Freesurfer October 2019

freesurfer@nmr.mgh.harvard.edu

111 participants
180 discussions

Re: [Freesurfer] Freesurfer Digest, Vol 188, Issue 14
by Juan Rivas 09 Oct '19

09 Oct '19

External Email - Use Caution Hi anatasia, thank you so much. I made a correction on the file, and the dwi.nii.gz problem was solved. I tried again with the previous script, but now I have a new error announce: JC. [shiraz:esq-15-hc] (nmr-stable6-env) trac-all -prep -c dmrirc-esq-15-hc INFO: SUBJECTS_DIR is /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing INFO: Diffusion root is /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc Actual FREESURFER_HOME /autofs/cluster/freesurfer/centos7_x86_64/stable6 trac-preproc -c /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/dmrirc.local -log /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/trac-all.log -cmd /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/trac-all.cmd #------------------------------------- /usr/local/freesurfer/stable6/bin/trac-preproc #------------------------------------- #@# Image corrections Wed Oct 9 12:25:33 EDT 2019 mri_convert --bvec-voxel /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc_dwi.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz mri_convert.bin --bvec-voxel /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc_dwi.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc_dwi.nii.gz... TR=0.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0.999334, 0.0365012) k_ras = (-0, -0.0365012, 0.999334) writing to /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz... cp /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/processing/31190669_bvecs.dat /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvecs cp /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/processing/31190669_bvals.dat /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvals mv -f /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs.tmp /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvecs mv -f /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvals.tmp /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvals orientLAS /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.nii.gz INFO: input image orientation is LAS INFO: input image determinant is -11.2683 mri_convert -oni 130 -onj 130 -onk 44 -oid -1 0 0 -ojd 0 0.999334 0.0365012 -okd 0 -0.0365012 0.999334 -oc 3.38982 26.1253 36.373 -rt nearest /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.nii.gz mri_convert.bin -oni 130 -onj 130 -onk 44 -oid -1 0 0 -ojd 0 0.999334 0.0365012 -okd 0 -0.0365012 0.999334 -oc 3.38982 26.1253 36.373 -rt nearest /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.nii.gz normalizing out_j_direction: (0, 0.999334, 0.0365012) -> (0, 0.999334, 0.0365012) normalizing out_k_direction: (0, -0.0365012, 0.999334) -> (0, -0.0365012, 0.999334) $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz... TR=0.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-1, 0, 0) j_ras = (0, 0.999334, 0.0365012) k_ras = (-0, -0.0365012, 0.999334) Reslicing using nearest writing to /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.nii.gz... INFO: found /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvals, copying INFO: found /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.bvecs, converting to LAS mv -f /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.bvecs /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs mv -f /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.bvals /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvals eddy_correct /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig_las.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.nii.gz 0 File "/usr/pubsw/packages/fsl/current/bin/imglob", line 78 print "Usage: $0 [-extension/extensions] <list of names>" ^ SyntaxError: Missing parentheses in call to 'print'. Did you mean print("Usage: $0 [-extension/extensions] <list of names>")? Usage: fslmerge <-x/y/z/t/a/tr> <output> <file1 file2 .......> [tr value in seconds] -t : concatenate images in time -x : concatenate images in the x direction -y : concatenate images in the y direction -z : concatenate images in the z direction -a : auto-choose: single slices -> volume, volumes -> 4D (time series) -tr : concatenate images in time and set the output image tr to the final option value mv -f /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs.norot xfmrot /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.ecclog /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs.norot /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/bvecs grep: /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.ecclog: No such file or directory head: cannot open ‘/autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.ecclog’ for reading: No such file or directory wc: /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.ecclog: No such file or directory wc: /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi.ecclog: No such file or directory ERROR: Transform file should be eddy_correct/eddy log file or .mat file Linux shiraz.nmr.mgh.harvard.edu 3.10.0-957.1.3.el7.x86_64 #1 SMP Thu Nov 29 14:49:43 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux trac-preproc exited with ERRORS at Wed Oct 9 12:28:15 EDT 2019 [shiraz:esq-15-hc] (nmr-stable6-env) [shiraz:esq-15-hc] (nmr-stable6-env) # FreeSurfer SUBJECTS_DIR # T1 images and FreeSurfer segmentations are expected to be found here # setenv SUBJECTS_DIR /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing # Output directory where trac-all results will be saved # Default: Same as SUBJECTS_DIR # set dtroot = /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc # Subject IDs # set subjlist = (esq-15-hc) # Default: Run analysis on all subjects # set runlist = (1) # Input diffusion DICOMs (file names relative to dcmroot) # If original DICOMs don't exist, these can be in other image format # but then bvecfile and bvalfile must be specified (see below) # set dcmroot = /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc set dcmlist = (esq-15-hc_dwi.nii.gz) # Diffusion gradient table # Must be specified if inputs are not MGH DICOMs # Three-column format, one row for each volume in the diffusion data set # Default: Read from DICOM header # set bvecfile = /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/processing/31190669_bvecs.dat # Diffusion b-value table # Must be specified if inputs are not MGH DICOMs # Single-column format, one value for each volume in the diffusion data set # Default: Read from DICOM header # set bvalfile = /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/processing/31190669_bvals.dat # Perform registration-based B0-inhomogeneity compensation? # Default: 0 (no) # # set dob0 = 1 # Input B0 field map magnitude DICOMs (file names relative to dcmroot) # Only used if dob0 = 1 # Default: None # # set b0mlist = (huey/fmag/XXX-1.dcm dewey/fmag/XXX-1.dcm louie/fmag/XXX-1.dcm) # Input B0 field map phase DICOMs (file names relative to dcmroot) # Only used if dob0 = 1 # Default: None # # set b0plist = (huey/fphas/XXX-1.dcm dewey/fphas/XXX-1.dcm louie/fphas/XXX-1.dcm) # Echo spacing for field mapping sequence (from sequence printout) # Only used if dob0 = 1 # Default: None # set echospacing = 0.7 # Perform registration-based eddy-current compensation? # Default: 1 (yes) # set doeddy = 1 # Rotate diffusion gradient vectors to match eddy-current compensation? # Only used if doeddy = 1 # Default: 1 (yes) # set dorotbvecs = 1 # Fractional intensity threshold for BET mask extraction from low-b images # This mask is used only if usemaskanat = 0 # Default: 0.3 # set thrbet = 0.5 # Perform diffusion-to-T1 registration by flirt? # Default: 0 (no) # set doregflt = 0 # Perform diffusion-to-T1 registration by bbregister? # Default: 1 (yes) # set doregbbr = 1 # Perform registration of T1 to MNI template? # Default: 1 (yes) # set doregmni = 1 # MNI template # Only used if doregmni = 1 # Default: $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz # set mnitemp = $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz # Perform registration of T1 to CVS template? # Default: 0 (no) # set doregcvs = 0 # CVS template subject ID # Only used if doregcvs = 1 # Default: cvs_avg35 # set cvstemp = donald # Parent directory of the CVS template subject # Only used if doregcvs = 1 # Default: # set cvstempdir = $FREESURFER_HOME/subjects # Use brain mask extracted from T1 image instead of low-b diffusion image? # Has no effect if there is no T1 data # Default: 1 (yes) # set usemaskanat = 1 # Paths to reconstruct # Default: All paths in the atlas # set pathlist = ( lh.cst_AS rh.cst_AS \ lh.unc_AS rh.unc_AS \ lh.ilf_AS rh.ilf_AS \ fmajor_PP fminor_PP \ lh.atr_PP rh.atr_PP \ lh.ccg_PP rh.ccg_PP \ lh.cab_PP rh.cab_PP \ lh.slfp_PP rh.slfp_PP \ lh.slft_PP rh.slft_PP ) # Number of path control points # It can be a single number for all paths or a different number for each of the # paths specified in pathlist # Default: 7 for the forceps major, 6 for the corticospinal tract, # 4 for the angular bundle, and 5 for all other paths # set ncpts = (6 6 5 5 5 5 7 5 5 5 5 5 4 4 5 5 5 5) # List of training subjects # This text file lists the locations of training subject directories # Default: $FREESURFER_HOME/trctrain/trainlist.txt # set trainfile = $FREESURFER_HOME/trctrain/trainlist.txt # Number of "sticks" (anisotropic diffusion compartments) in the bedpostx # ball-and-stick model # Default: 2 # set nstick = 2 # Number of MCMC burn-in iterations # (Path samples drawn initially by MCMC algorithm and discarded) # Default: 200 # set nburnin = 200 # Number of MCMC iterations # (Path samples drawn by MCMC algorithm and used to estimate path distribution) # Default: 7500 # set nsample = 7500 # Frequency with which MCMC path samples are retained for path distribution # Default: 5 (keep every 5th sample) # set nkeep = 5 # Reinitialize path reconstruction? # This is an option of last resort, to be used only if one of the reconstructed # pathway distributions looks like a single curve. This is a sign that the # initial guess for the pathway was problematic, perhaps due to poor alignment # between the individual and the atlas. Setting the reinit parameter to 1 and # rerunning "trac-all -prior" and "trac-all -path", only for the specific # subjects and pathways that had this problem, will attempt to reconstruct them # with a different initial guess. # Default: 0 (do not reinitialize) # set reinit = 0 El mié., 9 oct. 2019 a las 12:01, <freesurfer-request(a)nmr.mgh.harvard.edu> escribió: > 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: Questions re slice thickness, aseg and longitudinal > analysis (Martin Reuter) > 2. Re: Label created in the symmetric space (Jose Graterol) > 3. How to measure the cortical thickness in tumor patients? (???) > 4. Re: ECC or non-ECC Memory? (Sotiris Michos) > 5. Re: Freesurfer Digest, Vol 188, Issue 13 (Juan Rivas) > 6. Re: Brodmann area parcellation (sang ho shin) > 7. Re: [FSL] Hippocampus subregions question > (Iglesias Gonzalez, Juan E.) > 8. Re: Freesurfer Digest, Vol 188, Issue 13 (Yendiki, Anastasia) > 9. Re: How to measure the cortical thickness in tumor patients? > (Bruce Fischl) > 10. mri_convert no scaling (Ezequiel Mikulan) > 11. Re: mri_convert no scaling (Bruce Fischl) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Wed, 09 Oct 2019 10:48:48 +0200 > From: Martin Reuter <mreuter(a)nmr.mgh.harvard.edu> > Subject: Re: [Freesurfer] Questions re slice thickness, aseg and > longitudinal analysis > To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> > Message-ID: > < > 7a4d059cb5cb42f390e03f523677a3362d17cb81.camel(a)nmr.mgh.harvard.edu> > Content-Type: text/plain; charset="UTF-8" > > Hi David, > > I am not very optimistic: > > 5mm is too thick for FreeSurfer (recommendation is 1 up to 1.5). You > will certainly get something, but it can be very unreliable and > completely wrong. Especially longitudinally these thick slices will > induce large variance due to different head positioning (and different > slice angulations) in the scanner. > > Furthermore, FreeSurfer does not take Gad-Enhanced images. Also it will > not work if tumor lesions are present. > > About your questions: > > 1. Surfaces update the aseg, but if you are only interested in the > volumes, you can skip this expensive step (potentially at the cost of > slightly higher noise levels in your measurements). > > 2. I think not (see above). 5mm is too low. > > 3. Theoretically yes, but I have never tested if the scripts will do > it. You could run up to the aseg in the cross, then create base (up to > aseg) and then run the longs up to aseg. Not even sure you really need > the base aseg. You might be able to just run the initial base > registration step, obtain the transformations and median norm.mgz > image, could be sufficient for the long runs. > > 4. No. Gad images won't work. > > Best, Martin > > > On Mon, 2019-10-07 at 18:12 +0000, David Kamson wrote: > > External Email - Use Caution > > Freesurfers, > > > > First of all, I'd like to express my gratitude to the community for > > the support that keeps researchers like myself afloat! > > > > > > I have a unique set of oncology patients that I want to evaluate for > > brain atrophy in a retrospective longitudinal analysis. > > I was thinking about using Aseg.auto results to assess longitudinal > > volume changes, but before I invest all the time I wanted to check > > with the community whether this makes any sense at all: > > > > The dataset that looks like this: > > - 22 patients (no control dataset [yet]) > > - 10-25 MRIs per patient acquired over 2-8 years in relatively > > uniform intervals > > - Patients had most of their scans on the same scanner, but > > scanners differed widely between patients > > - All patients have axial T1 post gadolinium scans of 1x1x5mm > > resolution (3D acquisition available in <10%) > > - About 80% of scans have an axial pre-contrast T1 sequence > > - All scans are skullstripped (third party algorithm) > > > > I'm looking for crude changes, no subtleties; volumes of interest > > are: > > - Whole brain volume > > - White matter volume > > - Ventricular volume (mainly lateral ventricle) > > - Subcortical gray matter volume (whole thalamus most importantly) > > > > I ran a few test analyses and to my surprise I was able to generate > > pretty acceptable surfaces, however, topology fixing took about 24H > > per scan, and I feel aseg.auto contained all the volumetric data I > > was really interested in. > > > > My concrete questions are: > > 1) Does the full autorecon pipeline affect Aseg.auto? If there is no > > benefit, I could reduce the per scan analysis time from 28 hours to > > 1-2 h. > > 2) Would this low-resolution dataset be accepted by reviewers if used > > for Aseg? Should I do any quantitative validation beyond a visual > > quality analysis of Aseg? > > 3) Can I perform a longitudinal analysis only for the Aseg results? > > 4) Is it OK to use T1-gad images for the analysis? > > > > I'd appreciate any input! > > > > Best regards, > > David O. Kamson, MD PhD > > Neuro-oncology fellow > > Johns Hopkins Hospital & > > National Institutes of Health > > > > > > _______________________________________________ > > Freesurfer mailing list > > Freesurfer(a)nmr.mgh.harvard.edu > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > ------------------------------ > > Message: 2 > Date: Wed, 9 Oct 2019 11:34:59 +0200 > From: Jose Graterol <gpjosealberto13(a)gmail.com> > Subject: Re: [Freesurfer] Label created in the symmetric space > To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu> > Message-ID: > <CAFMm1s5_X3SzSWv=+ > wUKKH0z3HbVRceBQFG1v7SOA6_jWMbNYg(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > External Email - Use Caution > > It worked as intended. Thank you very much. > > On Tue, Oct 8, 2019 at 6:43 PM Greve, Douglas N.,Ph.D. < > DGREVE(a)mgh.harvard.edu> wrote: > > > In that case, run mris_preproc in the same way but don't include the > > paired-diff. Concatenate the two groups together to give you one file. > > Smooth as you did before. Now with that one file run > > mri_segstats --seg csdbase.sig.ocn.mgh --i youronefile.mgh --avgwf > > subject.hemi.cluster.dat --excludeid 0 > > > > The output file subject.hemi.cluster.dat will have a row for each > > subject and hemisphere (the order will be subject.lh, subject.rh, > > nextsubject.lh, nextsubject.rh) and a column for each cluster. The > > values will be the thickness. > > > > > > > > On 10/8/19 12:22 PM, Jose Graterol wrote: > > > > > > External Email - Use Caution > > > > > > Aha, yes. That is what I want. The original thickness values. > > > > > > Greve, Douglas N.,Ph.D. <DGREVE(a)mgh.harvard.edu > > > <mailto:DGREVE@mgh.harvard.edu>> schrieb am Di., 8. Okt. 2019, 17:36: > > > > > > That all looks fine, I'm just not sure what problem you want me to > > > solve. You said that the values in the y.ocn.dat file were all > > > negative, > > > it has to be this way because you specified only negative values > when > > > you ran glmfit-sim (and negative values are possible because you > have > > > computed left-right and negated half of your subjects). Do you > > > want the > > > original thickness values? > > > > > > On 10/8/19 11:14 AM, Jose Graterol wrote: > > > > > > > > External Email - Use Caution > > > > > > > > I will try to go back a bit, just to be sure I did not make a > > > mistake. > > > > I registered both hemispheres of every subject (affected and > > > > non-affected) with xhemi. > > > > > > > > Then for the left-affected subjects I ran: > > > > mris_preproc --target fsaverage_sym --hemi lh --xhemi > --paired-diff > > > > --srcsurfreg fsaverage_sym.sphere.reg --meas thickness --out > > > > leftlesionsubjects.lh.lh-rh.thickness.sm00.mgh --s sub-xxx > > > > > > > > For the right-affected: > > > > mris_preproc --target fsaverage_sym --hemi lh --xhemi > --paired-diff > > > > --srcsurfreg fsaverage_sym.sphere.reg --meas thickness --out > > > > rightlesionsubjects.lh.lh-rh.thickness.sm00.mgh --s sub-xxx > > > > > > > > Then I changed the sign of the right-affected subjects: > > > > fscalc rightlesionsubjects.lh.lh-rh.thickness.sm00.mgh mul -1 -o > > > > rightlesionsubjects.lh.rh-lh.thickness.sm00.mgh > > > > > > > > Then I concatenated them: > > > > > > > > mri_concat leftlesionsubjects.lh.lh-rh.thickness.sm00.mgh > > > > rightlesionsubjects.lh.rh-lh.thickness.sm00.mgh --o > > > > allsubjects.lh.lesion-healthy.thickness.sm00.mgh > > > > > > > > Then I smoothed the file: > > > > mris_fwhm --s fsaverage_sym --hemi lh --cortex --smooth-only > > > --fwhm 10 > > > > --i allsubjects.lh.lesion-healthy.thickness.sm00.mgh --o > > > > allsubjects.lh.lesion-healthy.thickness.sm10.mgh > > > > > > > > Then glmfit: > > > > mri_glmfit --y allsubjects.lh.lesion-healthy.thickness.sm10.mgh > > > > --glmdir glmdir.allsubjects.lh.lesion-healthy.thickness.sm10 > --osgm > > > > --surf fsaverage_sym lh > > > > > > > > and finally the correction for multiple comparisons: > > > > > > > > mri_glmfit-sim --y > allsubjects.lh.lesion-healthy.thickness.sm10.mgh > > > > --glmdir glmdir.allsubjects.lh.lesion-healthy.thickness.sm10 > > > > --cwpvalthresh 0.05 --cache 4 neg > > > > > > > > > > > > Hopefully this helps. > > > > > > > > > > > > > > > > > > > > On Tue, Oct 8, 2019 at 4:45 PM Greve, Douglas N.,Ph.D. > > > > <DGREVE(a)mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edu> > > > <mailto:DGREVE@mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edu>>> > > > wrote: > > > > > > > > The input has both positives and negatives, so is is not > > > > surprising that the y.ocn.dat also has positive and > > > negative. Not > > > > sure what is going wrong here ... > > > > > > > > On 10/7/2019 5:48 PM, Jose Graterol wrote: > > > >> > > > >> External Email - Use Caution > > > >> > > > >> Ok, I uploaded it with the name > > > "glmdir.jg.allsubjects.tar.gz". I > > > >> had to log in as anonymous and not with my email, otherwise > it > > > >> would throw an error (503 Login with USER first. Login > > > failed.). > > > >> The mgh file is inside the gz file too. Thanks again for > your > > > >> time and help. > > > >> > > > >> On Mon, Oct 7, 2019 at 7:06 PM Greve, Douglas N.,Ph.D. > > > >> <DGREVE(a)mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edu> > > > <mailto:DGREVE@mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edu>>> > > > wrote: > > > >> > > > >> ok, I still don't know what is going on. Can you upload > > the > > > >> glmdir and the input to mri_glmfit (ie, the argument of > > the > > > >> --y flag). You can upload it using these instructions: > > > >> > > > >> From the linux command line, > > > >> Create the file you want to upload, eg, > > > >> cd $SUBJECTS_DIR > > > >> tar cvfz subject.tar.gz ./subject > > > >> Now log into our anonymous FTP site: > > > >> ftp surfer.nmr.mgh.harvard.edu > > > <http://surfer.nmr.mgh.harvard.edu> > > > >> <http://surfer.nmr.mgh.harvard.edu> > > > >> It will ask you for a user name: use your email address > > > >> It will ask you for a password: use "anonymous" (no > > quotes) > > > >> cd transfer/incoming > > > >> put subject.tar.gz > > > >> > > > >> Send an email that the file has been and the name of > > > the file. > > > >> > > > >> On 10/7/2019 11:53 AM, Jose Graterol wrote: > > > >>> > > > >>> External Email - Use Caution > > > >>> > > > >>> Hi Douglas, > > > >>> > > > >>> thanks for you answer. I have attached the file to the > > > email. > > > >>> > > > >>> On Mon, Oct 7, 2019 at 5:24 PM Greve, Douglas N.,Ph.D. > > > >>> <DGREVE(a)mgh.harvard.edu > > > <mailto:DGREVE@mgh.harvard.edu> <mailto:DGREVE@mgh.harvard.edu > > > <mailto:DGREVE@mgh.harvard.edu>>> wrote: > > > >>> > > > >>> Let's backup a moment. Can you send the y.ocn.dat > > file > > > >>> that has problematic values? > > > >>> > > > >>> On 10/4/2019 4:58 AM, Jose Graterol wrote: > > > >>>> > > > >>>> External Email - Use Caution > > > >>>> > > > >>>> Dear Freesurfer Experts, > > > >>>> > > > >>>> I would appreciate your help. I will explain > > > first what > > > >>>> I have done and where my problem is. > > > >>>> > > > >>>> I want to measure the cortical thickness in stroke > > > >>>> patients. Therefore I followed Douglas' > instructions > > > >>>> provided in this link > > > >>>> > > > < > > > https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=subj… > > >to > > > >>>> join both affected hemispheres (left or right, > > > >>>> depending on the patient) and to analyze them > > > with xhemi. > > > >>>> > > > >>>> After mri_glmfit and mri_glmfit-sim > (--cwpvalthresh > > > >>>> 0,05 --cache 4 abs/neg) have run I obtained 2 > > > >>>> significant clusters. One in the precentral area, > > > which > > > >>>> I am interested in. When I check the > > > *abs/neg.y.ocn.dat > > > >>>> file the values are all -0.XXXX. If I understood > it > > > >>>> correctly, those should be cortical thickness > > > values in > > > >>>> mm, making those values implausible. For that > > > reason I > > > >>>> made a label of that cluster using the autofill > > > option > > > >>>> from tksurfer. The idea was to obtain the mean > > > cortical > > > >>>> thickness using mris_anatomical_stats after > > > mapping the > > > >>>> label to the subjects. > > > >>>> > > > >>>> Now my questions, what would be the best method > > > to map > > > >>>> the label created from the fsaverage_sym space to > > the > > > >>>> subject space? Or just simply, is this the right > > > way to > > > >>>> do this? or should I check why I am obtaining > those > > > >>>> values in the *y.ocn.dat file? > > > >>>> > > > >>>> Thanks in advance. > > > >>>> > > > >>>> Kind Regards > > > >>>> > > > >>>> Jos? Graterol > > > >>>> > > > >>>> _______________________________________________ > > > >>>> Freesurfer mailing list > > > >>>> Freesurfer(a)nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu> > > > <mailto:Freesurfer@nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu>> > > > >>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > >>> > > > >>> _______________________________________________ > > > >>> Freesurfer mailing list > > > >>> Freesurfer(a)nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu> > > > >>> <mailto:Freesurfer@nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu>> > > > >>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > >>> > > > >>> > > > >>> _______________________________________________ > > > >>> Freesurfer mailing list > > > >>> Freesurfer(a)nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu> > > > <mailto:Freesurfer@nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu>> > > > >>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > >> > > > >> _______________________________________________ > > > >> Freesurfer mailing list > > > >> Freesurfer(a)nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu> > > > >> <mailto:Freesurfer@nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu>> > > > >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > >> > > > >> > > > >> _______________________________________________ > > > >> Freesurfer mailing list > > > >> Freesurfer(a)nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu> > > > <mailto:Freesurfer@nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu>> > > > >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > > > > > _______________________________________________ > > > > Freesurfer mailing list > > > > Freesurfer(a)nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu> > > > <mailto:Freesurfer@nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu>> > > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > > > > > > > > > _______________________________________________ > > > > Freesurfer mailing list > > > > Freesurfer(a)nmr.mgh.harvard.edu > > > <mailto:Freesurfer@nmr.mgh.harvard.edu> > > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > > > > > > _______________________________________________ > > > Freesurfer mailing list > > > Freesurfer(a)nmr.mgh.harvard.edu <mailto: > > Freesurfer(a)nmr.mgh.harvard.edu> > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > > > > > > _______________________________________________ > > > Freesurfer mailing list > > > Freesurfer(a)nmr.mgh.harvard.edu > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > > > > _______________________________________________ > > Freesurfer mailing list > > Freesurfer(a)nmr.mgh.harvard.edu > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >

2 1

[FSL] Hippocampus subregions question
by briend, Frederic 09 Oct '19

09 Oct '19

External Email - Use Caution Dear FSL members, First, thanks for your great work about the hippocampus subfield. Second, I have some question about your subfield/subregions definitions: 1. Can you give me your actual definition of the GC-ML-DG in your last release (FSL6.0) of the hippocampus segmentation? Is-it possible to speak about granular cell layer, like used in the Ho, 2017 (Molecular psychiatry) article? 2. Moreover, is-it possible to say for the the hippocampal tail that it is comprised of portions of CA and dentate gyrus? 3. For molecular layer (ML), is it the ML of the CA subiculum sub volumes or the ML of the DG or both? Thank you in advance, -- Frederic Briend, PhD

2 2

mri_convert no scaling
by Ezequiel Mikulan 09 Oct '19

09 Oct '19

External Email - Use Caution Dear All, I have a similar problem to the one posted here ( Re: [Freesurfer] mri_convert scaling ). In my case I'm trying to convert from ANALYZE (.img, .hdr and .mat) to NIFTI and I need to keep the scale as in the original file. I've tried the -ns 1 flag but it doesn't work (which is not surprising as I've seen on the help page that it is for COR). The file was originally in NIFTI format and was converted to ANALYZE (mri_convert), as I had to use a tool that only accepts this format, and now I'm trying to go back to NIFTI. I would like to know if there is a way of doing it without rescaling the output. These are the contents of the folder:. ├── CC0061_philips_3_55_F_full_normfilter.hdr ├── CC0061_philips_3_55_F_full_normfilter.img ├── CC0061_philips_3_55_F_full_normfilter.mat And this is the output of the command call: $ mri_convert -ns 1 CC0061_philips_3_55_F_full_normfilter.img test.nii mri_convert.bin -ns 1 CC0061_philips_3_55_F_full_normfilter.img test.nii $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from CC0061_philips_3_55_F_full_normfilter.img... analyzeRead() roi_scale 0.003921570 analyzeRead() scaling by 0.003921570 TR=0.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (0.984889, -0.156173, 0.0748529) j_ras = (0.154421, 0.987589, 0.0286842) k_ras = (-0.0784036, -0.0166919, 0.996782) writing to test.nii... Thanks in advance for your time, Best

2 1

How to measure the cortical thickness in tumor patients?
by 王子谦 09 Oct '19

09 Oct '19

External Email - Use Caution Dear developers, right now I'm trying to measure the cortical thickness of the right hemisphere in the left side tumor patients. Here I met some problem. Since the complex intensities in the tumor, it makes the 'recon-all -all' command to be very time consuming(3 days per subject). Do you have any experience dealing with the tumor patients? I also tried the '-lh' flag, but there is only one file named 'rh.curv.stats' in stats folder. The command is:recon-all -s 130 -i 130.nii -qcache -3T -hemi rh -all Do you have any idea about where the problem is? Best wishes, James

2 1

Re: [Freesurfer] Freesurfer Digest, Vol 188, Issue 13
by Juan Rivas 09 Oct '19

09 Oct '19

External Email - Use Caution Hi, thank you for your answer. This file does exist, it contains 59.3 MB. JC. Message: 10 Date: Tue, 8 Oct 2019 22:23:11 +0000 From: "Yendiki, Anastasia" <AYENDIKI(a)mgh.harvard.edu> Subject: Re: [Freesurfer] Tracula To: "freesurfer(a)nmr.mgh.harvard.edu" <freesurfer(a)nmr.mgh.harvard.edu> Message-ID: < BL0PR04MB4833DA61A1A5E068536924AF8A9A0(a)BL0PR04MB4833.namprd04.prod.outlook.com > Content-Type: text/plain; charset="us-ascii" Does this file exist? niiRead(): error opening file /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz Hi, I have been trying to process my diffusion images, but I have a new problem: I wrote this dmrirc: # FreeSurfer SUBJECTS_DIR # T1 images and FreeSurfer segmentations are expected to be found here # setenv SUBJECTS_DIR /autofs/cluster/neuromod/rivas/imagenes/DICOM # Output directory where trac-all results will be saved # Default: Same as SUBJECTS_DIR # set dtroot = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc # Subject IDs # set subjlist = (esq-15-hc) # Default: Run analysis on all subjects # set runlist = (1) # Input diffusion DICOMs (file names relative to dcmroot) # If original DICOMs don't exist, these can be in other image format # but then bvecfile and bvalfile must be specified (see below) # set dcmroot = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc set dcmlist = (dwi.nii.gz) # Diffusion gradient table # Must be specified if inputs are not MGH DICOMs # Three-column format, one row for each volume in the diffusion data set # Default: Read from DICOM header # set bvecfile = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc/processing/31190669_bvecs.dat # Diffusion b-value table # Must be specified if inputs are not MGH DICOMs # Single-column format, one value for each volume in the diffusion data set # Default: Read from DICOM header # set bvalfile = /autofs/cluster/neuromod/rivas/imagenes/DICOM/esq-15-hc/processing/31190669_bvals.dat # Perform registration-based B0-inhomogeneity compensation? # Default: 0 (no) # # set dob0 = 1 # Input B0 field map magnitude DICOMs (file names relative to dcmroot) # Only used if dob0 = 1 # Default: None # # set b0mlist = (huey/fmag/XXX-1.dcm dewey/fmag/XXX-1.dcm louie/fmag/XXX-1.dcm) # Input B0 field map phase DICOMs (file names relative to dcmroot) # Only used if dob0 = 1 # Default: None # # set b0plist = (huey/fphas/XXX-1.dcm dewey/fphas/XXX-1.dcm louie/fphas/XXX-1.dcm) # Echo spacing for field mapping sequence (from sequence printout) # Only used if dob0 = 1 # Default: None # set echospacing = 0.7 # Perform registration-based eddy-current compensation? # Default: 1 (yes) # set doeddy = 1 # Rotate diffusion gradient vectors to match eddy-current compensation? # Only used if doeddy = 1 # Default: 1 (yes) # set dorotbvecs = 1 # Fractional intensity threshold for BET mask extraction from low-b images # This mask is used only if usemaskanat = 0 # Default: 0.3 # set thrbet = 0.5 # Perform diffusion-to-T1 registration by flirt? # Default: 0 (no) # set doregflt = 0 # Perform diffusion-to-T1 registration by bbregister? # Default: 1 (yes) # set doregbbr = 1 # Perform registration of T1 to MNI template? # Default: 1 (yes) # set doregmni = 1 # MNI template # Only used if doregmni = 1 # Default: $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz # set mnitemp = $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz # Perform registration of T1 to CVS template? # Default: 0 (no) # set doregcvs = 0 # CVS template subject ID # Only used if doregcvs = 1 # Default: cvs_avg35 # set cvstemp = donald # Parent directory of the CVS template subject # Only used if doregcvs = 1 # Default: # set cvstempdir = $FREESURFER_HOME/subjects # Use brain mask extracted from T1 image instead of low-b diffusion image? # Has no effect if there is no T1 data # Default: 1 (yes) # set usemaskanat = 1 # Paths to reconstruct # Default: All paths in the atlas # set pathlist = ( lh.cst_AS rh.cst_AS \ lh.unc_AS rh.unc_AS \ lh.ilf_AS rh.ilf_AS \ fmajor_PP fminor_PP \ lh.atr_PP rh.atr_PP \ lh.ccg_PP rh.ccg_PP \ lh.cab_PP rh.cab_PP \ lh.slfp_PP rh.slfp_PP \ lh.slft_PP rh.slft_PP ) # Number of path control points # It can be a single number for all paths or a different number for each of the # paths specified in pathlist # Default: 7 for the forceps major, 6 for the corticospinal tract, # 4 for the angular bundle, and 5 for all other paths # set ncpts = (6 6 5 5 5 5 7 5 5 5 5 5 4 4 5 5 5 5) # List of training subjects # This text file lists the locations of training subject directories # Default: $FREESURFER_HOME/trctrain/trainlist.txt # set trainfile = $FREESURFER_HOME/trctrain/trainlist.txt # Number of "sticks" (anisotropic diffusion compartments) in the bedpostx # ball-and-stick model # Default: 2 # set nstick = 2 # Number of MCMC burn-in iterations # (Path samples drawn initially by MCMC algorithm and discarded) # Default: 200 # set nburnin = 200 # Number of MCMC iterations # (Path samples drawn by MCMC algorithm and used to estimate path distribution) # Default: 7500 # set nsample = 7500 # Frequency with which MCMC path samples are retained for path distribution # Default: 5 (keep every 5th sample) # set nkeep = 5 # Reinitialize path reconstruction? # This is an option of last resort, to be used only if one of the reconstructed # pathway distributions looks like a single curve. This is a sign that the # initial guess for the pathway was problematic, perhaps due to poor alignment # between the individual and the atlas. Setting the reinit parameter to 1 and # rerunning "trac-all -prior" and "trac-all -path", only for the specific # subjects and pathways that had this problem, will attempt to reconstruct them # with a different initial guess. # Default: 0 (do not reinitialize) # set reinit = 0 And I gave this command: [shiraz:processing] (nmr-stable6-env) trac-all -prep -c dmrirc-esq-15-hc But there is an error: INFO: SUBJECTS_DIR is /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing INFO: Diffusion root is /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc Actual FREESURFER_HOME /autofs/cluster/freesurfer/centos7_x86_64/stable6 trac-preproc -c /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/dmrirc.local -log /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/trac-all.log -cmd /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/scripts/trac-all.cmd #------------------------------------- /usr/local/freesurfer/stable6/bin/trac-preproc #------------------------------------- #@# Image corrections Tue Oct 8 17:53:12 EDT 2019 mri_convert --bvec-voxel /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz mri_convert.bin --bvec-voxel /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/esq-15-hc/dmri/dwi_orig.nii.gz niiRead(): error opening file /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz $Id: mri_convert.c,v 1.226 2016/02/26 16:15:24 mreuter Exp $ reading from /autofs/cluster/neuromod/rivas/imagenes/Diffusion_Processing/esq-15-hc/dwi.nii.gz... Linux shiraz.nmr.mgh.harvard.edu 3.10.0-957.1.3.el7.x86_64 #1 SMP Thu Nov 29 14:49:43 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux trac-preproc exited with ERRORS at Tue Oct 8 17:53:12 EDT 2019 > > ------------------------------ > > >

2 1

Label created in the symmetric space
by Jose Graterol 09 Oct '19

09 Oct '19

External Email - Use Caution Dear Freesurfer Experts, I would appreciate your help. I will explain first what I have done and where my problem is. I want to measure the cortical thickness in stroke patients. Therefore I followed Douglas' instructions provided in this link <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=subj…>to join both affected hemispheres (left or right, depending on the patient) and to analyze them with xhemi. After mri_glmfit and mri_glmfit-sim (--cwpvalthresh 0,05 --cache 4 abs/neg) have run I obtained 2 significant clusters. One in the precentral area, which I am interested in. When I check the *abs/neg.y.ocn.dat file the values are all -0.XXXX. If I understood it correctly, those should be cortical thickness values in mm, making those values implausible. For that reason I made a label of that cluster using the autofill option from tksurfer. The idea was to obtain the mean cortical thickness using mris_anatomical_stats after mapping the label to the subjects. Now my questions, what would be the best method to map the label created from the fsaverage_sym space to the subject space? Or just simply, is this the right way to do this? or should I check why I am obtaining those values in the *y.ocn.dat file? Thanks in advance. Kind Regards José Graterol

2 10

Questions re slice thickness, aseg and longitudinal analysis
by David Kamson 09 Oct '19

09 Oct '19

External Email - Use Caution Freesurfers, First of all, I'd like to express my gratitude to the community for the support that keeps researchers like myself afloat! I have a unique set of oncology patients that I want to evaluate for brain atrophy in a retrospective longitudinal analysis. I was thinking about using Aseg.auto results to assess longitudinal volume changes, but before I invest all the time I wanted to check with the community whether this makes any sense at all: The dataset that looks like this: - 22 patients (no control dataset [yet]) - 10-25 MRIs per patient acquired over 2-8 years in relatively uniform intervals - Patients had most of their scans on the same scanner, but scanners differed widely between patients - All patients have axial T1 post gadolinium scans of 1x1x5mm resolution (3D acquisition available in <10%) - About 80% of scans have an axial pre-contrast T1 sequence - All scans are skullstripped (third party algorithm) I'm looking for crude changes, no subtleties; volumes of interest are: - Whole brain volume - White matter volume - Ventricular volume (mainly lateral ventricle) - Subcortical gray matter volume (whole thalamus most importantly) I ran a few test analyses and to my surprise I was able to generate pretty acceptable surfaces, however, topology fixing took about 24H per scan, and I feel aseg.auto contained all the volumetric data I was really interested in. My concrete questions are: 1) Does the full autorecon pipeline affect Aseg.auto? If there is no benefit, I could reduce the per scan analysis time from 28 hours to 1-2 h. 2) Would this low-resolution dataset be accepted by reviewers if used for Aseg? Should I do any quantitative validation beyond a visual quality analysis of Aseg? 3) Can I perform a longitudinal analysis only for the Aseg results? 4) Is it OK to use T1-gad images for the analysis? I'd appreciate any input! Best regards, David O. Kamson, MD PhD Neuro-oncology fellow Johns Hopkins Hospital & National Institutes of Health

2 1

PostDoc on structural MRI processing, Göttingen/Germany
by Focke, Niels 09 Oct '19

09 Oct '19

External Email - Use Caution We are currently offering a PostDoc position focused on structural (brain) MRI processing (100%, pay-grade TDL E13) in the context of a recently started "BigData" initiative on heart&brain imaging in Göttingen/Germany. Funding is currently available until 10/2022. Details can be found in the attached PDF. Applications and enquires should be directed to: Universitätsmedizin Göttingen Klinik für Klinische Neurophysiologie Prof. Dr. Niels Focke Robert-Koch Str. 40 Deutschland/Germany E-Mail: niels.focke(a)med.uni-goettingen.de<mailto:niels.focke@med.uni-goettingen.de>

1 0

Notice about macOS Catalina Notarization and FreeSurfer
by Aditya Kulkarni 08 Oct '19

08 Oct '19

External Email - Use Caution Hello FreeSurfer team, With Apple’s recent release of macOS Catalina, I wanted to test its compatibility with FreeSurfer. Indeed the executables in the freesurfer/bin folder are subject to the “notarization” problem in the OS, such that every dependent executable that is used in a recon-all stream requires the user to allow it manually through security (a process which becomes cumbersome and interrupts recon-all runs with the many libraries it requires). I know you are all working very hard on the software’s development and just wanted to update with this information in case other users are updating to Catalina and going to run a FreeSurfer stream soon. Best, Aditya Kulkarni

1 0

FS dev install error at mri_surf2vol on mac
by Jonathan DuBois 08 Oct '19

08 Oct '19

External Email - Use Caution Hi Freesurfer developers I’m trying to install the development package from github on my mac running osx 10.14.6. I ran CMake with the OSX package without any issue, although when I ran make I got the following error: [ 28%] Linking CXX executable surf2vol ld: warning: could not create compact unwind for _MatSetValuesLocal: stack subq instruction is too different from dwarf stack size … "vnl_nonlinear_minimizer::is_a() const", referenced from: vtable for fs_powell in libutils.a(numerics.cpp.o) ld: symbol(s) not found for architecture x86_64 clang: error: linker command failed with exit code 1 (use -v to see invocation) make[2]: *** [fem_elastic/surf2vol] Error 1 make[1]: *** [fem_elastic/CMakeFiles/surf2vol.dir/all] Error 2 make: *** [all] Error 2 The full error is copied in the attached text file. I have not been able to find anything useful on google so please let me know if you have any ideas. I don’t necessarily need MRI_surf2vol so I could exclude it from the install but I’m guessing that there are other programs that rely on it? Best, Jon

1 0

← Newer
1
...
10
11
12
13
14
15
16
17
18
Older →

Jump to page:

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Freesurfer October 2019