Freesurfer

freesurfer@nmr.mgh.harvard.edu

17 participants
30129 discussions

Inquiry about the gtmpvc --reg-identity flag
by Y Zeng 01 May '25

01 May '25

External Email - Use Caution Hi, I am currently using mri_gtmpvc from PETSURFER for partial volume correction. I have registered the PET image to nu.mgz to get the coregistered PET image (in 1mm T1 space). As I also want to get the final output after PVC in the same space as PET scans, I use the –reg-identity flag, and the coregistered PET scan rather than the original PET scan as input. However, as the gtmseg.mgz is in the 0.5 mm space, it did give me results after Muller-Gartner PVC without any errors (the mgx.gm file is in the 1mm T1 space), but I am not sure if it is correct or not. Can you help me with this issue? I attached the code I used: mri_gtmpvc \ --i ${PET_smoothed_image_coregistered} \ --seg ${GTMSEG_resampled_to_T1mm} \ --reg-identity \ --default-seg-merge \ --no-reduce-fov \ --mgx .1 \ --psf 6 \ --no-rescale \ --o ${ROI_folder}/${FS_SUBJECT_ID}_gtmpvc_noscale Thanks, Yuheng

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mris_flatten limitations
by Glen, Daniel (NIH/NIMH) [E] 28 Apr '25

28 Apr '25

External Email - Use Caution I would like to abuse mris_flatten with a non-cerebrum surface. Will this work? It works for a sphere, but if I make cuts on more complicated shapes, it fails after hundreds of iterations with a "Segmentation fault". Are there any special tricks? Each patch file of cuts requires a different ?h.smoothwm file or h?.smoothwm file. If I copy the original surface to that requested file, the program continues. Is this correct? How can I predict this? I did manage to get one to make a flat.3D file, but display in Freeview or info from mris_info fails for that output, first using up large amounts of memory then crashes with another segmentation fault . Thanks, Daniel

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Join the mailing list
by Y Zeng 23 Apr '25

23 Apr '25

External Email - Use Caution Tsengyuheng1999(a)gmail.com

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mri_surf2vol error: mri_read(): code inconsistency (file type recognized but not caught)
by Loïc Daumail 20 Apr '25

20 Apr '25

External Email - Use Caution Dear all, I am trying to convert label files into volume ROI masks. This is my command below: mri_surf2vol --surfval /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/analysis/functional_surf_roi/sub-NSxLxYKx1964/sub-NSxLxYKx1964_hemi-L_space-fsnative_label-MT_mask.label --o /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/analysis/fonctional_vol_roi/sub-NSxLxYKx1964/sub-NSxLxYKx1964_hemi-L_space-fsnative_label-MT_mask.nii.gz --identity sub-NSxLxYKx1964 --hemi lh --fillribbon --template /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/derivatives/freesurfer/sub-NSxLxYKx1964/mri/brain.mgz I get this error: Reading surface /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/derivatives/freesurfer/sub-NSxLxYKx1964/surf/lh.white Done reading source surface Reading thickness /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/derivatives/freesurfer/sub-NSxLxYKx1964/surf/lh.thickness Done INFO: reading /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/analysis/functional_surf_roi/sub-NSxLxYKx1964/sub-NSxLxYKx1964_hemi-L_space-fsnative_label-MT_mask.label as unknown mri_read(): type = 32 error: mri_read(): code inconsistency (file type recognized but not caught) ERROR: could not read /Users/ldaumail3/Documents/research/ampb_mt_tractometry_analysis/ampb/analysis/functional_surf_roi/sub-NSxLxYKx1964/sub-NSxLxYKx1964_hemi-L_space-fsnative_label-MT_mask.label as unknown Any idea what would cause this? Is it because the labels weren't saved in the correct format? Thanks, Best, -Loïc

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problems processing of 7T mp2rage sequences
by Sabrina Turker 18 Apr '25

18 Apr '25

External Email - Use Caution Dear all, I'm new to freesurfer and I'm trying to preprocess a 7T mp2rage dataset (0.6mm³ isotropic, TR=6000ms, TE=2.05ms) with freesurfer 7.2.0. The images have been corrected for B1, for intensity inhomogeneity with ANTs N4 bias field removal and they were skull striped with FSL based on intensity. This was done bya colleague who analyzed this dataset in 2017 and I'm hoping to run our updated toolboxes on the preprocessed data. However, he ran into several problems while first analyzing the data: freesurfer did not like the noisy background of the mp2rage, which is why he skullstripped the data before preprocessing it with freesurfer. He then ran recon-all once with the default pipeline and once with skull strip deactivated. According to him, the default pipeline yielded better results. I'm using his skull-stripped data now for the preprocessing and was wondering whether not deactivating the skull strip will lead to inaccurate measures for cortical thickness and gray matter volume. Since this is crucial for our toolboxes, we really want to be sure that we're going for the best solution. A second problem is the high resolution of the data - it's not recommended to run the default recon-all on anything lower than 1mm³ (https://secure-web.cisco.com/1yYZ_CG0JlkOTN6kC9-IeDAZVMirVH7tWBx6OXS1Dwof-Q…) but most of the information I could find is from 2016 or 2017. At the time, he used the -hires flag but got very bad results, which made him use the default pipeline even if the mp2rage data got downsampled. I was wondering if anybody has feedback on how they analyzed a similar dataset (high resolution, problems with skullstripping etc.), general opinions on the best way to analyze this dataset and/or if there have been any developments (e.g., new flags for recon-all flags?) that I'm not aware of. We got feedback from one colleague that they did analyze their dataset successfully with the -hires flag and did not encounter any problems at the time. Any advice would be much appreciated! Best and thanks in advance, Sabrina -- Dr. Sabrina Turker (she/her) Postdoctoral researcher Brain and Language Lab Department of Behavioral & Cognitive Biology University of Vienna

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Unsubscribe
by Katarzyna Ciesla 17 Apr '25

17 Apr '25

External Email - Use Caution hi twice i unsscribed from the maiing list because the amount of emsils I get is just too much. I keep getting them So again, pless unsubscribe me. Dr Katarzyna Cieśla-Seifer The Baruch Ivcher Institute for Brain, Cognition & Technology, The Baruch Ivcher School of Psychology and the Ruth and Meir Rosental Brain Imaging Center, Reichman University, Herzliya, Israel, World Hearing Center, Warsaw, Poland

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SUVr in PET neurological images
by Gonzalo Rojas Costa 17 Apr '25

17 Apr '25

External Email - Use Caution Hi: Is it possible to compute SUVr in neurological PET images ? Using PETSurfer? How? Sincerely, Gonzalo Rojas Costa Adjunct Assistant Professor School of Medicine Finis Terrae University

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clarification - recon-all output data
by Stefanie Bradley 17 Apr '25

17 Apr '25

External Email - Use Caution Hi there, I wanted to clarify about the recon-all output: is it in the subject's native space or is everything already mapped to fsaverage space? For example, is aparc.stats based on the brain in the subject's native space or has the cortical surface already been transformed to fsaverage space, and then stats (ex., cortical thickness) derived after that? Thanks, Stefanie

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LME ROI group analysis
by Bondi, Daniel 16 Apr '25

16 Apr '25

External Email - Use Caution Hi FreeSurfer experts, I am using the univariate LME pipeline (lme_fit_FS & lme_F) to conduct cortical thickness analysis on a subset of D-K atlas ROIs between two groups. Although I am testing longitudinal group*time differences with this pipeline, I am wondering if it is valid to test group-level differences (irrespective of time) with this model as well. I have been doing so by specifying only the group covariate in the contrast matrix before running the model. Thank you, Daniel Bondi

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Hypothalamic subunits segmentation yields different results in versions 7.2.0 and 7.4.1
by Maria Paternina Die 15 Apr '25

15 Apr '25

External Email - Use Caution Dear all, I am running the mri_segment_hypothalamic_subunits module on a longitudinal dataset. As a quality control step, I have been removing those subjects for whom the output volume of any subunit is exactly zero, assuming that the segmentation failed for those cases. I have run this with FreeSurfer versions 7.2.0 (with the --threads flag enabled to 4) and 7.4.1 (without the --threads flag, as it caused the process to fail). From my 1,732 subjects, using the exclusion criteria of zero-volume in any subunit: - With version 7.2.0, I had to exclude about 17% of subjects. - With version 7.4.1, this number jumps to 72%. My question is: why is the latest version (7.4.1) producing so many 0 in some subunits? I’ve seen that some changes were introduced to this module in version 7.3.2. I believe this issue might be related to the following thread on the mailing list: https://secure-web.cisco.com/1XkIk65tX0PeiP2O9MZLgv1T5LO0ub4C7O1QmkU5Ce9f35… As additional information, we are running over ubuntu (24.04.2). The version 7.2.0 was installed with the deb installer and the version 7.4.1was the compiled one (tar.gz). The subunits with more 0 values are the anterior-inferior, posterior, and anterior-superior but also we found 0 in the tubular subunits, Many thanks for your help! Sincerely, *María Paternina Die* *Grupo de Neuroimagen, * *Hospital General Universitario Gregorio Marañón,* *Madrid, Spain* *Google Scholar: María Paternina-Die* *Website:https://secure-web.cisco.com/1g00gucsTRfBUHoFwCMckaiWl-XXgR4anWnA0N2hNTMX1Oyld05_nOH5knqLQ55m49-uPh62hII0ZFAsLy16YUwvaw7dd_4beDxHSue6_Vn5cGxSKDtKnzQvZhZlgvPGHswFqu9gqrqbfW0S3mBT3AEfZ3xAE6i1Ol_p8luGDA8zOpICAYDd9PEPaZ-N2zowKEPU5eNrLviBTjMTprN3ww90dZaraK1fcsZfBDNP5Wf53NA8Yoy-gbNtqJnClDURAGAo-OW92CYVkv26aTG1CuhMlkls6n-UsK7R_ZbMrkMGaT42vyEXfkLMLx7bhGL0c/https%3A%2F%2Fneuromaternal.es <https://secure-web.cisco.com/18fzi4Ql2j271hl0quVZhU9HPdWC0xf-x7Zs8qwrDJzvsR…>* -- *Este mensaje y sus archivos adjuntos son confidenciales y únicamente podrán ser usados por la persona o entidad a la que van dirigidos. Este mensaje puede contener información confidencial o legalmente protegida. No hay renuncia a la confidencialidad o secreto profesional por cualquier transmisión defectuosa o errónea. Si usted ha recibido este mensaje por error notifíqueselo inmediatamente al remitente. Le informamos que los datos personales que usted nos ha proporcionado son tratados por FUNDACIÓN PARA LA INVESTIGACIÓN BIOMÉDICA DEL HOSPITAL GREGORIO MARAÑÓN, domiciliada en la calle Doctor Esquerdo 46, Pabellón de Gobierno, planta baja, 28007 de Madrid con la finalidad de gestionar y mantener las relaciones profesionales que nos unen con Usted. Sus datos podrán ser comunicados a las entidades y administraciones públicas necesarias para la realización de dicha gestión. Este tratamiento de datos es necesario para mantener dicha relación profesional. Los datos se eliminarán cuando finalicen los plazos de prescripción marcados por la ley, conservándose únicamente para atender posibles reclamaciones. Ud. puede ejercer sus derechos de acceso, rectificación, cancelación, oposición, portabilidad y limitación del tratamiento de sus datos dirigiéndose a FUNDACIÓN PARA LA INVESTIGACIÓN BIOMÉDICA DEL HOSPITAL GREGORIO MARAÑÓN, a la dirección calle Doctor Esquerdo 46, Pabellón de Gobierno, planta baja, 28007 de Madrid, o vía correo electrónico al email gerencia(a)fibhgm.org <mailto:gerencia@fibhgm.org>, acompañando copia de su DNI acreditando debidamente su identidad. En cualquier situación, Ud. tiene derecho a presentar una reclamación ante la Agencia Española de Protección de Datos (AEPD). Datos Delegado de Protección de Datos (DPO) de FUNDACIÓN PARA LA INVESTIGACIÓN BIOMÉDICA DEL HOSPITAL GREGORIO MARAÑÓN: Alaro Avant, S.L. Avda. de Brasil 17, 7C, 28020, Madrid dpo.fibgregoriomaranon(a)alaroavant.com <mailto:dpo.fibgregoriomaranon@alaroavant.com> 9**14265115*

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