Freesurfer

freesurfer@nmr.mgh.harvard.edu

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30088 discussions

Re: [Freesurfer] denoising and recon-all
by Glasser, Matthew 12 Oct '18

12 Oct '18

External Email - Use Caution What are you using for denoising? Matt. From: <freesurfer-bounces(a)nmr.mgh.harvard.edu<mailto:freesurfer-bounces@nmr.mgh.harvard.edu>> on behalf of Lisa Crystal Krishnamurthy <lkrishnamurthy(a)gsu.edu<mailto:lkrishnamurthy@gsu.edu>> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>> Date: Thursday, October 11, 2018 at 11:05 AM To: "freesurfer(a)nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>" <freesurfer(a)nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>> Subject: [Freesurfer] denoising and recon-all External Email - Use Caution Hi, I have found that denoising the MPRAGE prior to FS recon-all seems to improve the segmentation (see attached pdf). However, it is not clear if the denoising algorithm may cause some signal intensity changes (especially in voxels with partial voluming at the edge of the brain) that violate assumptions of recon-all. Could you help me understand what the assumptions of your algorithm are, and what I need to do to make sure my images conform to those assumptions? Your help is greatly appreciated. Best, -Lisa ________________________________ The materials in this message are private and may contain Protected Healthcare Information or other information of a sensitive nature. If you are not the intended recipient, be advised that any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited. If you have received this email in error, please immediately notify the sender via telephone or return mail.

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Postdoctoral position open to work on brain MRI function & microstructure (Italy)
by Jovicich, Jorge 11 Oct '18

11 Oct '18

External Email - Use Caution [Apologies for cross-posting] The MRI Group of the Center for Mind/Brain Sciences, University of Trento, Italy (https://r.unitn.it/en/cimec/mri/) has an open postdoctoral position to develop/apply MRI acquisition and analyses methods to characterize structural and dynamic functional connectivity (deadline October 30, 2018). More information about our group, the call and application links can be found here: https://r.unitn.it/en/cimec/mri/opportunities Feel free to distribute this announcement to potentially interested people. Best, Jorge Jovicich -- Jorge Jovicich, Ph.D. MR Lab Head Center for Mind/Brain Sciences University of Trento, Via delle Regole, 101 38100 Mattarello (TN) Italy Telephone: +39-0461-28 3064 Fax: +39-0461-28-3066 Email: jorge.jovicich(a)unitn.it MRI Methods Group: http://r.unitn.it/en/cimec/mri CIMEC: http://www.cimec.unitn.it/

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Desikan Atlas in MNI Space
by ece ulug 11 Oct '18

11 Oct '18

External Email - Use Caution Dear All, I am a master student in Turkey. For my thesis; I have to do exactly this; Desikan atlas (which I have in the tailarach space) in the MNI space, as a file (Desikan_mni.nii). I am aware that transforming from one space to the other is not a very good idea but nevertheless I would like to test some computations. Anyone can attach/send a link/know of if exists such a file? the aparc+aseg.mgz in fsaverage is in mni305 space. will that work? This is a mail dated 2014, freesurfer maillist, and I find this via internet. Desikan Atlas 'aparc+aseg.mgz' or .nii format where should I look for these file? And what is your opinion, is it good idea? (To transform Desikan atlas to mni space for visualization VBM results?) Thanks in advance E. Ece Uluğ

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Hippocampal/Amygdala subfield segmentation error - Freesurfer devel-20180612
by Sally Grace 11 Oct '18

11 Oct '18

External Email - Use Caution Dear experts, I am running the hippocampal/amygdala segmentation on T1's preprocessed in -reconall in FS 5.3 using FS devel-20180612 using the command segmentHA_T1.sh bert [SUBJECTS_DIR] This has worked successfully for roughly 1000 participants but for one participant I keep getting the error below, despite several troubleshooting attempts. The error appears to be "Fitting mesh to synthetic image resulted in no deformation" - I have had a look at the hippocampus masks and they appear to be registered incorrectly, despite the grey matter volume output from -reconall having no errors. Do you have any idea what I can do to troubleshoot or fix this? Thanks in advance, Sally. Error Log: ------------------------------ USER valentil HOST m3a007 PROCESSID 29440 PROCESSOR x86_64 OS Linux Linux m3a007 3.10.0-514.26.1.el7.x86_64 #1 SMP Thu Jun 29 16:05:25 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux ------------------------------ #-------------------------------------------- #@# Hippocampal Subfields processing (T1) left Tue Sep 25 10:18:56 AEST 2018 ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/usr/local/freesurfer/devel-20180612/MCRv84//runtime/glnxa64:/usr/local/freesurfer/devel-20180612/MCRv84//bin/glnxa64:/usr/local/freesurfer/devel-20180612/MCRv84//sys/os/glnxa64:/usr/local/freesurfer/devel-20180612/lib/tcltktixblt:/usr/local/freesurfer/devel-20180612/lib/qt/lib:/usr/local/freesurfer/devel-20180612/lib/petsc/lib:/usr/local/freesurfer/devel-20180612/lib/KWWidgets/lib/KWWidgets:/usr/local/freesurfer/devel-20180612/lib/cuda/lib64:/usr/local/freesurfer/devel-20180612/lib/cuda/lib:/usr/local/freesurfer/devel-20180612/lib/bem:/usr/local/freesurfer/devel-20180612/lib:/usr/local/freesurfer/devel-20180612/lib/vtk/lib/vtk-5.6:/usr/local/fsl/5.0.11/fsl/lib:/usr/local/libjpeg-turbo/1.4.2/lib64:/usr/local/cuda/7.5/extras/CUPTI/lib64:/usr/local/cuda/7.5/lib64:/usr/local/cuda/7.5/lib:/usr/local/cuda/7.5/lib64/stubs:/opt/munge-0.5.11/lib:/opt/slurm-17.11.4/lib:/opt/slurm-17.11.4/lib/slurm:/usr/local/tigervnc/1.8.0/lib64:/usr/local/tigervnc/1.8.0/lib:/opt/munge-0.5.11/lib:/opt/slurm-17.11.4/lib:/opt/slurm-17.11.4/lib/slurm::/usr/local/freesurfer/devel-20180612/MCRv84//sys/opengl/lib/glnxa64:/usr/local/freesurfer/devel-20180612/lib/tcltktixblt:/usr/local/freesurfer/devel-20180612/lib/qt/lib:/usr/local/freesurfer/devel-20180612/lib/petsc/lib:/usr/local/freesurfer/devel-20180612/lib/KWWidgets/lib/KWWidgets:/usr/local/freesurfer/devel-20180612/lib/cuda/lib64:/usr/local/freesurfer/devel-20180612/lib/cuda/lib:/usr/local/freesurfer/devel-20180612/lib/bem:/usr/local/freesurfer/devel-20180612/lib:/usr/local/freesurfer/devel-20180612/lib/vtk/lib/vtk-5.6:/usr/local/fsl/5.0.11/fsl/lib:/usr/local/libjpeg-turbo/1.4.2/lib64:/usr/local/cuda/7.5/extras/CUPTI/lib64:/usr/local/cuda/7.5/lib64:/usr/local/cuda/7.5/lib:/usr/local/cuda/7.5/lib64/stubs:/opt/munge-0.5.11/lib:/opt/slurm-17.11.4/lib:/opt/slurm-17.11.4/lib/slurm:/usr/local/tigervnc/1.8.0/lib64:/usr/local/tigervnc/1.8.0/lib:/opt/munge-0.5.11/lib:/opt/slurm-17.11.4/lib:/opt/slurm-17.11.4/lib/slurm: Registering imageDump.mgz to hippocampal mask from ASEG $Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $ --mov: Using imageDump.mgz as movable/source volume. --dst: Using /home/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz as target volume. --lta: Output transform as trash.lta . --mapmovhdr: Will save header adjusted movable as imageDump_coregistered.mgz ! --sat: Using saturation 50 in M-estimator! reading source 'imageDump.mgz'... reading target '/home/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz'... Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) Type Source : 0 Type Target : 3 ensure both FLOAT (3) Reordering axes in mov to better fit dst... ( -1 3 -2 ) MRIreorder() ----------- xdim=-1 ydim=3 zdim=-2 src 131 241 99, 0.250000 0.250000 0.250000 dst 131 99 241, 0.250000 0.250000 0.250000 Determinant after swap : 0.015625 Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241) Dst: (1, 1, 1)mm and dim (36, 31, 52) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 0 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (36, 31, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (36, 31, 52) voxels. -- Resampled: (1, 1, 1)mm and (36, 31, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 Registration::computeMultiresRegistration - computing centroids - computing initial transform -- using translation info - Get Gaussian Pyramid Limits ( min size: 16 max size: -1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 0.8149113483238 0 1.0000000000000 0 0.6091444604181 0 0 1.0000000000000 -10.4099368843102 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 0 S( 36 31 61 ) T( 36 31 61 ) Iteration(f): 1 -- diff. to prev. transform: 8.43588 Iteration(f): 2 -- diff. to prev. transform: 6.13157 Iteration(f): 3 -- diff. to prev. transform: 5.20397 Iteration(f): 4 -- diff. to prev. transform: 5.47621 Iteration(f): 5 -- diff. to prev. transform: 8.03452 max it: 5 reached! - final transform: Tf = [ ... 0.9476591624290 -0.1857780011274 0.2596702642998 -6.9596494990127 0.2461981066435 0.9430226622820 -0.2238185664945 2.3191313534194 -0.2032943780569 0.2760340426825 0.9394022584233 -11.1737673430040 0 0 0 1.0000000000000 ] - final iscale: If = 1 ********************************************************** * * WARNING: Registration did not converge in 5 steps! * Problem might be ill posed. * Please inspect output manually! * ********************************************************** Final Transform: Adjusting final transform due to initial resampling (voxel or size changes) ... M = [ ... -0.2369147906072 0.0649175660749 0.0464445002818 20.3444784868448 -0.0615495266609 -0.0559546416236 -0.2357556655705 36.6877108257354 0.0508235945142 0.2348505646058 -0.0690085106706 -14.6334717181848 0 0 0 1.0000000000000 ] Determinant : -0.015625 writing output transformation to trash.lta ... converting VOX to RAS and saving RAS2RAS... mapmovhdr: Changing vox2ras MOV header (to map to DST) ... To check aligned result, run: freeview -v /home/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz imageDump_coregistered.mgz Registration took 0 minutes and 1 seconds. Thank you for using RobustRegister! If you find it useful and use it for a publication, please cite: Highly Accurate Inverse Consistent Registration: A Robust Approach M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010. http://dx.doi.org/10.1016/j.neuroimage.2010.07.020 http://reuter.mit.edu/papers/reuter-robreg10.pdf Highly Accurate Inverse Consistent Registration: A Robust ...<http://reuter.mit.edu/papers/reuter-robreg10.pdf> reuter.mit.edu Highly Accurate Inverse Consistent Registration: A Robust Approach Martin Reutera,b,c,∗, H. Diana Rosasa,b, Bruce Fischla,b,c aMassachusetts General Hospital / Harvard Medical School, Boston, MA, USA bMartinos Center for Biomedical Imaging, 143 13th Street, Charlestown, MA, USA cMIT Computer Science and AI Lab, Cambridge, MA, USA Abstract ... $Id: mri_robust_register.cpp,v 1.77 2016/01/20 23:36:17 greve Exp $ --mov: Using imageDump.mgz as movable/source volume. --dst: Using /home/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz as target volume. --lta: Output transform as trash.lta . --mapmovhdr: Will save header adjusted movable as imageDump_coregistered.mgz ! --affine: Enableing affine transform! --sat: Using saturation 50 in M-estimator! reading source 'imageDump.mgz'... reading target '/home/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz'... Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) Type Source : 0 Type Target : 3 ensure both FLOAT (3) Reordering axes in mov to better fit dst... ( -1 3 -2 ) MRIreorder() ----------- xdim=-1 ydim=3 zdim=-2 src 131 241 99, 0.250000 0.250000 0.250000 dst 131 99 241, 0.250000 0.250000 0.250000 Determinant after swap : 0.015625 Mov: (0.25, 0.25, 0.25)mm and dim (131, 99, 241) Dst: (1, 1, 1)mm and dim (36, 31, 52) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 0 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (36, 31, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (36, 31, 52) voxels. -- Resampled: (1, 1, 1)mm and (36, 31, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 Registration::computeMultiresRegistration - computing centroids - computing initial transform -- using translation info - Get Gaussian Pyramid Limits ( min size: 16 max size: -1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 0 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 0.8149148880414 0 1.0000000000000 0 0.6090566088288 0 0 1.0000000000000 -10.4098892270167 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 0 S( 36 31 61 ) T( 36 31 61 ) Iteration(f): 1 -- diff. to prev. transform: 41.9486 Iteration(f): 2 -- diff. to prev. transform: 11.6817 Iteration(f): 3 -- diff. to prev. transform: 6.67838 Iteration(f): 4 -- diff. to prev. transform: 7.29474 Iteration(f): 5 -- diff. to prev. transform: 6.81314 max it: 5 reached! - final transform: Tf = [ ... 0.7187784317010 -0.7039049689666 0.2089453505114 9.6512053486057 0.0746036190898 0.1293914974162 0.0117417560783 14.1524707759350 -0.2465560963278 -0.6917283813412 1.0760848577522 2.1884563417970 0 0 0 1.0000000000000 ] - final iscale: If = 1 ********************************************************** * * WARNING: Registration did not converge in 5 steps! * Problem might be ill posed. * Please inspect output manually! * ********************************************************** Final Transform: Adjusting final transform due to initial resampling (voxel or size changes) ... M = [ ... -0.1796946067577 0.0522363297057 0.1759762437984 14.8124989197474 -0.0186509047068 0.0029354397057 -0.0323478742666 20.2771618184274 0.0616390300951 0.2690212102028 0.1729321033528 -29.4307376768930 0 0 0 1.0000000000000 ] Determinant : -0.00250544 Decompose into Rot * Shear * Scale : Rot = [ ... -0.8007108168802 0.0158873366257 0.5988403629232 -0.5106339926748 0.5045973184270 -0.6961569304125 0.3132333207947 0.8632086299216 0.3959239168991 ] Shear = [ ... 1.0000000000000 0.1745654451980 -0.3575927026144 0.2370458412539 1.0000000000000 0.6912992608189 -0.4065670242970 0.5788093472423 1.0000000000000 ] Scale = diag([ 0.1727145993906 0.2345325414370 0.1963688302739 ]) writing output transformation to trash.lta ... converting VOX to RAS and saving RAS2RAS... mapmovhdr: Changing vox2ras MOV header (to map to DST) ... To check aligned result, run: freeview -v /home/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/tmp/hippoSF_T1_v21_left//hippoAmygBinaryMask_autoCropped.mgz imageDump_coregistered.mgz Registration took 0 minutes and 1 seconds. Thank you for using RobustRegister! If you find it useful and use it for a publication, please cite: Highly Accurate Inverse Consistent Registration: A Robust Approach M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010. http://dx.doi.org/10.1016/j.neuroimage.2010.07.020 http://reuter.mit.edu/papers/reuter-robreg10.pdf Highly Accurate Inverse Consistent Registration: A Robust ...<http://reuter.mit.edu/papers/reuter-robreg10.pdf> reuter.mit.edu Highly Accurate Inverse Consistent Registration: A Robust Approach Martin Reutera,b,c,∗, H. Diana Rosasa,b, Bruce Fischla,b,c aMassachusetts General Hospital / Harvard Medical School, Boston, MA, USA bMartinos Center for Biomedical Imaging, 143 13th Street, Charlestown, MA, USA cMIT Computer Science and AI Lab, Cambridge, MA, USA Abstract ... Reading contexts of file /usr/local/freesurfer/devel-20180612/average/HippoSF/atlas/compressionLookupTable.txt Constructing image-to-world transform from header information (asegModCHA.mgz) Constructing image-to-world transform from header information (/home/valentil/kg98/Valentina/ENIGMA_sex_differences/Scripts/rerun_reconall/NIAAA_MRI00032_1-out/tmp/hippoSF_T1_v21_left/imageDump.mgz) Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Transforming points Reading contexts of file /usr/local/freesurfer/devel-20180612/average/HippoSF/atlas/compressionLookupTable.txt -------------- Making Left-Cerebral-Cortex map to reduced label 1 Making alveus map to reduced label 1 Making subiculum-body map to reduced label 1 Making subiculum-head map to reduced label 1 Making Hippocampal_tail map to reduced label 1 Making molecular_layer_HP-body map to reduced label 1 Making molecular_layer_HP-head map to reduced label 1 Making GC-ML-DG-body map to reduced label 1 Making GC-ML-DG-head map to reduced label 1 Making CA4-body map to reduced label 1 Making CA4-head map to reduced label 1 Making CA1-body map to reduced label 1 Making CA1-head map to reduced label 1 Making CA3-body map to reduced label 1 Making CA3-head map to reduced label 1 Making HATA map to reduced label 1 Making fimbria map to reduced label 1 Making presubiculum-body map to reduced label 1 Making presubiculum-head map to reduced label 1 Making parasubiculum map to reduced label 1 Making Lateral-nucleus map to reduced label 1 Making Paralaminar-nucleus map to reduced label 1 Making Basal-nucleus map to reduced label 1 Making Accessory-Basal-nucleus map to reduced label 1 Making Corticoamygdaloid-transitio map to reduced label 1 Making Central-nucleus map to reduced label 1 Making Cortical-nucleus map to reduced label 1 Making Medial-nucleus map to reduced label 1 Making Anterior-amygdaloid-area-AAA map to reduced label 1 -------------- Making Left-Cerebral-White-Matter map to reduced label 2 -------------- Making Left-Lateral-Ventricle map to reduced label 3 -------------- Making Left-choroid-plexus map to reduced label 4 -------------- Making hippocampal-fissure map to reduced label 5 Making Unknown map to reduced label 5 -------------- Making Left-VentralDC map to reduced label 6 -------------- Making Left-Putamen map to reduced label 7 -------------- Making Left-Pallidum map to reduced label 8 -------------- Making Left-Thalamus-Proper map to reduced label 9 -------------- Making Left-Accumbens-area map to reduced label 10 -------------- Making Left-Caudate map to reduced label 11 Smoothing mesh collection with kernel size 3.000000 ...Smoothing class: 0 Smoothing class: 1 Smoothing class: 2 Smoothing class: 3 Smoothing class: 4 Smoothing class: 5 Smoothing class: 6 Smoothing class: 7 Smoothing class: 8 Smoothing class: 9 Smoothing class: 10 done numberOfContrasts = 1 Image: 4 AtlasMeshToIntensityImage 1 0.398942 SLIDING L-BFGS Verbose: 0 MaximalDeformationStopCriterion: 1e-10 LineSearchMaximalDeformationIntervalStopCriterion: 1e-10 MaximumNumberOfIterations: 1000 BFGS-MaximumMemoryLength: 12 Resolution 1, iteration 1 Did one deformation step of max. 0 voxels in 4.2457 seconds minLogLikelihoodTimesPrior = 2.4171e-16 Smoothing mesh collection with kernel size 2.000000 ...Smoothing class: 0 Smoothing class: 1 Smoothing class: 2 Smoothing class: 3 Smoothing class: 4 Smoothing class: 5 Smoothing class: 6 Smoothing class: 7 Smoothing class: 8 Smoothing class: 9 Smoothing class: 10 done numberOfContrasts = 1 Image: 4 AtlasMeshToIntensityImage 1 0.398942 SLIDING L-BFGS Verbose: 0 MaximalDeformationStopCriterion: 1e-10 LineSearchMaximalDeformationIntervalStopCriterion: 1e-10 MaximumNumberOfIterations: 1000 BFGS-MaximumMemoryLength: 12 Resolution 2, iteration 1 Did one deformation step of max. 0 voxels in 4.2086 seconds minLogLikelihoodTimesPrior = 2.4171e-16 Fitting mesh to synthetic image from ASEG took 21.6669 seconds Error using segmentSubjectT1_autoEstimateAlveusML (line 533) Fitting mesh to synthetic image resulted in no deformation @#@FSTIME 2018:09:25:10:18:56 run_segmentSubjectT1_autoEstimateAlveusML.sh N 13 e 65.81 S 1.73 U 34.89 P 55% M 1012356 F 271 R 219755 W 0 c 3155 w 11451 I 146312 O 477024 L 11.09 10.51 11.31 @#@FSLOADPOST 2018:09:25:10:20:02 run_segmentSubjectT1_autoEstimateAlveusML.sh N 13 35.39 16.50 13.25 Linux m3a007 3.10.0-514.26.1.el7.x86_64 #1 SMP Thu Jun 29 16:05:25 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux T1 hippocampal subfields exited with ERRORS at Tue Sep 25 10:20:02 AEST 2018 -- Sally Grace, BSc (Hons), PhD Candidate. Centre for Mental Health, Faculty of Health, Arts and Design Swinburne University of Technology

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denoising and recon-all
by Lisa Crystal Krishnamurthy 11 Oct '18

11 Oct '18

External Email - Use Caution Hi, I have found that denoising the MPRAGE prior to FS recon-all seems to improve the segmentation (see attached pdf). However, it is not clear if the denoising algorithm may cause some signal intensity changes (especially in voxels with partial voluming at the edge of the brain) that violate assumptions of recon-all. Could you help me understand what the assumptions of your algorithm are, and what I need to do to make sure my images conform to those assumptions? Your help is greatly appreciated. Best, -Lisa

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Recon-all with/without T2 give the same stats
by Jianzhong Chen 11 Oct '18

11 Oct '18

External Email - Use Caution Dear All, I tried recon-all with/without T2 image using FS 5.3.0. Then I found that the aseg stats and aparc stats with/without T2 are exactly the same. When I check the pial surfaces, I can see that they are very similar but not the same. So is it reasonable to see exactly the same stats? An example of the pial surfaces with/without T2 is attached here. The red line is lh.pial with T2 and the yellow line is the lh.pial without T2 My command for recon-all with T2 is: recon-all -s ${subject} -i T1.nii -all -T2 T2.nii -T2pial -sd ${T2_dir} My command for recon-all without T2 is: recon-all -s ${subject} -i T1.nii -all -sd ${T1_dir} Thanks, Jianzhong

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Difference between 2 mgh files in surf dir (or: effects of 'mri_surf2surf --prune' without smoothing)
by ts+ml＠rcmd.org 11 Oct '18

11 Oct '18

External Email - Use Caution Dear FreeSurfer experts, what is the difference between the following 2 files found in a subject's surf/ directory: * file #1: lh.area.fsaverage.mgh * file #2: lh.area.fwhm0.fsaverage.mgh I see in the logs (<SUBJECT>/scripts/recon-all.log), that file #2 is generated from file #1 by running the following command: mri_surf2surf --prune --s fsaverage --hemi lh --fwhm 0 --sval lh.area.fsaverage.mgh --tval lh.area.fwhm0.fsaverage.mgh --cortex I know that if fwhm > 0, spatial smoothing will be applied. But what happens if fwhm is 0? From a quick comparison of the data in both files, they seem to be identical, but the hashes of the files differ. Is the difference only about the header? Thanks for your help, -- Dr. Tim Schäfer Postdoc Computational Neuroimaging Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany

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masking cortical thickness
by N Saf 11 Oct '18

11 Oct '18

External Email - Use Caution Dear Freesurfer Experts, I wonder how can I show the cortical thickness surface on just one region of my interest in Freeview. assume that I want to see the thickness surface of one of the regions in DKTatlas. does it work if I make a binary mask of that specific region on aparcDKTatlas.annot and then multiply it to the ?h.thickness surface ?? any help will be appreciated. Best Regards, Nazanin

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Pet Surfer
by David.Mackarthy 10 Oct '18

10 Oct '18

External Email - Use Caution Dear Dr Greeve, I am following petsurfer pipeline to do surface based analysis of PET-FDG data and I have interest in doing partial volume correction. I would like to inquire about: 1) Can partial volume correction be applied on white mater, or just on gray matter? 2) Can partial volume correction be applied between gray matter and CSF? According to Muller-Gartner (MG) Method we segment the brain to gray/white and csf, then we apply partial volume correction only on gray matter. why not on the other parts. I apologies if my question is basics and I appreciate your input. Thanks for publishing this valuable manuscript https://www.ncbi.nlm.nih.gov/pubmed/26915497 With all respect! Dev

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Moving VOI to FreeSurfer Space
by Sarosh, Cyrus 10 Oct '18

10 Oct '18

External Email - Use Caution Hello FreeSurfer Experts, I'm trying to find the best method and command to move a hand drawn VOI in original MRI space to FreeSurfer space. These scans have already been ran through the entire FreeSurfer process and the hand drawn VOIs have already been converted to .mgz files. Thanks for your help Thanks, Cyrus Cyrus Sarosh [Description: Description: Description: Description: Description: Description: Description: cid:image002.png@01CC3BB7.F8D9A990] Research Area Specialist Associate Department of Radiology Functional Neuroimaging, Cognitive and Mobility Laboratory Domino's Farms Lobby B Suite 1200 24 Frank Lloyd Wright Dr PO BOX 362 Ann Arbor, MI 48106 t: 734-998-4790 f: 734-998-8403 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues

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