Freesurfer October 2020

freesurfer@nmr.mgh.harvard.edu

112 participants
180 discussions

white matter surface extends too much into the cortex
by Caroline Chwiesko 07 Oct '20

07 Oct '20

External Email - Use Caution Dear Freesurfer team, when checking the output after recon-all i am having the issue that the white matter surface extends too much into the cortex. I observe this in most of my subjects (mostly in the posterior part of the brain) so it seems to be a systematic issue. I found in the archive that others had a similar problem and were asked to upload one subject to get a recommendation on how to change certain default parameters in recon-all to fix this problem. I was wondering if I could also upload a subject and get a recommendation for my data set on how to fix this issue ? Thank you very much in advance! Carolin

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Re: [Freesurfer] Hippocampal subfields error
by Emily K Clarke 07 Oct '20

07 Oct '20

External Email - Use Caution Hi Eugenio, No, I don't think so - we're running recon-all and using the hippocampal pipeline from FS 6.0. I bug tested with the FS 6.0dev version and found that that hippocampal pipeline worked okay, but since we're combining data with an older dataset we need to be able to extract the older hippocampal subfields, starting with the recon-all hippocampal subfields processing. Thanks, Emily Date: Wed, 9 Sep 2020 21:21:41 +0000 From: "Iglesias Gonzalez, Juan E." <JIGLESIASGONZALEZ(a)mgh.harvard.edu<mailto:JIGLESIASGONZALEZ@mgh.harvard.edu>> Subject: Re: [Freesurfer] Hippocampal subfields error To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>> Message-ID: <25DF95C0-873A-4906-9D51-A94CF022375D(a)mgh.harvard.edu<mailto:25DF95C0-873A-4906-9D51-A94CF022375D@mgh.harvard.edu>> Content-Type: text/plain; charset="utf-8" Hi Emily. Is it possibly that you may be mixing FS versions, by any chance? Cheers, /Eugenio Juan Eugenio Iglesias Senior research fellow CMIC (UCL), MGH (HMS) and CSAIL (MIT) https://urldefense.com/v3/__http://www.jeiglesias.com__;!!OToaGQ!54kYqoLJMa…<https://urldefense.com/v3/__http:/www.jeiglesias.com__;!!OToaGQ!54kYqoLJMaz…> From: <freesurfer-bounces(a)nmr.mgh.harvard.edu<mailto:freesurfer-bounces@nmr.mgh.harvard.edu>> on behalf of Emily K Clarke <ekc23(a)duke.edu<mailto:ekc23@duke.edu>> Reply-To: Freesurfer support list <freesurfer(a)nmr.mgh.harvard.edu<mailto:freesurfer@nmr.mgh.harvard.edu>> Date: Wednesday, September 9, 2020 at 15:53 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] Hippocampal subfields error External Email - Use Caution Hi Eugenio, We?re still running into the issue where running recon-all with the hippocampal subfields tag isn?t actually generating any of the hippocampal files. I?ve included our full recon-all log below. We already examined the scans, and they look perfectly normal ? do you know why this might be happening? Thanks, Emily Clarke-Rubright Morey Neuroimaging Lab The whole log message: #-------------------------------------------- #@# Hippocampal Subfields processing (T1 only) left Mon Aug 10 18:54:21 EDT 2020 ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/usr/local/packages/freesurfer_v6.0.0/MCRv80/runtime/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/bin/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/os/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/native_threads:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/server:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/client:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64:/usr/lib64/qt5/plugins/platforms:/usr/local/packages/ants-2.3.4/lib:/usr/lib64/openmpi/lib: Warning: application is running on a locale different from the original platform locale. Warning: No display specified. You will not be able to display graphics on the screen. Registering imageDump.mgz to hippocampal mask from ASEG This file does not contain MRI parameters $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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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 '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. 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 (38, 40, 59) 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 (38, 40, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 40, 59) voxels. -- Resampled: (1, 1, 1)mm and (38, 40, 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 1.1459029727155 0 1.0000000000000 0 -0.0876360601093 0 0 1.0000000000000 -2.9034875996918 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 20 30 ) T( 19 20 30 ) Iteration(f): 1 -- diff. to prev. transform: 7.11234 Iteration(f): 2 -- diff. to prev. transform: 5.92976 Iteration(f): 3 -- diff. to prev. transform: 7.00993 Iteration(f): 4 -- diff. to prev. transform: 4.91305 Iteration(f): 5 -- diff. to prev. transform: 0.781018 max it: 5 reached! Resolution: 0 S( 38 40 61 ) T( 38 40 61 ) Iteration(f): 1 -- diff. to prev. transform: 8.1296 Iteration(f): 2 -- diff. to prev. transform: 1.32542 Iteration(f): 3 -- diff. to prev. transform: 0.660013 Iteration(f): 4 -- diff. to prev. transform: 0.272219 Iteration(f): 5 -- diff. to prev. transform: 0.0943126 max it: 5 reached! - final transform: Tf = [ ... 0.9986737402682 -0.0371977207162 0.0355962086772 0.4749903609000 0.0194355513105 0.9126027300469 0.4083852549446 -14.0571487737101 -0.0476761978762 -0.4071517980857 0.9121153400046 9.9144950322154 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 = [ ... -0.2496684350671 0.0088990521693 0.0092994301791 34.3717771918614 -0.0048588878276 0.1020963137361 -0.2281506825117 16.0960341057205 0.0119190494690 0.2280288350011 0.1017879495214 -5.4978401025085 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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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. https://urldefense.com/v3/__http://dx.doi.org/10.1016/j.neuroimage.2010.07.…<https://urldefense.com/v3/__http:/dx.doi.org/10.1016/j.neuroimage.2010.07.0…> https://urldefense.com/v3/__http://reuter.mit.edu/papers/reuter-robreg10.pd…<https://urldefense.com/v3/__http:/reuter.mit.edu/papers/reuter-robreg10.pdf…> $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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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 '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. 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 (38, 40, 59) 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 (38, 40, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 40, 59) voxels. -- Resampled: (1, 1, 1)mm and (38, 40, 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 1.1459028059189 0 1.0000000000000 0 -0.0876339827813 0 0 1.0000000000000 -2.9034861096682 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 20 30 ) T( 19 20 30 ) Iteration(f): 1 -- diff. to prev. transform: 7.39891 Iteration(f): 2 -- diff. to prev. transform: 6.07128 Iteration(f): 3 -- diff. to prev. transform: 6.45354 Iteration(f): 4 -- diff. to prev. transform: 9.61165 Iteration(f): 5 -- diff. to prev. transform: 6.45157 max it: 5 reached! Resolution: 0 S( 38 40 61 ) T( 38 40 61 ) Iteration(f): 1 -- diff. to prev. transform: 18.1397 Iteration(f): 2 -- diff. to prev. transform: 10.4781 Iteration(f): 3 -- diff. to prev. transform: 4.71708 Iteration(f): 4 -- diff. to prev. transform: 2.16703 Iteration(f): 5 -- diff. to prev. transform: 0.732237 max it: 5 reached! - final transform: Tf = [ ... 1.1003251237945 0.4596695415609 0.1319823678432 -15.3375075204562 -0.0809129505733 1.0958357713115 0.4294815397408 -16.7169936140805 -0.0871431123400 -0.3796938017010 0.9044729973197 10.3182069614882 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 = [ ... -0.2750812479116 0.0329955927294 -0.1149173912565 38.1277797083535 0.0202282358422 0.1073703848787 -0.2739589559572 15.8057165631404 0.0217857755217 0.2261182492690 0.0949234628220 -5.6011779490725 0 0 0 1.0000000000000 ] Determinant : -0.0203612 Decompose into Rot * Shear * Scale : Rot = [ ... -0.9711353975406 0.1063152910440 -0.2135254048909 0.2376286779964 0.3535137619413 -0.9047434064484 0.0207038894236 0.9293681073764 0.3685732898005 ] Shear = [ ... 1.0000000000000 -0.0073421724799 0.1576683162397 -0.0067818833526 1.0000000000000 -0.0678198022274 0.1779196232492 -0.0828533754661 1.0000000000000 ] Scale = diag([ 0.2723989962754 0.2516119340902 0.3073865945084 ]) 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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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. https://urldefense.com/v3/__http://dx.doi.org/10.1016/j.neuroimage.2010.07.…<https://urldefense.com/v3/__http:/dx.doi.org/10.1016/j.neuroimage.2010.07.0…> https://urldefense.com/v3/__http://reuter.mit.edu/papers/reuter-robreg10.pd…<https://urldefense.com/v3/__http:/reuter.mit.edu/papers/reuter-robreg10.pdf…> Reading contexts of file /usr/local/packages/freesurfer_v6.0.0/average/HippoSF/atlas/compressionLookupTable.txt Constructing image-to-world transform from header information (asegMod.mgz) Returning the following transform: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 422 Debug: Off Object Name: Observers: none Matrix: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Offset: [132.04, -111.801, 144.75] Center: [0, 0, 0] Translation: [132.04, -111.801, 144.75] Inverse: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Singular: 0 Constructing image-to-world transform from header information (/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… ) Returning the following transform: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 579 Debug: Off Object Name: Observers: none Matrix: 0.275081 -0.0329956 0.114917 0.0217858 0.226118 0.0949235 -0.0202282 -0.10737 0.273959 Offset: [-38.0874, -33.402, 1.94475] Center: [0, 0, 0] Translation: [-38.0874, -33.402, 1.94475] Inverse: 3.54297 -0.162038 -1.43002 -0.38743 3.81537 -1.15946 0.109759 1.48336 3.09018 Singular: 0 Transform before cropping: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 581 Debug: Off Object Name: Observers: none Matrix: -0.275081 0.0329956 -0.114917 0.0202282 0.10737 -0.273959 0.0217858 0.226118 0.0949235 Offset: [170.128, 142.806, 78.3988] Center: [0, 0, 0] Translation: [170.128, 142.806, 78.3988] Inverse: -3.54297 1.43002 -0.162038 0.38743 1.15946 3.81537 -0.109759 -3.09018 1.48336 Singular: 0 WorldToImageTransform before cropping: AffineTransform (0x2b5e6a47e8f0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 583 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [132.04, 144.75, 111.801] Center: [0, 0, 0] Translation: [132.04, 144.75, 111.801] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 m_BoundingBoxSize: [131, 241,99] minimalMappedCoordinate: [123.105, 115.958,78.3988] maximalMappedCoordinate: [178.047, 171.204,144.802] Cropping with min [118 110 72] and max [184 177 151] m_OriginalImageRegion before checking margins: ImageRegion (0x2b5e69a829b8) Dimension: 3 Index: [118, 110, 72] Size: [67, 68, 80] m_CroppedImageRegion before checking margins: ImageRegion (0x2b5e69a82980) Dimension: 3 Index: [0, 0, 0] Size: [67, 68, 80] m_OriginalImageRegion after checking margins: ImageRegion (0x2b5e69a829b8) Dimension: 3 Index: [118, 110, 72] Size: [67, 68, 80] m_CroppedImageRegion after checking margins: ImageRegion (0x2b5e69a82980) Dimension: 3 Index: [0, 0, 0] Size: [67, 68, 80] Transform after cropping: AffineTransform (0x2b5e6a48e220) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 740 Debug: Off Object Name: Observers: none Matrix: -0.275081 0.0329956 -0.114917 0.0202282 0.10737 -0.273959 0.0217858 0.226118 0.0949235 Offset: [52.1278, 32.8057, 6.39882] Center: [0, 0, 0] Translation: [52.1278, 32.8057, 6.39882] Inverse: -3.54297 1.43002 -0.162038 0.38743 1.15946 3.81537 -0.109759 -3.09018 1.48336 Singular: 0 WorldToImageTransform after cropping: AffineTransform (0x2b5e6a47e8f0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 741 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [14.0404, 34.7505, 39.8009] Center: [0, 0, 0] Translation: [14.0404, 34.7505, 39.8009] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 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 Careful here: the applied transformation will turn positive tetrahedra into negative ones. -0.275081 0.0329956 -0.114917 0.0202282 0.10737 -0.273959 0.0217858 0.226118 0.0949235 determinant: -0.0203612 Starting to swap the point assignments of each tetrahedron... Warning: This concatenation operation includes an empty array with an incorrect number of rows. Concatenation including empty arrays will require all arrays to have the same number of rows in a future release. [ > In segmentSubjectT1_autoEstimateAlveusML at 325] Reading contexts of file /usr/local/packages/freesurfer_v6.0.0/average/HippoSF/atlas/compressionLookupTable.txt -------------- 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 alveus map to reduced label 2 Making Hippocampal_tail map to reduced label 2 Making HATA map to reduced label 2 Making fimbria map to reduced label 2 Making parasubiculum map to reduced label 2 Making hippocampal-fissure map to reduced label 2 -------------- Making Left-Cerebral-Cortex map to reduced label 3 -------------- Making Left-Cerebral-White-Matter map to reduced label 4 -------------- Making Left-Lateral-Ventricle map to reduced label 5 -------------- Making Left-choroid-plexus map to reduced label 6 -------------- Making Unknown map to reduced label 7 -------------- Making Left-VentralDC map to reduced label 8 -------------- Making Left-Putamen map to reduced label 9 -------------- Making Left-Pallidum map to reduced label 10 -------------- Making Left-Thalamus-Proper map to reduced label 11 -------------- Making Left-Accumbens-area map to reduced label 12 -------------- Making Left-Caudate map to reduced label 13 Error using segmentSubjectT1_autoEstimateAlveusML (line 348) The vector of prior probabilities in the mesh nodes must always sum to one over all classes #-------------------------------------------- #@# Hippocampal Subfields processing (T1 only) right Mon Aug 10 18:54:37 EDT 2020 ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/usr/local/packages/freesurfer_v6.0.0/MCRv80/runtime/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/bin/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/os/glnxa64:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/native_threads:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/server:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64/client:/usr/local/packages/freesurfer_v6.0.0/MCRv80/sys/java/jre/glnxa64/jre/lib/amd64:/usr/lib64/qt5/plugins/platforms:/usr/local/packages/ants-2.3.4/lib:/usr/lib64/openmpi/lib: Warning: application is running on a locale different from the original platform locale. Warning: No display specified. You will not be able to display graphics on the screen. Registering imageDump.mgz to hippocampal mask from ASEG This file does not contain MRI parameters $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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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 '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) Type Source : 4 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 (38, 43, 57) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 4 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 43, 57) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 0.1781192630761 0 1.0000000000000 0 1.0539982530706 0 0 1.0000000000000 -3.1974090629449 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 21 30 ) T( 19 21 30 ) Iteration(f): 1 -- diff. to prev. transform: 13.2959 Iteration(f): 2 -- diff. to prev. transform: 9.20966 Iteration(f): 3 -- diff. to prev. transform: 6.22883 Iteration(f): 4 -- diff. to prev. transform: 8.12577 Iteration(f): 5 -- diff. to prev. transform: 6.11784 max it: 5 reached! Resolution: 0 S( 38 43 61 ) T( 38 43 61 ) Iteration(f): 1 -- diff. to prev. transform: 5.83495 Iteration(f): 2 -- diff. to prev. transform: 1.34307 Iteration(f): 3 -- diff. to prev. transform: 0.589867 Iteration(f): 4 -- diff. to prev. transform: 0.289573 Iteration(f): 5 -- diff. to prev. transform: 0.137676 max it: 5 reached! - final transform: Tf = [ ... 0.9980802661280 -0.0059694736544 0.0616453384301 -1.7131111872539 -0.0239991870640 0.8803011391816 0.4738079182283 -13.5066607583463 -0.0570948455303 -0.4743777711274 0.8784679327523 13.0909553444701 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 = [ ... 0.2495200665320 0.0154113346075 0.0014923684136 0.7292410424572 -0.0059997967660 0.1184519795571 -0.2200752847954 16.2081470043947 -0.0142737113826 0.2196169831881 0.1185944427819 -4.6804480513511 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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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. https://urldefense.com/v3/__http://dx.doi.org/10.1016/j.neuroimage.2010.07.…<https://urldefense.com/v3/__http:/dx.doi.org/10.1016/j.neuroimage.2010.07.0…> https://urldefense.com/v3/__http://reuter.mit.edu/papers/reuter-robreg10.pd…<https://urldefense.com/v3/__http:/reuter.mit.edu/papers/reuter-robreg10.pdf…> $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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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 '/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/…'. .. Registration::setSourceAndTarget(MRI s, MRI t, keeptype = TRUE ) Type Source : 4 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 (38, 43, 57) Asserting both images: 1mm isotropic - reslicing Mov ... -- changing data type from 4 to 3 (noscale = 0)... -- Original : (0.25, 0.25, 0.25)mm and (131, 99, 241) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - reslicing Dst ... -- Original : (1, 1, 1)mm and (38, 43, 57) voxels. -- Resampled: (1, 1, 1)mm and (38, 43, 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: 1 ) - Build Gaussian Pyramid ( Limits min steps: 0 max steps: 1 ) - initial transform: Ti = [ ... 1.0000000000000 0 0 0.1781175809123 0 1.0000000000000 0 1.0539986543476 0 0 1.0000000000000 -3.1974083471847 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 1 S( 19 21 30 ) T( 19 21 30 ) Iteration(f): 1 -- diff. to prev. transform: 9.84742 Iteration(f): 2 -- diff. to prev. transform: 7.72517 Iteration(f): 3 -- diff. to prev. transform: 8.94618 Iteration(f): 4 -- diff. to prev. transform: 7.30294 Iteration(f): 5 -- diff. to prev. transform: 5.69484 max it: 5 reached! Resolution: 0 S( 38 43 61 ) T( 38 43 61 ) Iteration(f): 1 -- diff. to prev. transform: 11.7013 Iteration(f): 2 -- diff. to prev. transform: 3.26787 Iteration(f): 3 -- diff. to prev. transform: 1.06278 Iteration(f): 4 -- diff. to prev. transform: 0.347277 Iteration(f): 5 -- diff. to prev. transform: 0.0826464 max it: 5 reached! - final transform: Tf = [ ... 1.1636664104160 -0.3014738824441 0.0004403516595 4.0520187720971 -0.0480158583329 1.0596278475093 0.4954259490783 -17.9010148715481 -0.2453688431946 -0.4779665918288 0.8802303084621 16.7816318160186 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 = [ ... 0.2909164812245 0.0001100842157 0.0753684565922 -3.0302464159984 -0.0120039592287 0.1238564808785 -0.2649069144859 17.7887142169889 -0.0613421845898 0.2200575304725 0.1194916267047 -1.6040021052477 0 0 0 1.0000000000000 ] Determinant : 0.0216399 Decompose into Rot * Shear * Scale : Rot = [ ... 0.9808313831675 0.1119800663090 0.1594686882846 0.0897438323260 0.4668219029177 -0.8797859714247 -0.1729619679387 0.8772330224665 0.4478240524369 ] Shear = [ ... 1.0000000000000 -0.1069794540990 0.0987385001868 -0.0910164402411 1.0000000000000 -0.0348388469911 0.0999837864599 -0.0414655431721 1.0000000000000 ] Scale = diag([ 0.2948725983257 0.2508729779031 0.2985915205121 ]) 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 /mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… 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. https://urldefense.com/v3/__http://dx.doi.org/10.1016/j.neuroimage.2010.07.…<https://urldefense.com/v3/__http:/dx.doi.org/10.1016/j.neuroimage.2010.07.0…> https://urldefense.com/v3/__http://reuter.mit.edu/papers/reuter-robreg10.pd…<https://urldefense.com/v3/__http:/reuter.mit.edu/papers/reuter-robreg10.pdf…> Reading contexts of file /usr/local/packages/freesurfer_v6.0.0/average/HippoSF/atlas/compressionLookupTable.txt Constructing image-to-world transform from header information (asegMod.mgz) Returning the following transform: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 422 Debug: Off Object Name: Observers: none Matrix: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Offset: [132.04, -111.801, 144.75] Center: [0, 0, 0] Translation: [132.04, -111.801, 144.75] Inverse: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Singular: 0 Constructing image-to-world transform from header information (/mnt/BIAC/.users/ch186/ duhsnas-pri.dhe.duke.edu/dusom_morey/Data/Lab/PGC/T1s/Duke_data/Freesurfer/… ) Returning the following transform: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 579 Debug: Off Object Name: Observers: none Matrix: -0.290916 -0.000110086 -0.0753684 -0.0613422 0.220058 0.119492 0.012004 -0.123856 0.264907 Offset: [49.0706, -22.4049, 0.96176] Center: [0, 0, 0] Translation: [49.0706, -22.4049, 0.96176] Inverse: -3.37777 -0.432721 -0.765819 -0.81721 3.51948 -1.82003 -0.229025 1.66513 2.95866 Singular: 0 Transform before cropping: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 581 Debug: Off Object Name: Observers: none Matrix: 0.290916 0.000110086 0.0753685 -0.012004 0.123856 -0.264907 -0.0613422 0.220058 0.119492 Offset: [82.9698, 143.789, 89.396] Center: [0, 0, 0] Translation: [82.9698, 143.789, 89.396] Inverse: 3.37777 0.765819 -0.432721 0.81721 1.82003 3.51948 0.229025 -2.95866 1.66513 Singular: 0 WorldToImageTransform before cropping: AffineTransform (0x2b82ec807bf0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 583 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [132.04, 144.75, 111.801] Center: [0, 0, 0] Translation: [132.04, 144.75, 111.801] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 m_BoundingBoxSize: [131, 241,99] minimalMappedCoordinate: [82.9698, 116.267,81.4215] maximalMappedCoordinate: [128.201, 173.514,153.92] Cropping with min [78 111 74] and max [133 179 161] m_OriginalImageRegion before checking margins: ImageRegion (0x2b82ec809218) Dimension: 3 Index: [78, 111, 74] Size: [56, 69, 88] m_CroppedImageRegion before checking margins: ImageRegion (0x2b82ec8091e0) Dimension: 3 Index: [0, 0, 0] Size: [56, 69, 88] m_OriginalImageRegion after checking margins: ImageRegion (0x2b82ec809218) Dimension: 3 Index: [78, 111, 74] Size: [56, 69, 88] m_CroppedImageRegion after checking margins: ImageRegion (0x2b82ec8091e0) Dimension: 3 Index: [0, 0, 0] Size: [56, 69, 88] Transform after cropping: AffineTransform (0x2b82ee48f820) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 740 Debug: Off Object Name: Observers: none Matrix: 0.290916 0.000110086 0.0753685 -0.012004 0.123856 -0.264907 -0.0613422 0.220058 0.119492 Offset: [4.96976, 32.7887, 15.396] Center: [0, 0, 0] Translation: [4.96976, 32.7887, 15.396] Inverse: 3.37777 0.765819 -0.432721 0.81721 1.82003 3.51948 0.229025 -2.95866 1.66513 Singular: 0 WorldToImageTransform after cropping: AffineTransform (0x2b82ec807bf0) RTTI typeinfo: itk::AffineTransform<double, 3u> Reference Count: 1 Modified Time: 741 Debug: Off Object Name: Observers: none Matrix: -1 1.3024e-09 1.86265e-09 -1.86265e-09 -1.16415e-10 -1 -3.49428e-09 1 -1.16415e-10 Offset: [54.0404, 33.7505, 37.8009] Center: [0, 0, 0] Translation: [54.0404, 33.7505, 37.8009] Inverse: -1 -1.86265e-09 1.3024e-09 -3.49428e-09 -1.16415e-10 1 1.86265e-09 -1 -1.16415e-10 Singular: 0 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 Warning: This concatenation operation includes an empty array with an incorrect number of rows. Concatenation including empty arrays will require all arrays to have the same number of rows in a future release. [ > In segmentSubjectT1_autoEstimateAlveusML at 325] Reading contexts of file /usr/local/packages/freesurfer_v6.0.0/average/HippoSF/atlas/compressionLookupTable.txt -------------- 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 alveus map to reduced label 2 Making Hippocampal_tail map to reduced label 2 Making HATA map to reduced label 2 Making fimbria map to reduced label 2 Making parasubiculum map to reduced label 2 Making hippocampal-fissure map to reduced label 2 -------------- Making Left-Cerebral-Cortex map to reduced label 3 -------------- Making Left-Cerebral-White-Matter map to reduced label 4 -------------- Making Left-Lateral-Ventricle map to reduced label 5 -------------- Making Left-choroid-plexus map to reduced label 6 -------------- Making Unknown map to reduced label 7 -------------- Making Left-VentralDC map to reduced label 8 -------------- Making Left-Putamen map to reduced label 9 -------------- Making Left-Pallidum map to reduced label 10 -------------- Making Left-Thalamus-Proper map to reduced label 11 -------------- Making Left-Accumbens-area map to reduced label 12 -------------- Making Left-Caudate map to reduced label 13 Error using segmentSubjectT1_autoEstimateAlveusML (line 348) The vector of prior probabilities in the mesh nodes must always sum to one over all classes -------------- next part -------------- An HTML attachment was scrubbed... URL: https://urldefense.com/v3/__http://mail.nmr.mgh.harvard.edu/pipermail/frees…<https://urldefense.com/v3/__http:/mail.nmr.mgh.harvard.edu/pipermail/freesu…> ------------------------------ _______________________________________________ Freesurfer mailing list Freesurfer(a)nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> https://urldefense.com/v3/__https://mail.nmr.mgh.harvard.edu/mailman/listin…<https://urldefense.com/v3/__https:/mail.nmr.mgh.harvard.edu/mailman/listinf…> End of Freesurfer Digest, Vol 199, Issue 17 *******************************************

2 1

Resting state fMRI - volume analysis
by Wenzhen Zhao 07 Oct '20

07 Oct '20

External Email - Use Caution Dear Freesurfer Developers, I have a question regarding volume analysis of fMRI data. I wanted to perform volume analysis in MNI305 space, and I used following command after creating a seed; $mkanalysis-sess -analysis lh.pcc.mni305.vol -mni305 2 -stc siemens -fwhm 0 -notask -taskreg lh.pcc.dat 1 -nuisreg vcsf.dat 5 -nuisreg wm.dat 5 -mcextreg -polyfit 5 -nskip 10 -fsd rest -hpf 0.01 -TR 3 -per-run $selxavg3-sess -s sess01 -a lh.pcc.mni305.vol -no-preproc -overwrite The resulting analysis seems only showing subcortical regions, not the whole brain. (Shown in the attachment) I read on the page that volume analysis is only for subcortical regions, is this the reason that I only see subcortical regions? Another problem is that in the preprocessing step, I could not smooth the volume. If I do, it seems to distort the image and only gives something similar to the picture shown. Could this be the reason for the distorted analysis? Thanks for your help. Best, Wenzhen

3 5

Recon-all Results for fMRI
by goktekin.durusoy＠boun.edu.tr 07 Oct '20

07 Oct '20

External Email - Use Caution Hi, I am using freesurfer 5.3.0. I would like to ask whether it is possible or not to have different results for destrieux atlas parcellation for the same data. (aparc.a2009s+aseg.mgz file). It seems there is a little difference in terms of parcellation locations and their volumes. Thank you, Goktekin Durusoy

2 3

create .nv or .pial file with atlas based values
by Xin Niu 07 Oct '20

07 Oct '20

External Email - Use Caution Dear freesurfer experts, We have some files containing the values for regions based on the Deskian/Killiany atlas like : ROI, value 'lh_bankssts', .9 'lh_caudalanteriorcingulate', .1 'lh_caudalmiddlefrontal', .5 'lh_cuneus', .2 'lh_entorhinal', .5 'lh_fusiform', .9 'lh_inferiorparietal', .7 And we have the information on the labels in Matlab data format: [image: image.png] I wish to create a surface file (.nv or .pial) based on the atlas and values which can be visualized directly in software like BrainNetViewer. We are also considering to visualized the brain images with SurfStat which needs a .obj file and .txt file: s100 = SurfStatReadSurf( {... 'c:/keith/fMRI/ICBM/surfaces/mni_icbm_00100_mid_surface_left_81920.obj',... 'c:/keith/fMRI/ICBM/surfaces/mni_icbm_00100_mid_surface_right_81920.obj'} ); t100 = SurfStatReadData( {... 'c:/keith/fMRI/ICBM/thickness/mni_icbm_00100_native_rms_rsl_tlink_20mm_left.txt',... 'c:/keith/fMRI/ICBM/thickness/mni_icbm_00100_native_rms_rsl_tlink_20mm_right.txt'} ); SurfStatView( t100, s100, 'Cort Thick (mm), Subj 00100' ); But I have no idea how the .obj file is created and how the .txt file should be organized. Is there any way to make it? Thanks in advance! Best, Xin Xin Niu Drexel University, Department of Psychology Stratton Hall 314 3201 Chestnut Street Philadelphia, PA 19104

2 1

recon-all -s <subject> -autorecon2-cp -autorecon3 error
by Kazuki Sakakura 07 Oct '20

07 Oct '20

External Email - Use Caution Hello FreeSurfer Developers, I'm attempting to compute recon-all after putting control point, but for some of my subjects I get the attached error when I run the recon-all -s <subject> -autorecon2-cp -autorecon3 command. I've searched the list and no similar errors have been reported. Does anyone have any thoughts on how to trouble-shoot this one? Also, Ive attached the recon-all.log in case it's of any use. 1) FreeSurfer version: freesurfer-Linux-centos6_x86_64-stable-pub-v5.3.0 2) Platform: Ubuuntu 14.04 3) recon-all.log: see attached Best Regards, Kazuki Sakakura

2 1

qdec problems not receiving keyboard input
by Agurne Sampedro Calvete 07 Oct '20

07 Oct '20

External Email - Use Caution Dear Freesurfer Experts, Although I haven't had any problems with the qdec until now, this week the qdec is having some errors when receiving inputs from the keyboard. For example, I can't write in the design name box, nor can I change the minimum or maximum threshold in the display page. I've seen that other people have had a similar problem too. How can I solve it? Thanks in advance. Kind regards, Agurne Sampedro

2 1

Turn off bias field correction for SAMSEG
by Donatas Sederevicius 06 Oct '20

06 Oct '20

External Email - Use Caution Dear FreeSurfer team, Is there a way to turn off the bias field estimation and correction while running SAMSEG? I have images that are bias field corrected and would be great to skip this extra bias field estimation. Thanks in advance. Best, Donatas

2 1

Lobe segmentation
by Platten, Michael 06 Oct '20

06 Oct '20

Hi, I have a question regarding lobe (temporal, frontal, parietal, and occipital) segmentation. I have already completed a recon-all for all subjects. I have seen that the surface stream has a -lobesStrict function which parcellates the lobes. I have also seen that you can convert the surface to a volume through mri_surf2vol. This, I believe, would in the end get me what I'm after; which are the lobe segmentations. My plan is to overlay a lesion mask and extract the lesions based on, among other things, which lobe they belong to. I was wondering if there was a way to use the segmentation outputs from FreeSurfer to get lobe segmentation directly, i.e. by adding up various segmentation volumes? Or do I have to go through the Surface stream? Is my above-stated method the easiest and best way to get lobe segmentations in order to extract the lesion load per lobe? Thank you for your time and help! Best, Michael

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Parallel Run of Multiple Patients
by gunes 06 Oct '20

06 Oct '20

External Email - Use Caution Dear Freesurfer experts, We have a pipeline script that uses *recon-all* and other executables (like applywarp, mri_convert, mri_em_register etc.) several times. We ran our script in parallel from different terminals to process the patients faster. However, it's come to our attention that the directory *$FREESURFER_HOME/subjects* is being used by *recon-all* and other scripts in common. So, if we run our pipeline script in parallel, they'll call *recon-all* and other freesurfer scripts in parallel, possibly writing to and reading from this directory, eventually leading to a mix up in the patient data. So, we feel that we need to run our script one by one for the patients. Am I right to be concerned about this? Kind Regards, Gunes

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Freesurfer October 2020