Freesurfer October 2024

freesurfer@nmr.mgh.harvard.edu

44 participants
51 discussions

Re: [Freesurfer] Question on using thalseg_1.1.h5
by Georgios Argyropoulos 23 May '25

23 May '25

External Email - Use Caution Many thanks and apologies, Henry - I figured it out... I stupidly replaced your improved segmentation script in both the scripts and the bin directory instead of only replacing the former. many thanks for your time, much appreciated g

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mri_glmfit error：ill-conditioned matrix
by Chen Yang 22 Nov '24

22 Nov '24

External Email - Use Caution Dear FreeSurfer experts, I tried to examine the thickness differences between two groups after controlling for the age, gender, and mean thickness, while I got an error message below: ERROR: matrix is ill-conditioned or badly scaled, condno = 1e+08 Possible problem with experimental design: Check for duplicate entries and/or lack of range of continuous variables within a class. If you seek help with this problem, make sure to send: Your command line: mri_glmfit.bin --y glm/FB_demo_lh_CT/lh.FB_demo_lh_CT.thickness.10B.mgh --fsgd glm/FSGD_files/createFSGD/FB_demo_lh_CT/FB_demo_lh_CT.fsgd --C glm/FB_demo_lh_CT/PS-NP.mtx --C glm/FB_demo_lh_CT/NP-PS.mtx --surf 43kids lh --glmdir glm/FB_demo_lh_CT/lh.FB_demo_lh_CT.thickness.10B.glmdir The FSGD file (if using one) And the design matrix above Attempting to diagnose further SumSq: Min=1.258825 (col 10), Max=5.384785 (col 5) ... could not determine the cause of the problem I have searched this problem in the mail archive. I have done the normalization for all covariables (age and mean thickness). Sex and test condition are treated as class variables. So I have no idea about the cause of the error. Here I attached the FSGD file and the matrix. The contrast is 0.5, -0.5, 0.5, -0.5, 0,0,0,0,0,0,0,0. Can someone help me? Thank you! Best regards, Chen -- Chen Yang PhD Student Independent Research Group 'Milestones of Early Childhood Development' Max Planck Institute for Human Cognitive and Brain Sciences Email:yangc@cbs.mpg.de

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Resampling FLAIR images with anisotropic voxels
by Jin Liu 12 Nov '24

12 Nov '24

External Email - Use Caution Hello Freesurfer team, I tried to use SAMSEG-long to deal with my longitudinal data with paired MPRAGE and FLAIR images. But when I checked the original FLAIR images I have, I found that they have a voxel resolution of 1.333333 x 1.333333 x 2.000000. When I view it on Freeview, although the axial view is fine, only a rectangular portion of the sagittal and coronal brain is available, with the top and the bottom cut off. I guess it’s probably due to resampling issue that assumes isotropic voxels which we do not have. Is there any way to fix this problem? Best, Jin

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recon all data for classification between groups !
by JM 11 Nov '24

11 Nov '24

External Email - Use Caution Dear FS experts, I have longitudinal data: Three time-points [T1-T3] with an interval of about 6 months between T1 and T2 and 6 months between T2 and T3 from a control group as well as from a clinical group (again three time-points [T1-T3] with an interval of about 6 months between T1 and T2 and 6 months between T2 and T3). Sample sizes are: - For control group: 48 (for T1), 36 (for T2) and 33 (for T3) - For clinical group: 38 (for T1), 33 (for T2) and 31 (for T3) I was wondering if it's fine to consider and combine three time-points within each group as independent samples for classification purposes (i.e., N = 117 (48+36+33) for the control group and N = 102 (38+33+31) for clinical group) between the two groups (based on brain morphometry measures). If so, I was wondering whether the recon-all should be done in the longitudinal way ( https://secure-web.cisco.com/17EJvCDRair_uWYfmRy5gjgpT3fQihw2Qeo96vmyp96h9n…) i.e., making it unbiased towards a specific time-point, OR, the recon all should be run cross-sectionally so that we can keep all the time-points independent from each other. Any detailed explanation/justification would be really helpful. Also, I would greatly appreciate the response at earliest convenience. Thanks, M

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Re: [Freesurfer] PVC on DCE MRI
by Shen, Qian 07 Nov '24

07 Nov '24

External Email - Use Caution Hi FreeSurfer Team, We are looking to apply voxelwise PVC on our dynamic contrast-enhanced (DCE) MRI data using methods implemented in PETSurfer. Our DCE MRI data voxelsize =1x1x5mm, and we have used the Patlak model to estimate the transfer coefficient (Ktrans). We are interested in both GM and WM signals. I have a few specific questions regarding the applicability and execution of PVC in our case: 1. Given that the M?ller-G?rtner (MG) method assumes constant WM signals and zero CSF signals (which is more suited to PET studies), it seems this may not be optimal for us. Would the RBV or Meltzer (MZ) methods be more appropriate for our DCE MRI data? 2. Should PVC be applied to the 4D dynamic DCE data before dynamic modeling, or directly to the 3D Ktrans map after modeling? 3. Does the point spread function (PSF) of the MRI scanner cause more PVE than the TFE? Additionally, I have one more question about the outputs of the MG PVC method. Could you clarify the role of mgx.gm in the results? Does it the combination of mgx.ctxgm and mgx.subctxgm? Any help is appreciated. Many thanks! Shen

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mri_glmfit-sim Error for Custom Threshold
by Sujeevini Sujanthan 07 Nov '24

07 Nov '24

External Email - Use Caution Dear Freesurfer Experts, I'm encountering an error when attempting to run mri_glmfit-sim with a custom threshold of 1.6. I've already generated the necessary tables using mri_mcsim for this threshold. ############## mri_glmfit-sim --glmdir glm_wls --mczsim-label {ROI} --mczsim 1.6 abs --cwp 0.05 ERROR: thresh = 1.6, must be 1.3, 2.0, 2.3, 3.0, 3.3, 4.0 ############## Could you please advise on what I can do to fix this? Thank you for your time, Sujeevini

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glmfit-sim question
by 이종훈(보건과학대학 의공학부) 07 Nov '24

07 Nov '24

External Email - Use Caution Dear Freesurfer expert, I hope this email finds you well. I am currently working on a project involving FreeSurfer's cluster-wise correction methods, and I encountered an unexpected result when adjusting the Cluster-Forming Threshold (CFT) and Cluster-Wise Probability (CWP) values. I would greatly appreciate your expertise on this matter. In my analysis, I observed that some regions were not detected when using a CFT of 1.3 and a CWP of 0.05. However, these regions emerged as significant when I increased the CFT to 2.0 while keeping the CWP constant at 0.05. This finding appears counterintuitive, as a higher CFT typically results in fewer clusters being detected due to the increased threshold for significance. Could you please help clarify why this phenomenon might occur? Specifically: 1. How might the higher CFT (2.0) interact with the CWP (0.05) to reveal regions that did not appear with a lower CFT (1.3)? 2. Is it possible that the increase in CFT reduces noise or increases signal clarity in such a way that previously undetected clusters reach significance? Thank you very much for your time and guidance. I look forward to any insights you can provide on this topic. Jonghun Lee Master's Student, Advanced Image Processing Technology Lab Yonsei University

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Re: [Freesurfer] recon-all-clinical output brainvol.stats vs synthseg.vol.csv
by Melissa Marie Rundle 30 Oct '24

30 Oct '24

External Email - Use Caution Hello, Recon-all-clinical outputs several files, including brainvol.stats and synthseg.vol.csv, that have volumetric data that don’t match each other. See example data below. Are the stats for brainvol (as well as lh.aprac.stats/rh.aprac.stats) generated from the synthseg processing? And if not, how is the data generated? Thanks so much for your help, Melissa Rundle In brainvol.stats: Left hemisphere cerebral white matter volume=225511.42 mm3 Left hemisphere cortical gray matter volume=194309.18 mm3 In synthseg.vol.csv: left cerebral white matter volume=224286.48 mm3 left cerebral cortex volume=252060.19 mm3 ________________________________ This electronic message is intended only for the use of the individual(s) and entity named as recipients in the message. If you are not an intended recipient of this message, please notify the sender immediately and delete the material from any computer. Do not deliver, distribute or copy this message, and do not disclose its contents or take any action in reliance on the information it contains. Thank you.

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Error During T2/FLAIR Pial Surface Refinement
by Thomas Kaufmann 29 Oct '24

29 Oct '24

External Email - Use Caution Hello FreeSurfer Developers, I'm attempting to refine pial surfaces with T2/FLAIR images as described here ( https://secure-web.cisco.com/1AH1BRBqXr5DfxhMfqvYMi7G1gSsW58-k5PUb3k7qBej25…) For some of my subjects I get the following error during the mris_place_surface subprocess when I run recon-all: error: MRIhistogramLabelRegion: constant image error: MRIhistogramLabelRegion: constant image Segmentation fault The error occurs independent of whether I process T1 and T2 images together with -all or if I run only -autorecon3 with the T2 or FLAIR images added. I've searched the list and while similar errors have been reported, no solutions have been proposed. Does anyone have any thoughts on how to troubleshoot this one? 1) FreeSurfer version: freesurfer-macOS-darwin_x86_64-7.4.1-20230614-7eb8460 2) Platform: macOS Sonoma 14.6.1 3) uname -a: Darwin MacBook-Air-M3 23.6.0 Darwin Kernel Version 23.6.0: Mon Jul 29 21:14:04 PDT 2024; root:xnu-10063.141.2~1/RELEASE_ARM64_T8122 arm64 4) recon-all.log: relevant section below Thank you! Thomas mris_place_surface --adgws-in ../surf/autodet.gw.stats.lh.dat --seg aseg.presurf.mgz --wm wm.mgz --threads 8 --invol brain.finalsurfs.mgz --lh --i ../surf/lh.pial.T1 --o ../surf/lh.pial.T2 --pial --nsmooth 0 --rip-label ../label/lh.cortex+hipamyg.label --pin-medial-wall ../label/lh.cortex.label --white-surf ../surf/lh.white --aparc ../label/lh.aparc.annot --repulse-surf ../surf/lh.white --mmvol T2.mgz T2 Reading in input surface ../surf/lh.pial.T1 Not smoothing input surface Area 280714 0.42434 0.32508 0.000178 6.1090 Corner 842142 60.00000 30.99319 0.049953 179.7466 Edge 421071 1.04711 0.48130 0.006015 7.4374 Hinge 421071 18.87838 25.11022 0.000011 179.9979 Reading white surface coordinates from ../surf/lh.white Reading repulsion surface coordinates from ../surf/lh.white Reading in aparc ../label/lh.aparc.annot [DEBUG] CTABreadFromBinaryV2(): ct->nentries=36, num_entries_to_read=36 Reading in input volume brain.finalsurfs.mgz Reading in seg volume aseg.presurf.mgz Reading in wm volume wm.mgz MRIclipBrightWM(): nthresh=15378, wmmin=5, clip=110 MRIfindBrightNonWM(): 1814 bright non-wm voxels segmented. Masking bright non-wm for pial surface mid_gray = 67.5589 MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 MRImask(): AllowDiffGeom = 1 Ripping frozen voxels Ripping vertices not in label ../label/lh.cortex+hipamyg.label MRISripNotLabel() ripped 6543/140359 vertices (133816 unripped) INFO: rip surface needed but not specified, so using input surface Ripping segs (eg, WMSA, BG, frozen) Starting MRISripSegs() d = (-2 2 0.5) segnos: 247 MRISripSegs(): -2 2 0.5 ripped 0 Reading in multimodal volume T2.mgz using multi modal weights vertex 70180: xyz = (-39.3637,-26.8713,28.9107) oxyz = (-42.1603,-27.2597,31.4024) wxzy = (-42.1603,-27.2597,31.4024) pxyz = (-39.3637,-26.8713,28.9107) CBVO Creating mask 140359 n_averages 4 Iteration 0 ========================================= n_averages=4, current_sigma=2 Computing pial target locations using multimodal (1) starting MRIScomputePialTargetLocationsMultiModal() max_outward_dist = 3, sample_dist = 0.1, pix_size = 1, whalf = 7 T2_min_inside = 110, T2_max_inside 300, T2_min_outside = 130, T2_max_outside 300 inside_peak_pct = 0.1, 0.01, outside_peak_pct = 0.5, 0.5 wm_weight = 3, nlabels=0, contrast_type=1 Changed 198177 aseg cortex voxels to 0 Creating lowres distance volumes t=0.0164299 Creating white distance volumes t=0.307498 (box.dx, box.dy, box.dz) = (132, 242, 326) (region->dx, region->dy, region->dz) = (132, 242, 326) (region->dx, region->dy, region->dz) = (132, 242, 326) Creating pial distance volumes t=0.609579 (box.dx, box.dy, box.dz) = (144, 256, 336) (region->dx, region->dy, region->dz) = (144, 256, 336) (region->dx, region->dy, region->dz) = (144, 256, 336) locating cortical regions not in interior range [110.0 --> 300.0], and not in exterior range [130.0 --> 300.0] t = 0.984043 Starting loop over 140359 vertices vno = 0, t = 0.984043 error: MRIhistogramLabelRegion: constant image error: MRIhistogramLabelRegion: constant image Segmentation fault

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Edit control points for better tract reconstruction
by Mariana Falcao 29 Oct '24

29 Oct '24

External Email - Use Caution Dear TRACULA experts, As you can see in the figure attached to this email, there are 3 tracts that were not able to be properly reconstructed (right fornix and left and right corticospinal tract). What I have tried: 1) Setting reinit=1 in the configuration file and re-run TRACULA; 2) Changing the number of control points for each tract, according to the pathlist.txt (even though the second column refers to the default control points, I am not sure what the first column means); My idea now would be to edit the control points manually but I don't know which file contains those points. I am assuming that each tract must have a file with the respective control points. This process is described in this article <https://secure-web.cisco.com/1Syob_RQa8CnqTu5J98-5wBZC9rpcTVD0Wgicm76PjaNZv…>. I am looking for something such as Figure 2 and 3 in that article. I have looked into the email archive and didn't find anything related to this problem. Best wishes, Mariana

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Freesurfer October 2024