Freesurfer

freesurfer@nmr.mgh.harvard.edu

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Notice on freesurfer list archives
by Hoopes, Andrew 15 Oct '19

15 Oct '19

Hi all, Our primary archive for all posts to the mailing list can be found at: https://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu And we've now disabled the public 'pipermail' archives, which include anything under: https://mail.nmr.mgh.harvard.edu/pipermail/freesurfer If you come across any broken links pointing to the above, please let us know. best Andrew

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Re: [Freesurfer] ERROR gtmseg
by Marina Fernández 15 Oct '19

15 Oct '19

External Email - Use Caution Dear Douglas, I put the subjects yesterday. They are: qsm_04_yr3 (it is the first subject with problems and we have another copy that is solved) and mci13_hsp (it is the new subject with problems). Thank you very much for your attention. Marina.

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Re: [Freesurfer] Freesurfer Digest, Vol 188, Issue 21
by Juan Rivas 14 Oct '19

14 Oct '19

External Email - Use Caution Dear Juan Eugenio, thanks for your answer. I tried to do with the corrected images and wit the raw ones (before the correction), and I got the same error. Then I tried with two patients more and happened the same. I don´t know if it is due to the dev version we used, because the first time it worked very well. Best regards, JC. El dom., 13 oct. 2019 a las 12:01, <freesurfer-request(a)nmr.mgh.harvard.edu> escribió: > Send Freesurfer mailing list submissions to > freesurfer(a)nmr.mgh.harvard.edu > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > or, via email, send a message with subject or body 'help' to > freesurfer-request(a)nmr.mgh.harvard.edu > > You can reach the person managing the list at > freesurfer-owner(a)nmr.mgh.harvard.edu > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Freesurfer digest..." > > > Today's Topics: > > 1. HippoAmyg (Juan Rivas) > 2. Learning dti processing tutorial (Renew Andrade) > 3. Re: HippoAmyg (Iglesias Gonzalez, Juan E.) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Sat, 12 Oct 2019 16:02:28 -0400 > From: Juan Rivas <jcrn12(a)gmail.com> > Subject: [Freesurfer] HippoAmyg > To: freesurfer(a)nmr.mgh.harvard.edu > Message-ID: > <CACYE61CZSKt0p-q= > k+Yph1dDvpW4O67qehmXfhVpem7gqobdUg(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > External Email - Use Caution > > *Hi, I runned the reconall of my images with FS60 with this command:* > > *shiraz[0]:NIFTI$ recon-all -i > > /autofs/cluster/neuromod/rivas/imagenes/NIFTI/sub-esq-02-en/anat/sub-esq-02-en_T1w.nii.gz > -s /autofs/cluster/neuromod/rivas/subject-esq-02-en. There were no errors.* > > *Then I runned recon for hippocampus and amygdala with fsdev on Thu Aug 22 > 15:36:32 , with this command:* > > *segmentHA_T1.sh* > > *There were no errors. Then I identified and corrected manually the errors > on the FS60 images.* > > *Then I run recon-all on fs60 without to touch hippo-amyg.* > > *Now, I am trying to make the hippo-amyg correction with this command:* > > *segmentHA_T1.sh on fsdev, and I got this error:* > > > > [shiraz:FS] (nmr-dev-env) segmentHA_T1.sh test1 > > #-------------------------------------------- > > #@# Hippocampal Subfields processing (T1) left Fri Oct 11 17:20:27 EDT 2019 > > /usr/bin/time -o /dev/stdout > > @#@FSTIME 2019:10:11:17:20:27 run_segmentSubjectT1_autoEstimateAlveusML.sh > N 13 e %e S %S U %U P %P M %M F %F R %R W %W c %c w %w I %I O %O L 1.23 > 1.35 1.67 > > run_segmentSubjectT1_autoEstimateAlveusML.sh > /usr/local/freesurfer/dev/MCRv84/ test1 /cluster/neuromod/rivas/imagenes/FS > 0.333333333333333333333333333333333333 > /usr/local/freesurfer/dev/average/HippoSF/atlas/AtlasMesh.gz > /usr/local/freesurfer/dev/average/HippoSF/atlas/AtlasDump.mgz > /usr/local/freesurfer/dev/average/HippoSF/atlas/compressionLookupTable.txt > 0.05 left L-BFGS v21 /usr/local/freesurfer/dev/bin/ 0 > > ------------------------------------------ > > Setting up environment variables > > --- > > LD_LIBRARY_PATH is > > .:/lib64:/usr/local/freesurfer/dev/MCRv84//runtime/glnxa64:/usr/local/freesurfer/dev/MCRv84//bin/glnxa64:/usr/local/freesurfer/dev/MCRv84//sys/os/glnxa64:/native_threads:/server:/client:: > > 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 > > /cluster/neuromod/rivas/imagenes/FS/test1/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 > > '/cluster/neuromod/rivas/imagenes/FS/test1/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 ) > > 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 (37, 33, 61) > > 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 (37, 33, 61) voxels. > > -- Reslicing using cubic bspline > > MRItoBSpline degree 3 > > - no Dst reslice necessary > > > > > > 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 -0.9335110261151 > > 0 1.0000000000000 0 -0.6030053897425 > > 0 0 1.0000000000000 -1.9033167008449 > > 0 0 0 1.0000000000000 ] > > > > - initial iscale: Ii =1 > > > > Resolution: 0 S( 37 33 61 ) T( 37 33 61 ) > > Iteration(f): 1 > > -- diff. to prev. transform: 17.9258 > > Iteration(f): 2 > > -- diff. to prev. transform: 13.3727 > > Iteration(f): 3 > > -- diff. to prev. transform: 12.6349 > > Iteration(f): 4 > > -- diff. to prev. transform: 0.963353 > > Iteration(f): 5 > > -- diff. to prev. transform: 0.23376 max it: 5 reached! > > > > - final transform: > > Tf = [ ... > > 0.9994502743718 -0.0309610775894 -0.0118558311665 0.1083605389283 > > 0.0330356012422 0.9601637341023 0.2774783825190 -11.0026122646332 > > 0.0027925093931 -0.2777175100517 0.9606587253036 3.8161835807274 > > 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 = [ ... > > -0.2498625685929 -0.0029639577916 0.0077402693973 33.8236195063683 > > -0.0082589003105 0.0693695956298 -0.2400409335256 17.7299867104994 > > -0.0006981273483 0.2401646813259 0.0694293775129 -3.6765424847757 > > 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 > > /cluster/neuromod/rivas/imagenes/FS/test1/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 > > > > $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 > > /cluster/neuromod/rivas/imagenes/FS/test1/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: Enabling affine transform! > > --sat: Using saturation 50 in M-estimator! > > > > reading source 'imageDump.mgz'... > > reading target > > '/cluster/neuromod/rivas/imagenes/FS/test1/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 ) > > 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 (37, 33, 61) > > 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 (37, 33, 61) voxels. > > -- Reslicing using cubic bspline > > MRItoBSpline degree 3 > > - no Dst reslice necessary > > > > > > 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 -0.9335121201217 > > 0 1.0000000000000 0 -0.6030049400697 > > 0 0 1.0000000000000 -1.9033196349668 > > 0 0 0 1.0000000000000 ] > > > > - initial iscale: Ii =1 > > > > Resolution: 0 S( 37 33 61 ) T( 37 33 61 ) > > Iteration(f): 1 > > -- diff. to prev. transform: 29.7552 > > Iteration(f): 2 > > -- diff. to prev. transform: 13.9258 > > Iteration(f): 3 > > -- diff. to prev. transform: 12.8176 > > Iteration(f): 4 > > -- diff. to prev. transform: 4.31284 > > Iteration(f): 5 > > -- diff. to prev. transform: 1.08934 max it: 5 reached! > > > > - final transform: > > Tf = [ ... > > 1.1485282870250 0.1923732018210 0.0456719546813 -8.8430815689836 > > 0.0248585691740 1.1974718877709 0.2901612074595 -15.4946754855698 > > 0.0132871026014 0.0262311630292 0.9721734242629 -1.7628900186542 > > 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 = [ ... > > -0.2871321056267 0.0114179894966 -0.0480933061884 36.4485194686672 > > -0.0062146408039 0.0725403105008 -0.2993680076302 19.7524929053736 > > -0.0033217759975 0.2430433850326 -0.0065577915391 -0.1873902167255 > > 0 0 0 1.0000000000000 ] > > > > Determinant : -0.020683 > > > > Decompose into Rot * Shear * Scale : > > > > Rot = [ ... > > -0.9973893744923 0.0079822957206 -0.0717685070543 > > 0.0722067536928 0.1210866300984 -0.9900122285773 > > -0.0007876362909 0.9926098483122 0.1213468939142 ] > > > > Shear = [ ... > > 1.0000000000000 -0.0253544642652 0.0881389503515 > > -0.0221787471386 1.0000000000000 -0.1442736100723 > > 0.0921761860222 -0.1724865310828 1.0000000000000 ] > > > > Scale = diag([ 0.2859363885412 0.2501220610640 0.2990338055489 ]) > > > > > > 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 > > /cluster/neuromod/rivas/imagenes/FS/test1/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 > > > > Reading contexts of file > /usr/local/freesurfer/dev/average/HippoSF/atlas/compressionLookupTable.txt > > Constructing image-to-world transform from header information > (asegModCHA.mgz) > > Constructing image-to-world transform from header information > > (/cluster/neuromod/rivas/imagenes/FS/test1/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/dev/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-Accumbens-area map to reduced label 9 > > -------------- > > Making Left-Caudate map to reduced label 10 > > Error using segmentSubjectT1_autoEstimateAlveusML (line 365) > > The vector of prior probabilities in the mesh nodes must always sum to one > over all classes. > > > > > How may I correct it? > > Thanks lot, > > JC. >

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help
by Asim Hassan 14 Oct '19

14 Oct '19

External Email - Use Caution Good afternoon, I would like to know is there a way to run recon-all on subjects in which manual edits have been done on the brainmask.mgz files? Or at least incorporate manually edited brainmask files into the recon-all process? Thanks! Asim Hassan Research Technician II Division of Pediatric Neurology University of Texas Southwestern Medical Center ________________________________ UT Southwestern Medical Center The future of medicine, today.

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BIDS Governance and Steering Group election closing tomorrow!
by Franklin Feingold 14 Oct '19

14 Oct '19

External Email - Use Caution Time is running out to vote on our BIDS Governance document (https://docs.google.com/document/d/1R-J2lL9V_wIkYhye4zH-feyl4P4J8NyO40rIYyY…) and for the inaugural Steering Group (https://docs.google.com/document/d/1yb6j3a90serWbtab7ukSMyecs16sGfYTSGftH-4…) Voting closes tomorrow October 15, 2019, at 11:59pm Pacific Daylight Time (UTC -7). Votes will be recorded using a Google form (https://forms.gle/YT5SvPgACHhVvf6x8) and a separate form for email validation (https://docs.google.com/forms/d/1zr-w3M2K7ApaW3yePXNRc1zYMhIiDsOVnG15nJ1sgx…) to make sure the vote is confirmed. We created separate forms to dissociate contact information from voting information, and no attempt will be made to re-associate these data. Contact information will be used to ensure that the number of votes matches the number of voters. All email addresses will be deleted immediately once the election results have been verified, with the exception of voters who opt to have their email address retained. If you have any questions regarding this process or BIDS please feel free to email Franklin ([ffein@stanford.edu](mailto:ffein@stanford.edu)) Thank you for taking the time to vote! Franklin

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Learning dti processing tutorial
by Renew Andrade 13 Oct '19

13 Oct '19

External Email - Use Caution Dear freesurfer experts: I am trying to learn dti processing. For using Tracula it appears there is a need for running "recon-all -all -i -s” on every subject before starting to run Tracula. But there seems to be a problem. I have a Parkinson dwi images database and every subject gives me an error of the type “input(s) cannot have multiple frames!”. I am a little bit stuck on this step. Is it a problem of the file as input? Am I putting the wrong file? Can it be done a recon-all analysis to a dwi image? With trackvis I can obtain the results without any problem. My doubt comes from FreeSurfer only and may be my little knowledge about it. Thanks for your help! Sincerely, Andrade.

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Re: [Freesurfer] Freesurfer Digest, Vol 188, Issue 21
by Juan Rivas 13 Oct '19

13 Oct '19

1 0

HippoAmyg
by Juan Rivas 13 Oct '19

13 Oct '19

External Email - Use Caution *Hi, I runned the reconall of my images with FS60 with this command:* *shiraz[0]:NIFTI$ recon-all -i /autofs/cluster/neuromod/rivas/imagenes/NIFTI/sub-esq-02-en/anat/sub-esq-02-en_T1w.nii.gz -s /autofs/cluster/neuromod/rivas/subject-esq-02-en. There were no errors.* *Then I runned recon for hippocampus and amygdala with fsdev on Thu Aug 22 15:36:32 , with this command:* *segmentHA_T1.sh* *There were no errors. Then I identified and corrected manually the errors on the FS60 images.* *Then I run recon-all on fs60 without to touch hippo-amyg.* *Now, I am trying to make the hippo-amyg correction with this command:* *segmentHA_T1.sh on fsdev, and I got this error:* [shiraz:FS] (nmr-dev-env) segmentHA_T1.sh test1 #-------------------------------------------- #@# Hippocampal Subfields processing (T1) left Fri Oct 11 17:20:27 EDT 2019 /usr/bin/time -o /dev/stdout @#@FSTIME 2019:10:11:17:20:27 run_segmentSubjectT1_autoEstimateAlveusML.sh N 13 e %e S %S U %U P %P M %M F %F R %R W %W c %c w %w I %I O %O L 1.23 1.35 1.67 run_segmentSubjectT1_autoEstimateAlveusML.sh /usr/local/freesurfer/dev/MCRv84/ test1 /cluster/neuromod/rivas/imagenes/FS 0.333333333333333333333333333333333333 /usr/local/freesurfer/dev/average/HippoSF/atlas/AtlasMesh.gz /usr/local/freesurfer/dev/average/HippoSF/atlas/AtlasDump.mgz /usr/local/freesurfer/dev/average/HippoSF/atlas/compressionLookupTable.txt 0.05 left L-BFGS v21 /usr/local/freesurfer/dev/bin/ 0 ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/lib64:/usr/local/freesurfer/dev/MCRv84//runtime/glnxa64:/usr/local/freesurfer/dev/MCRv84//bin/glnxa64:/usr/local/freesurfer/dev/MCRv84//sys/os/glnxa64:/native_threads:/server:/client:: 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 /cluster/neuromod/rivas/imagenes/FS/test1/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 '/cluster/neuromod/rivas/imagenes/FS/test1/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 ) 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 (37, 33, 61) 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 (37, 33, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - no Dst reslice necessary 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 -0.9335110261151 0 1.0000000000000 0 -0.6030053897425 0 0 1.0000000000000 -1.9033167008449 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 0 S( 37 33 61 ) T( 37 33 61 ) Iteration(f): 1 -- diff. to prev. transform: 17.9258 Iteration(f): 2 -- diff. to prev. transform: 13.3727 Iteration(f): 3 -- diff. to prev. transform: 12.6349 Iteration(f): 4 -- diff. to prev. transform: 0.963353 Iteration(f): 5 -- diff. to prev. transform: 0.23376 max it: 5 reached! - final transform: Tf = [ ... 0.9994502743718 -0.0309610775894 -0.0118558311665 0.1083605389283 0.0330356012422 0.9601637341023 0.2774783825190 -11.0026122646332 0.0027925093931 -0.2777175100517 0.9606587253036 3.8161835807274 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 = [ ... -0.2498625685929 -0.0029639577916 0.0077402693973 33.8236195063683 -0.0082589003105 0.0693695956298 -0.2400409335256 17.7299867104994 -0.0006981273483 0.2401646813259 0.0694293775129 -3.6765424847757 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 /cluster/neuromod/rivas/imagenes/FS/test1/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 $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 /cluster/neuromod/rivas/imagenes/FS/test1/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: Enabling affine transform! --sat: Using saturation 50 in M-estimator! reading source 'imageDump.mgz'... reading target '/cluster/neuromod/rivas/imagenes/FS/test1/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 ) 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 (37, 33, 61) 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 (37, 33, 61) voxels. -- Reslicing using cubic bspline MRItoBSpline degree 3 - no Dst reslice necessary 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 -0.9335121201217 0 1.0000000000000 0 -0.6030049400697 0 0 1.0000000000000 -1.9033196349668 0 0 0 1.0000000000000 ] - initial iscale: Ii =1 Resolution: 0 S( 37 33 61 ) T( 37 33 61 ) Iteration(f): 1 -- diff. to prev. transform: 29.7552 Iteration(f): 2 -- diff. to prev. transform: 13.9258 Iteration(f): 3 -- diff. to prev. transform: 12.8176 Iteration(f): 4 -- diff. to prev. transform: 4.31284 Iteration(f): 5 -- diff. to prev. transform: 1.08934 max it: 5 reached! - final transform: Tf = [ ... 1.1485282870250 0.1923732018210 0.0456719546813 -8.8430815689836 0.0248585691740 1.1974718877709 0.2901612074595 -15.4946754855698 0.0132871026014 0.0262311630292 0.9721734242629 -1.7628900186542 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 = [ ... -0.2871321056267 0.0114179894966 -0.0480933061884 36.4485194686672 -0.0062146408039 0.0725403105008 -0.2993680076302 19.7524929053736 -0.0033217759975 0.2430433850326 -0.0065577915391 -0.1873902167255 0 0 0 1.0000000000000 ] Determinant : -0.020683 Decompose into Rot * Shear * Scale : Rot = [ ... -0.9973893744923 0.0079822957206 -0.0717685070543 0.0722067536928 0.1210866300984 -0.9900122285773 -0.0007876362909 0.9926098483122 0.1213468939142 ] Shear = [ ... 1.0000000000000 -0.0253544642652 0.0881389503515 -0.0221787471386 1.0000000000000 -0.1442736100723 0.0921761860222 -0.1724865310828 1.0000000000000 ] Scale = diag([ 0.2859363885412 0.2501220610640 0.2990338055489 ]) 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 /cluster/neuromod/rivas/imagenes/FS/test1/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 Reading contexts of file /usr/local/freesurfer/dev/average/HippoSF/atlas/compressionLookupTable.txt Constructing image-to-world transform from header information (asegModCHA.mgz) Constructing image-to-world transform from header information (/cluster/neuromod/rivas/imagenes/FS/test1/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/dev/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-Accumbens-area map to reduced label 9 -------------- Making Left-Caudate map to reduced label 10 Error using segmentSubjectT1_autoEstimateAlveusML (line 365) The vector of prior probabilities in the mesh nodes must always sum to one over all classes. How may I correct it? Thanks lot, JC.

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TRACULA prep gets stuck in -prior 'WARN: Could not find satisfactory control point fit - try 20784 Finding center streamline'
by Mazzetti, C. (Cecilia) 11 Oct '19

11 Oct '19

External Email - Use Caution Dear all, I am trying to understand what is going on in my subjects (same problem with 5 different ones). the outputs from the first steps of trac-all seems to work fine, so I am running: trac-all -prep -c config_01C.txt -nocorr -noqa -nointra -nointer -nomasks -notensor -no-isrunning but the process gets stuck (in the log file I read something like 'WARN: Could not find satisfactory control point fit - try 20784 Finding center streamline' ) which keeps going... The only outputs which seem to be created are for cst and Ilf, the others not. I am also trying to include just some tract by specifying in the configuration file: set pathlist = ( lh.slfp_PP rh.slfp_PP \ lh.slft_PP rh.slft_PP ) but nothing seems to change.. Can I please have some assistance here? It has been days and I cannot seem to fix it.. also, I tried to run bedpost only but I receive an erro: trac-all -prep -c config_01C.txt -nocorr -noqa -nointra -nointer -nomasks -notensor -noprior -no-isrunning saying that I didn't specify any steps.. isn't bedpost a preprocessing step? also when tryingtrac-all -bedp -c config_02C.txt -no-isrunning? nothing works... is it because the -prior steps needs to be finished? Thanks to anyone willing to help! Best, Cecilia Cecilia Mazzetti - Ph.D. Researcher Donders Centre for Cognitive Neuroimaging, room 2.257 Kapittelweg 29 | 6525 EN Nijmegen

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Re: [Freesurfer] ERROR gtmseg
by Marina Fernández 11 Oct '19

11 Oct '19

External Email - Use Caution Dear Douglas, Thank you very much for the help. We could solve the problem with that subject but now we have a similar problem with another subject. In the previous case, we have only one problematic voxel in the skull, but now the label 75 (it shouldn't exist either) is taking a lot of voxels in the ventricles and the caudate. So, I think that we should use other solution to fix it. I know that this issue is weird. We use gtmseg command with more or less 500 subjects and we only have this type of problem with two subjects. Best regards, Marina. > Probably the easiest thing is just to edit that voxel to be one of the neighboring > segments. First, load the aseg and find that voxel. See which segment(s) are around > that voxel and get the segmentation id. Then run > mri_binarize --replaceonly 229 YourNewSegID --i aparc+aseg.mgz --o > aparc+aseg.mgz > then re-run gtmseg

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