132.dcm* 70731171155016_S32496_I63397_58.dcm* > 70731171155016_S32496_I63397_133.dcm* 70731171155016_S32496_I63397_59.dcm* > 70731171155016_S32496_I63397_134.dcm* 70731171155016_S32496_I63397_5.dcm* > 70731171155016_S32496_I63397_135.dcm* 70731171155016_S32496_I63397_60.dcm* > 70731171155016_S32496_I63397_136.dcm* 70731171155016_S32496_I63397_61.dcm* > 70731171155016_S32496_I63397_137.dcm* 70731171155016_S32496_I63397_62.dcm* > 70731171155016_S32496_I63397_138.dcm* 70731171155016_S32496_I63397_63.dcm* > 70731171155016_S32496_I63397_139.dcm* 70731171155016_S32496_I63397_64.dcm* > 70731171155016_S32496_I63397_13.dcm* 70731171155016_S32496_I63397_65.dcm* > 70731171155016_S32496_I63397_140.dcm* 70731171155016_S32496_I63397_66.dcm* > 70731171155016_S32496_I63397_141.dcm* 70731171155016_S32496_I63397_67.dcm* > 70731171155016_S32496_I63397_142.dcm* 70731171155016_S32496_I63397_68.dcm* > 70731171155016_S32496_I63397_143.dcm* 70731171155016_S32496_I63397_69.dcm* > 70731171155016_S32496_I63397_144.dcm* 70731171155016_S32496_I63397_6.dcm* > 70731171155016_S32496_I63397_145.dcm* 70731171155016_S32496_I63397_70.dcm* > 70731171155016_S32496_I63397_146.dcm* 70731171155016_S32496_I63397_71.dcm* > 70731171155016_S32496_I63397_147.dcm* 70731171155016_S32496_I63397_72.dcm* > 70731171155016_S32496_I63397_148.dcm* 70731171155016_S32496_I63397_73.dcm* > 70731171155016_S32496_I63397_149.dcm* 70731171155016_S32496_I63397_74.dcm* > 70731171155016_S32496_I63397_14.dcm* 70731171155016_S32496_I63397_75.dcm* > 70731171155016_S32496_I63397_150.dcm* 70731171155016_S32496_I63397_76.dcm* > 70731171155016_S32496_I63397_151.dcm* 70731171155016_S32496_I63397_77.dcm* > 70731171155016_S32496_I63397_152.dcm* 70731171155016_S32496_I63397_78.dcm* > 70731171155016_S32496_I63397_153.dcm* 70731171155016_S32496_I63397_79.dcm* > 70731171155016_S32496_I63397_154.dcm* 70731171155016_S32496_I63397_7.dcm* > 70731171155016_S32496_I63397_155.dcm* 70731171155016_S32496_I63397_80.dcm* > 70731171155016_S32496_I63397_156.dcm* 70731171155016_S32496_I63397_81.dcm* > 70731171155016_S32496_I63397_157.dcm* 70731171155016_S32496_I63397_82.dcm* > 70731171155016_S32496_I63397_158.dcm* 70731171155016_S32496_I63397_83.dcm* > 70731171155016_S32496_I63397_159.dcm* 70731171155016_S32496_I63397_84.dcm* > 70731171155016_S32496_I63397_15.dcm* 70731171155016_S32496_I63397_85.dcm* > 70731171155016_S32496_I63397_160.dcm* 70731171155016_S32496_I63397_86.dcm* > 70731171155016_S32496_I63397_161.dcm* 70731171155016_S32496_I63397_87.dcm* > 70731171155016_S32496_I63397_162.dcm* 70731171155016_S32496_I63397_88.dcm* > 70731171155016_S32496_I63397_163.dcm* 70731171155016_S32496_I63397_89.dcm* > 70731171155016_S32496_I63397_164.dcm* 70731171155016_S32496_I63397_8.dcm* > 70731171155016_S32496_I63397_165.dcm* 70731171155016_S32496_I63397_90.dcm* > 70731171155016_S32496_I63397_16.dcm* 70731171155016_S32496_I63397_91.dcm* > 70731171155016_S32496_I63397_17.dcm* 70731171155016_S32496_I63397_92.dcm* > 70731171155016_S32496_I63397_18.dcm* 70731171155016_S32496_I63397_93.dcm* > 70731171155016_S32496_I63397_19.dcm* 70731171155016_S32496_I63397_94.dcm* > 70731171155016_S32496_I63397_1.dcm* 70731171155016_S32496_I63397_95.dcm* > 70731171155016_S32496_I63397_20.dcm* 70731171155016_S32496_I63397_96.dcm* > 70731171155016_S32496_I63397_21.dcm* 70731171155016_S32496_I63397_97.dcm* > 70731171155016_S32496_I63397_22.dcm* 70731171155016_S32496_I63397_98.dcm* > 70731171155016_S32496_I63397_23.dcm* 70731171155016_S32496_I63397_99.dcm* > 70731171155016_S32496_I63397_24.dcm* 70731171155016_S32496_I63397_9.dcm* > > I first converted these files to an mgz file using: > > [jdrozd@trumpet orig]$ mri_convert -it dicom > 70731171155016_S32496_I63397_0.dcm T1.mgz > mri_convert -it dicom 70731171155016_S32496_I63397_0.dcm T1.mgz > $Id: mri_convert.c,v 1.146.2.4 2009/01/30 02:30:42 nicks Exp $ > reading from 70731171155016_S32496_I63397_0.dcm... > Starting DICOMRead2() > dcmfile = > /trumpet/downloads/FreeSurfer/freesurfer/subjects/buckner_data/tutorial_subjs/convertedfile/mri/orig/70731171155016_S32496_I63397_0.dcm > dcmdir = > /trumpet/downloads/FreeSurfer/freesurfer/subjects/buckner_data/tutorial_subjs/convertedfile/mri/orig > WARNING: tag ImageNumber not found > Ref Series No = 63443 > Found 168 files, checking for dicoms > WARNING: tag ImageNumber not found > Found 166 dicom files in series. > WARNING: tag ImageNumber not found > First Sorting > WARNING: files are not found to be different and cannot be sorted > File1: > /trumpet/downloads/FreeSurfer/freesurfer/subjects/buckner_data/tutorial_subjs/convertedfile/mri/orig/70731171155016_S32496_I63397_0.dcm > File2: > /trumpet/downloads/FreeSurfer/freesurfer/subjects/buckner_data/tutorial_subjs/convertedfile/mri/orig/70731171155016_S32496_I63397_1.dcm > Computing Slice Direction > Vs: 1.2024 0 0 > Vs: 1 0 0 > Second Sorting > Counting frames > nframes = 1 > nslices = 166 > ndcmfiles = 166 > PE Dir = UNKNOWN (dicom read) > TransferSyntaxUID: --1.2.840.10008.1.2-- > jpegUID: --1.2.840.10008.1.2.4-- > Loading pixel data > TR=0.00, TE=0.00, TI=0.00, flip angle=0.00 > i_ras = (-0, -0, -1) > j_ras = (-0, -1, 0) > k_ras = (-1, -0, 0) > writing to T1.mgz... > > Then I copied the outputted T1.mgz file using: > > [jdrozd@trumpet orig]$ cp T1.mgz ../rawavg.mgz > [jdrozd@trumpet orig]$ cd .. > [jdrozd@trumpet mri]$ ls > 1.Bvq.Xml* 2.Bvq.Xml* error.log orig/ orig2/ rawavg.mgz transforms/ > [jdrozd@trumpet mri]$ pwd > > /trumpet/downloads/FreeSurfer/freesurfer/subjects/buckner_data/tutorial_subjs/convertedfile/mri > > Then I tried to conform the data using: > > [jdrozd@trumpet mri]$ mri_convert rawavg.mgz orig.mgz --conform > mri_convert rawavg.mgz orig.mgz --conform > $Id: mri_convert.c,v 1.146.2.4 2009/01/30 02:30:42 nicks Exp $ > reading from rawavg.mgz... > TR=0.00, TE=0.00, TI=0.00, flip angle=0.00 > i_ras = (-0, -0, -1) > j_ras = (-0, -1, 0) > k_ras = (-1, -0, 0) > Original Data has (0.93905, 0.93905, 0) mm size and (256, 256, 166) voxels. > Data is conformed to 1 mm size and 256 voxels for all directions > changing data type from 4 to 0 (noscale = 0)... > MRIchangeType: Building histogram > Reslicing using trilinear interpolation > MRIresample(): source matrix has zero determinant; matrix is: > -0.000 -0.000 -0.000 0.000; > -0.000 -0.939 -0.000 0.000; > -0.939 0.000 0.000 0.000; > 0.000 0.000 0.000 1.000; > > Do you know why I get this error? > MRIresample(): source matrix has zero determinant; matrix is: > -0.000 -0.000 -0.000 0.000; > -0.000 -0.939 -0.000 0.000; > -0.939 0.000 0.000 0.000; > 0.000 0.000 0.000 1.000; > > Any help would be appreciated. > > John Drozd > PostDoctorate Fellow >

4 6

data conversion
by Al-Radaideh Ali 15 May '09

15 May '09

Dear FreeSurfer experts, I used 0.6mm isotropic resolution MPRAGE ( matrix size 320 x 320 ) and 260 slices in freesurfer. The output (.mgz) has a different resolution and matrix size (1mm isotropic resolution and 256 x 256 matrix size). I used mri_convert to convert the output image from mgz to analyze. is there any way I can get back to the original resolution and matrix size, please? Thanks in advance Ali This message has been checked for viruses but the contents of an attachment may still contain software viruses, which could damage your computer system: you are advised to perform your own checks. Email communications with the University of Nottingham may be monitored as permitted by UK legislation.

2 1

Processing high resolution data
by Falk Lüsebrink 15 May '09

15 May '09

Hi FreeSurfers, Im still trying to process some high resolution data through the freesurfer stream, but it is constantly failing during several different stages. The biggest issue is the skull stripping and Im still trying to get good results here. Using the hires switch during autorecon1 normally leaves to much skull or cuts of too much tissue. Adjusting the parameters during mri_watershed (like changing the preflooding height or using the atlas switch) doesnt work too well in most cases. I tried to manually edit the brainmask.mgz with tkmedit but I encountered another problem. I was using data with an isometric voxel size of 0.6mm and removed every bit of left skull from the coronal view. Afterwards I ran autorecon2 but it did exit, so I viewed the brainmask in Slicer and noticed that probably every second slice was skipped in tkmedit. I also tried using the cm switch during autorecon1 to create the .lta file for the skull stripping stage, which I was hoping to improve the results. But either mri_watershed exists with an error in which it states that the WM intensity is 0 and therefore below the csf intensity and that I should check the volume, though I cant find anything unusual, or it exists with the error segmentation fault. I read that these segmentation fault errors seem to occur if the system runs out of memory, but as Im having 32gb ram and 16gb swap memory I dont think this is the reason. Another approach I was using is to use either a brainmask created by a stream of Slicer and then importing it to freesurfer or to use a brainmask created by a conformed stream, then using mri_vol2vol to resample it to the original resolution and process the other stages. The first approach should work fine, but the volume I used wasnt well processed during mri_fill. Most voxels were recognized as being part of the left hemisphere and only a few (about 150) were marked as belonging to the right hemisphere. And I was unable to fix this issue. The second approach seems also to be a good idea, but the resulting brainmask is blurred due to the upscaling. I noticed that using the hires switch the brainmask isnt really used as a mask during normalization2 but is just normalized again and the output is written as brain.mgz. If I want to use the brainmask as a mask (as it should be intended) should I use it to mask the T1.mgz? Like mri_normalize noconform mask brainmask.mgz T1.mgz brain.mgz? In the original stream the brainmask is used here to mask the norm.mgz, which I quite dont understand as the norm.mgz is already stripped. But maybe I just get something wrong. Yesterday I was able get a good skull stripping by running mri_nu_correct again with T1.mgz as the input and using the distance 25 stop 0.0001 switches (which I read of in another mail here on the list to improve results using 3T data). But recon-all exists at the beginning of mris_fix_topology with the segmentation fault error on both hemispheres. I apologize for the long text, but maybe someone can give me some input to try something else or help with existing issues. Thanks in advance, Regards, Falk

1 0

segmentation
by Feng-Xian 14 May '09

14 May '09

Hi, I have some problem. Could you get me some suggestion? I get a simulation brain with no-adding noise from BrainWeb. Then, I add 1% noise and 5% noise, respectively, to the simulation brains. When I finished these simulation brains through FreeSurfer, I found that the cortical thickness of these two groups (1% vs 5%) has a bias. Would you get me some comment? I guess the finding of bias maybe has a great relationship with the process of segmentation. Would you tell me what to do segmentation in FreeSuefer? Thank you in advance! Feng-Xian ------------------------------------------------- Chang Gung University Mail Server Web Mail system By Chang-Gung University Information Center (http://www.cgu.edu.tw/ic/)

1 0

cluster spanning multiple labels
by Siddharth Srivastava 14 May '09

14 May '09

Hi everyone, For a cluster spanning multiple labels based on a specific annotation, is it possible to automatically generate a list of labels (text) of the regions it overlaps with? What command/process should do this? mri_surfcluster is just providing me with the parcellation where the maxima is located. thanks, sid.

2 1

FSL --> FreeSurfer
by Vina Goghari 14 May '09

14 May '09

Hello all, I have group contrast fMRI activations analzyed by FSL Flame 1 that I want to extract individual thickness data for each subject from data processed by FreeSurfer? Is that possible and if so, how? Thanks! Vina

2 4

[Fwd: mri_convert batch converting]
by ozgun ozalay 14 May '09

14 May '09

also i've tried ; //mri_convert -it dicom -ot nii /home/ozgun/AnV/dti samples/KARABAY_NURI_.MR.EGE_HEAD.4.1.2008.07.21.21.20.51.578125.24527595.IMA /home/ozgun/AnV/ok.nii / output was; / $Id: mri_convert.c,v 1.146.2.4 2009/01/30 02:30:42 nicks Exp $ reading from /home/ozgun/AnV/dti samples/KARABAY_NURI_.MR.EGE_HEAD.4.1.2008.07.21.21.20.51.578125.24527595.IMA... Starting DICOMRead2() dcmfile = /home/ozgun/AnV/dti samples/KARABAY_NURI_.MR.EGE_HEAD.4.1.2008.07.21.21.20.51.578125.24527595.IMA dcmdir = /home/ozgun/AnV/dti samples Ref Series No = 4 Found 73 files, checking for dicoms Found 70 dicom files in series. First Sorting Computing Slice Direction Vs: 0 0 0 Vs: nan nan nan Second Sorting Counting frames nframes = 70 nslices = 1 ndcmfiles = 70 PE Dir = COL (dicom read) TransferSyntaxUID: --1.2.840.10008.1.2.1-- jpegUID: --1.2.840.10008.1.2.4-- Loading pixel data TR=10700.00, TE=103.00, TI=0.00, flip angle=90.00 i_ras = (-1, -0, 0) j_ras = (-0, -1, 0) k_ras = (nan, nan, nan) Reslicing using trilinear interpolation *MRIresample(): error inverting matrix; determinant is nan, matrix is:* -2.000 -0.000 nan nan; -0.000 -2.000 nan nan; 0.000 0.000 nan nan; 0.000 0.000 0.000 1.000; /i think problem occurs because of the bold line. Does anyone have any idea to correct this ? Thanks anyway Yours Ozgun

1 0

mri_convert batch converting
by ozgun ozalay 14 May '09

14 May '09

Hi; i have 70 Siemens .ima files in a single directory. Does anyone know how to convert ALL files in a folder into another format using mri_convert ? Do i have to convert them one by one? Does "--sdcmlist" option does this? Simple usage; /mri_convert <input file> <outputfile>/ ie: mri_convert /home/ozgun/AnV/aman.ima /home/ozgun/AnV/aman.nii Thanks anyway Ozgun

3 2

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