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Periaqueductal gray ROI
by Ritobrato Datta 30 Aug '11

30 Aug '11

Hi All, Is there a Periaqueductal Gray ROI generated by freesurfer recon ? Please let me know. Thanks Ri

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bad pial surfaces
by Reza Farivar 30 Aug '11

30 Aug '11

Hello, I've been banging my head against the wall and keyboard to get mris_make_surfaces generate correct pial surfaces. While the white matter surface looks okay for the most part, the pial matter surfaces almost never actually reach the pial surface, but rather stop very close to the white matter surface. I was able to get it pushed out a bit by setting MIN_GRAY_AT_CSF_BORDER and MAX of that same parameter to really low numbers, but then it makes the error of putting the pial surface somewhere in the csf. Is there a list of parameters that I can play with to correct this problem? I should note that I'm working with macaque MRI's acquired at 0.35mm iso that have been made to look like they were acquired at 1mm in order to make freesurfer happy. If there is a thickness limit to gray matter, then this may fail at some locations because of the incorrect voxel size. Is there such a limit set in mris_make_surfaces? Many thanks in advance for your help, Reza

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Freesurfer surfaces in the original space
by Navid Shiee 30 Aug '11

30 Aug '11

Dear Freesurfer team, I have a question regarding Freesurfer surfaces. I submitted this questions to the wiki but did not get any response and I was wondering if you can help me on this issue. I am doing some experiments with Freesurfer and would like to have surfaces in the original image space. FS converts everything to 1mm^3 256x256x256 grid in the coronal space. I have axial images which does not have homogenous grid (although it has isotropic resolution). I know there is a command to convert the volumetric structural segmentation into the original space but could not find the command for surfaces. I would be very grateful if you can provide me with some information for doing this. I appreciate your kind help in advance. Regards, -- Navid Shiee Ph.D. Candidate Laboratory of MEDical Image Computing (MEDIC) Image Analysis and Communication Laboratory (IACL) Johns Hopkins University

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Error with dt_recon
by Antonella Kis 30 Aug '11

30 Aug '11

Dear All: I am running the dt_recon:# setenv FREESURFER_HOME /usr/local/freesurfer # setenv SUBJECTS_DIR $FREESURFER_HOME # cd /usr/local/freesurfer/NPI001 # dt_recon --i /usr/local/freesurfer/NPI001/dti/1.3.46.670589.11.17385.5.0.7524.2009112812454960001.dcm --s /usr/local/freesurfer/NPI001 --o /usr/local/freesurfer/NPI001/dti and I am getting the following error: ERROR: cannot extract sDiffusion.alBValue[1] from /usr/local/freesurfer/NPI001/dti/dwi-infodump.dat Can someone help, please? Thank you. AK

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mri_convert jpeg compression error
by dehaes＠nmr.mgh.harvard.edu 30 Aug '11

30 Aug '11

Dear Freesurfer developers and users, I experience error converting .dcm to .nii with mri_convert these days. I do not know why because it is not reproducible and error occurs mostly with new scans (MPRAGE and other functional sequences) from a Siemens Trio Tim 3T. Here is a display output as an example when I type : mri_convert IM-0001-0001.dcm anat.nii mri_convert IM-0001-0001.dcm anat.nii $Id: mri_convert.c,v 1.171 2010/09/14 21:03:12 greve Exp $ reading from IM-0001-0001.dcm... Getting Series No Scanning Directory INFO: Found 106 files in /Users/dehaes/Work/Research/ASL/CHB/Data/Siemens_pasl/4503697/Anat INFO: Scanning for Series Number 5 INFO: found 104 files in series INFO: loading series header info. INFO: sorting. RunNo = 4 INFO: (192 192 104), nframes = 1, ismosaic=0 PE Dir ROW ROW AutoAlign matrix detected AutoAlign Matrix --------------------- 0.964 -0.234 -0.129 -6.753; 0.238 0.971 0.015 7.906; 0.122 -0.045 0.992 -5.687; 0.000 0.000 0.000 1.000; ERROR: the pixel data cannot be loaded as it is JPEG compressed. (Transfer Syntax UID: 1.2.840.10008.1.2.4.70) Does Siemens recently changes the way they write DICOM from the console? Do you guys know how to avoid this error? Thanks Mathieu Dehaes

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mri_convert -conform issue
by Sebastian Moeller 29 Aug '11

29 Aug '11

Hi List, I just noticed that when I use mri_convert -conform (which reslices my RPI volumes to LIA) the tkrvox2vras matrices seem not to change (for both freesurfer centos4 64bit, version 4.5 and version 5.1). I use MRIread to read in the resulting files in matlat and try to compare the volumes there. All the vox2ras are changed by the conformation (as I expect), but the tkrvox2ras of the conformed volume equal the tkrvox2ras matrix from the original volume. I am confused now as the tkrvox2ras and the x_r, x_a, x_s, (as well as the y_* and z_*) vectors are updated and match the vox2ras matrices. Shouldn't tkrvox2ras also be updated? And if not why :) ? Best Sebastian -- Sebastian Moeller telephone: +1-626-325-8598 /+1-626-395-6523 / +1-626-395-6616 fax: 626-395-8826 German GSM: +49 - 15 77 - 1 90 31 41 mobile: +1-626-325-8598 +1-626-807-5242 US CDMA: +1-626-807-5242 moeller(a)caltech.edu Division of Biology MC 114-96 California Institute of Technology 1200 East California Boulevard CA 91125, Pasadena USA

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-localGI using compiled matlab - solved
by Bill Taylor 29 Aug '11

29 Aug '11

We here at the University of Wisconsin, Madison are constrained to run only compiled Matlab on our clusters. We have a number of groups that use freesurfer on I have just completed creating some workflows to do longitudinal, -localGI and a basic processing using autorecon1,2 and three. These run in our center on execute nodes not requiring a license since they use the compiled version and a runtime. For the -localGI: Besides the basics of running a compiled matlab in terms of setting up the environment right, I had to change mris_compute_lgi and some input mappings in some of the freesurfer matlab toolbox. My changes had all the args coming into matlab as strings and I had to find the trouble ones(10 or so) and convert them to the proper expected type . This allows thousands of jobs to be analyzed at once on our cluster. Anyone willing to take charge of these changes back at Harvard. It would be great to have this variation available to others. -- Bill Taylor 608-263-2656 (cell 608-219-4430) Center for High Throughput Computing(CHTC) Condor project Computer Sciences University of Wisconsin, Madison

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Re: [Freesurfer] Freesurfer Digest, Vol 90, Issue 93
by Hasan, Khader M 29 Aug '11

29 Aug '11

I read with interest the comment by Lucas and some excellent responses by Anderson, and possibly others I did not read yet. A. These are my personal views based on experience with T1-weighted, DTI-based, T2-weighted, multi-modal, combined T1-T2-PD-DTI tissue segmentation and lesion methods as we and others published on even across the human lifespan (see list of references below) using FreSurfer and other approaches. Voxel-based approaches could not be compared directly with volume-based results as you obtain from FS or FSL. B. As summarized on the FreSurfer Wiki ( http://surfer.nmr.mgh.harvard.edu/fswiki) FreeSurfer has been validated extensively using postmortem data and one can list scores of human pathologies and hundreds of papers that utlilized FreeSurfer results including child brain development, aging, brain trauma, Schizophrenia, Bipolar disorders, Parkinson, Alzheimer's, multiple sclerosis, Autsim, dyslexia and even the effect of mother's milk on deevelopment. Recently one can also appreciate the use of FreeSurfer in perfusion mapping, Iron mapping (Hasan et al., MRM 2011) and the fusion of quantittaive MRI methods to study the interplay between metrics such as relaxation time and radial diffusivity (Walimini et al., 2011). C. The problem is not in FreeSurfer results as you can always edit what you get or modulate it with experienced-rater masks or known landmarks. For example, thalamus in Fresurfer is a proper, and it is impossible to segment this region on T1-weighted alone into GM and WM. One can use T1-T2 and PD contrasts and hence you will get almost all gray matter zones in the thalmus along with its white matter islands. One can also use DTI along with cortical parcellations to regularize the solutions. For hippocampus, this is complicated and its subfields are highly variable, one may need higher spatial resolution methods, double fluid attenutaion and dedicated atalses. The Diencepahlon or brainstem are complicated as deep gray matter such as substantia nigra and red nucleus are not contrasted on T1-weighted data, so goes the Cerebellum and other regions (e.g. hypothalmus) D. The problem shows up when one tries to compare results, but caution as volume results and methods (x, y) may be related by a scaling or shift (y=a*x+c). No segmentation or parcellation method that uses single modality will be able to segment with 100% accuracy CSF, GM and WM as the very defintion of boundaries is limited by the acquisition paradigm details. For example, total CSF can never be estimated on T1-weighted. Lesions look dark on T1-weighted, so does CSF and GM. If you have iron accumulation you may see it as bright on T1-weighted data which may be confounded as white matter, but the training set and raters may help dtect and remove this from your data. E. From my experience, I am not a big fan of comparing results obtained from packages. I am more concerned with reliability, reproducibility on serial studies of one single package and sensitivity to variables such as Age, Side, Gender, Pathology and Clinical or Cognitive outcome ASSUMING your Scanner is STABLE and the groups being compared are carefully selected and screened (age range matched, comorbids, covariates) F. Clearly in pathologies, plaques, tumors or lesions will confound any method used as hypo or hyper-intesnities will be interpreted differenctly and one has to intervene to overide the results or use other contrasts such as T2-wighted, or FLAIR. G. As far as testing segmentation methods we made some suggestions in a recent Letter (Hasan and Pedraza Neuroimage 2009). I also bring attention for a paper that we published in August 2011 Neuroimage that used FreeSurfer and fused its regional GM and WM results with DTI-derived and Relaxation maps in both GM and WM. We did not find major problems with FreeSurfer volumetry if you carefully inspect the results, we have also tested subsets of the control data as function of age and compared it with known or expected trends using large samples. H. Occasionallly we encountered cases with poor results, but be careful. Failure or poor results may not be fairly blamed on FreeSurfer as some cases had poor SNR, some motion artifcats and we removed them from the analysis. FreeSurfer results are as good as your input data contrast-to-noise, slice thickness and other image acquisition factors that control the contrast (e.g. flip, TE/TR,..). I wont expect magic if the data original contrast and fed to any program does not have sufficient signal and contrast between CSF, GM and WM. I. References 1. http://surfer.nmr.mgh.harvard.edu/fswiki 2. Hasan KM, Frye RE. Diffusion tensor-based regional gray matter tissue segmentation using the international consortium for brain mapping atlases. Hum Brain Mapp. 2011;32(1):107-17. 3. Hasan KM, Walimuni IS, Kramer LA, Frye RE. Human brain atlas-based volumetry and relaxometry: application to healthy development and natural aging. Magn Reson Med. 2010;64(5):1382-9. 4. Hasan KM, Walimuni IS, Kramer LA, Narayana PA. Human brain iron mapping using atlas-based T(2) relaxometry. Magn Reson Med. 2011 (doi: 10.1002/mrm.23054). 5. Hasan KM, Walimuni IS, Abid H, Hahn KR. A review of diffusion tensor magnetic resonance imaging computational methods and software tools. Comput Biol Med. 2011 (doi:10.1016/j.compbiomed.2010.10.008) 6. Hasan KM and Pedraza O. Improving the reliability of manual and automated methods for hippocampal and amygdala volume measurements. Neuroimage. 2009;48(3):497-8. 7. Walimuni IS, Hasan KM. Atlas-based investigation of human brain tissue microstructural spatial heterogeneity and interplay between transverse relaxation time and radial diffusivity. Neuroimage. 2011;57(4):1402-10. 8. Walimuni IS, Abid H, Hasan KM. A computational framework to quantify tissue microstructural integrity using conventional MRI macrostructural volumetry. Comput Biol Med. 2011 (doi:10.1016/j.compbiomed.2010.10.009). Khader M Hasan, PhD Associate Professor of Radiology MSE 168, Tel 713 500 7690 (FAX 713 500 7684) University of Texas Health Science Center at Houston Medical School Diagnostic and Interventional Imaging Magnetic Resonance Imaging Research Division Diffusion Tensor Imaging Lab, Tel 713 500 7683 http://www.uth.tmc.edu/radiology/faculty/khader-m-hasan/index.html ________________________________________ From: freesurfer-bounces(a)nmr.mgh.harvard.edu [freesurfer-bounces(a)nmr.mgh.harvard.edu] On Behalf Of freesurfer-request(a)nmr.mgh.harvard.edu [freesurfer-request(a)nmr.mgh.harvard.edu] Sent: Monday, August 29, 2011 11:00 AM To: freesurfer(a)nmr.mgh.harvard.edu Subject: Freesurfer Digest, Vol 90, Issue 93 Send Freesurfer mailing list submissions to freesurfer(a)nmr.mgh.harvard.edu To subscribe or unsubscribe via the World Wide Web, visit https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer or, via email, send a message with subject or body 'help' to freesurfer-request(a)nmr.mgh.harvard.edu You can reach the person managing the list at freesurfer-owner(a)nmr.mgh.harvard.edu When replying, please edit your Subject line so it is more specific than "Re: Contents of Freesurfer digest..." Today's Topics: 1. Re: Accuracy of FreeSurfers gray matter segmentation (Anderson Winkler) 2. Re: Accuracy of FreeSurfers gray matter segmentation (Lucas Eggert) 3. patch present-label missing (maahmed(a)mappi.helsinki.fi) 4. Re: Paired analysis results into table? (Braber, A. den) 5. Re: patch present-label missing (Bruce Fischl) 6. Re: Accuracy of FreeSurfers gray matter segmentation (Anderson Winkler) 7. Re: Accuracy of FreeSurfers gray matter segmentation (Bruce Fischl) 8. FW: Cortical Flattening (Tax, C.M.W.) 9. Re: problem with selxavg3 (Douglas N Greve) 10. Re: problem with selxavg3 (Maryam Vaziri Pashkam) 11. Re: problem with selxavg3 (Douglas N Greve) 12. Re: Correlation cortical thickness and lgi (Douglas N Greve) ---------------------------------------------------------------------- Message: 1 Date: Sun, 28 Aug 2011 16:47:18 -0400 From: Anderson Winkler <andersonwinkler(a)hotmail.com> Subject: Re: [Freesurfer] Accuracy of FreeSurfers gray matter segmentation To: freesurfer(a)nmr.mgh.harvard.edu Message-ID: <BLU0-SMTP351AC314B26178D2AADB33DA1150(a)phx.gbl> Content-Type: text/plain; charset="iso-8859-1" Hi Lucas, To know if a given structure is gray or white matter you can look in any reasonable anatomy textbook. In any case, the question itself is somewhat ill-posed, because some of the subcortical structures have heterogeneous tissue composition and can't really be labeled entirely as gray matter, even macroscopically. The most notable examples are perhaps the thalamus and hippocampus, but the same applies to other structures too. Anyway, if you really want to make a hard distinction, you can call then caudate, putamen, pallidum, amygdala, accumbens, hippocampus and thalamus as gray matter. The region defined as ventral diencephalon is very heterogeneous and I would not classify it either as GM or WM, as it includes mamillary bodies, tuber cinereum/infundibulum (but not hypophysis), some hypothalamic nuclei near the lateral and inferior walls of the 3rd ventricle and sometimes fragments of the optic tracts (but not chiasm, which has its own label). It also includes parts of the mesencephalon (e.g. part of the cerebral crux, part of the substantia nigra and rubra). Importantly, if you are comparing algorithms, you have to be sure they are reporting the same thing. For instance, it's fairly common to run SPM or FSL/FAST segmentation, then sum the GM voxels within a region defined from an atlas. If you do this for, say, caudate or thalamus, you'll get the volume of what the algorithm classified as GM within the structure you selected. FreeSurfer (and, e.g. FSL/FIRST), on the other hand, will segment and report the volume of the structure as a whole, including all what it contains. A direct comparison, thus, is not valid. Hope this helps! All the best, Anderson On 28/08/11 02:53, Lucas Eggert wrote: > Dear List, > > I recently posted the message you see below. > > I currently compare the segmentation results of different > segementation algorithms and FreeSurfer always yields the least > accurate results. Because I still have difficulties to decide which of > the labels in the aseg.mgz files should be considered gray matter, I > have the feeling, the bad results I am getting for FreeSufer might be > caused by not including all relevant gray matter segments. > > So, I would very much appreaciate any help on deciding which of the > labels in the aseg.mgz file belong to gray matter! > > With kind regards > Lucas Eggert > > > -------- Original-Nachricht -------- > Betreff: [Freesurfer] How to determine gray matter in the aseg.mgz > Datum: Tue, 03 May 2011 17:26:55 +0200 > Von: Lucas Eggert <leggert(a)Uni-Osnabrueck.DE> > An: freesurfer(a)nmr.mgh.harvard.edu <freesurfer(a)nmr.mgh.harvard.edu> > > > > Dear Experts, > > I would like to generate a gray matter mask using the aparc+aseg.mgz file. > > Now, my question is whether there exists a file in addition to the > FreeSurferColorLUT.txt > file, which more explicitly, i. e., withouth the abbreviations, explains > the labels, because I > find it hard to decide whether, e. g., "Left-VentralDC", "Line1", > "LeftmOg", or "Left-Interior" > belong to gray matter, or not (to name only a few of those labels I > cannot clearly classify). > > Or is there a easier way to create a gray matter mask? > > Thank you very much in advance! > > Best regards, > Lucas Eggert > > -- > Lucas Eggert, M.Sc. > Institute of Cognitive Science > University of Osnabrueck > Albrechtstrasse 28 > D-49076 Osnabrueck > Germany > > Phone: +49-541-969-44-28 > Website:http://www.cogsci.uni-osnabrueck.de/~leggert/ > > > > > > > > _______________________________________________ > Freesurfer mailing list > Freesurfer(a)nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > The information in this e-mail is intended only for the person to whom it is > addressed. If you believe this e-mail was sent to you in error and the e-mail > contains patient information, please contact the Partners Compliance HelpLine at > http://www.partners.org/complianceline . If the e-mail was sent to you in error > but does not contain patient information, please contact the sender and properly > dispose of the e-mail.

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DTI and FS
by Antonella Kis 29 Aug '11

29 Aug '11

Dear FS experts, I'm interested in doing DTI analysis, using FS. Do I need to have functional data (feat) and then do I need to register the anatomical data on the functional one? My MRI data contains the following folders: 3DAx_T1, Ax_Flair, Ax_T2PD, DTI_15, DTI_30. I am not sure for a DTI /FSL analysis what data should I use. 1).Do I need to use the DTI data (DTI_15, DTI_30) and what steps should I follow in order to run a DTI/FSL data analysis for my subjects versus controls? 2).Do I need to run the dt_recon? In this case my input should be the dicom images from a DTI (DTI_15 or DTI_30) folder? 3) When I need to check the registration where I get the lowb.nii? tkregister2 --mov dti/lowb.nii --reg dti/register.dat \ --surf orig --tag 4) Or, should I use trac-all on the DTI_15 or DTI_30 data? 5) Do I need to run reg-feat2anat to first analyze my data with FEAT (No Smoothing )as in the example posted online: reg-feat2anat –featdir fbert1.feat –subject bert 6) From where fbert1.feat is coming/how was obtained? Sorry for asking so many/silly questions. I will be greatly appreciate any help/directions/suggestions. Antonella

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GLM analysis - covariates
by Jenessa Price 29 Aug '11

29 Aug '11

I'm examining differences in cortical thickness in a sample of drug users and non-users. From the literature, it appears that the 2 covariates I should use for the GLM analysis will be gender and age. Other researchers have examined other possible covariates (like age of first use, # past year uses, IQ, etc.) using correlations between those variables and significant clusters that differed between groups. Is this the best way to go about translating a typical regression model into the Freesurfer statistical package (especially if I'm interested in dose-dependent relationships)? Also, how do you suggest controlling for total brain volume? Other researchers have performed univariate analyses between groups on TBV controlling for age and gender, then examined mean cortical thickness, but I'm not quite sure what the steps are for this. Could someone help me identify the best way to control for TBV (i.e., is there a way to control from the first GLM analysis, or is this a possible covariate to enter in to the analysis)? Thanks! -- Jenessa S. Price, M.A. Graduate Student, Clinical Psychology and Neuropsychology University of Cincinnati Campus Representative, American Psychological Association of Graduate Students (APAGS)

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