Freesurfer

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30132 discussions

fMRI mask for hippocampal subfields
by Buckle, Chanelle [cbuckle＠sun.ac.za] 15 Oct '18

15 Oct '18

External Email - Use Caution Good day I have fMRI data and would like to look at BOLD activation in the hippocampal subfields (particularly the subiculum). I was wondering if there is currently an AAL mask available for the hippocampal subfields or if there is some way to create it using the WFU Pickatlas Tool? Kind regards, Chanelle [http://cdn.sun.ac.za/100/ProductionFooter.jpg]<http://www.sun.ac.za/english/about-us/strategic-documents> The integrity and confidentiality of this email are governed by these terms. Disclaimer<http://www.sun.ac.za/emaildisclaimer> Die integriteit en vertroulikheid van hierdie e-pos word deur die volgende bepalings bereël. Vrywaringsklousule<http://www.sun.ac.za/emaildisclaimer>

2 1

using mris_ca_train
by Maedeh Khalilian 13 Oct '18

13 Oct '18

External Email - Use Caution Dear Freesurfer experts, I have a question, does the number of input subjects matter in using "mris_ca_train" or not? i mean does it affect the results?(maybe the more subjects, the better result) and if yes, what is an approximate number? and the second question is that if there is a way so i can check my .gcs file? for example for reading annotation files u had an m file named "read_annotation.m".it did help me alot. Generally how can i check my .gcs file and evaluate its quality? just by applying it on a subject by "mris_ca_label" ? i really appreciate you if you can help me, Kind regards, Maedeh,

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FreeSurfer Errors
by Sparsh Jain 12 Oct '18

12 Oct '18

External Email - Use Caution Hey! I have recently started using FreeSurfer. I have encountered multiple errors over the past few days, and I would like to mention them all. 1. While testing the installation, the code ran for roughly 4-6 hours and generated the T1 and wm output images. The brainmask image failed to load even after multiple attempts. 2. We have patient data from a Seimens 1.5 T MRI machine. They are DICOM images (about 120-145 images). Running dcmunpack on the directory to locate the suitable T1 weighted image (to pass as the input image to freesurfer) returns an error message 'Element not found'. It fails to generate a list of usable input images. The log file also only mentions the error message. Basically, I am having trouble working with DICOM images using Freesurfer. Please advise me on how to go about it. 3. I tried using the dcm2nii tool on the DICOM images. The software gave out three (nearly identical) nii.gz files as output, each about 7 MB in size. I tried running reconstruction on all three one by one. Only the 'compressed' version output underwent reconstruction for a long time without giving any errors. However, the reconstruction process went on for 150 hours without completion. There were no errors during this time, and the mris_fix_topo process went on for about 74 hours until tonight. It was working on 'correcting defect 48' for most of this time, and then the PC suddenly stopped responding. The process had been utilizing 100% of the CPU and 87% of the RAM for a long time, but the computer was always responding. However it suddenly stopped responding after about 74 hours of mris_fix_topology process. My machine has 8 GB RAM and more than 3.4 GHz processing speed (Acer Veriton). 4. Previously, I tried running reconstruction on a different subject's data after converting it to nifti using dcm2niix. After 70 hours of execution, it terminated with an error which showed that the fsaverage folder was inaccessible. Further digging revealed that the folder was somehow pointing to itself. The original error said failed to read from the label file. I had to eventually reinstall FreeSurfer. 5. I tried running iElvis pipeline. After some time I got an error which said that the sphere.reg file couldn't be opened. I tried fixing this but nothing worked. I kept getting the error that lh.sphere.reg file could not be opened and so on. Please help. I have been trying to run it for weeks now. I hope you will be able to address each of these problems soon. Thank You Sparsh

2 3

mri_watershed Error
by Boris Rauchmann 12 Oct '18

12 Oct '18

External Email - Use Caution When I run recon-all i get the follwing error message: mri_watershed Error: GLOBAL region of the brain empty ! Linux 02f92500bcfb 4.15.0-36-generic #39~16.04.1-Ubuntu SMP Tue Sep 25 08:59:23 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux recon-all -s sub-XYZ exited with ERRORS at Thu Oct 11 15:10:22 UTC 2018 The .nii file looks fine. Can someone help me with this? Best, Boris

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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.

4 4

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/

1 0

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

1 0

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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