Freesurfer

freesurfer@nmr.mgh.harvard.edu

21 participants
30070 discussions

wm-hypointensities
by Andrew Dedinas Geoly 11 Jul '23

11 Jul '23

External Email - Use Caution Hi Freesurfer experts! I am hoping to extract the wm-hypointensity data from some recon-all-clinical runs. I was able to extract aseg.stats information correctly but noticed there was no information regarding the wm-hypointensities. Is this supported by recon-all-clinical? Best, Andrew --- Andrew D. Geoly, MS Research Data Analyst 1 Brain Stimulation Lab Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine

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WM-hypointensities
by Andrew Dedinas Geoly 11 Jul '23

11 Jul '23

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Preserving original T1w space coordinate
by parsatad＠student.ubc.ca 11 Jul '23

11 Jul '23

External Email - Use Caution Hello, Hope you are doing well. I was wondering if the parcellation process recenters the coordinates and if so, is there a way to not recenter and preserve original T1w space coordinates? Thanks, Parsa

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VM passcode
by Haley O Higginbotham 11 Jul '23

11 Jul '23

External Email - Use Caution Hi, I am trying to set up the VM version of freesurfer. Could you send the passcode? Thanks, -HH

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Partnership Proposal for Freesurfer Expert Training with Universitas Indonesia
by Pukovisa Prawiroharjo 11 Jul '23

11 Jul '23

External Email - Use Caution Dear Freesurfer Team, Greetings. My name is dr. Pukovisa Prawiroharjo, Sp.S(K), PhD. I am a lecturer and researcher at the Department of Neurology Faculty of Medicine Universitas Indonesia (UI) in Indonesia. I am pleased to inform you that we have been awarded a grant from the UI Incubate program. This program is aimed at promoting growth in university startups that focus on innovative solutions. Our project revolves around the concept of a "Brain Analysis Consultation Service." Our primary goal is to democratize access to predictive analytics for neurodegenerative diseases, making such advanced techniques more available to the general public. The business model is to build a clinic for brain analysis using Freesurfer as a core element of our service. The data given by the MRI will be used to analyze and predict neurodegenerative diseases from our patients. The service we aim to offer will allow patients to consult with us about their Freesurfer results for our team to analyze. Through this, we hope to give them a clearer understanding of their condition, helping predict the trajectory of their neurodegenerative disease more accurately. With this in mind, we would like to invite your team to conduct an expert class on Freesurfer for us. This class will surely boost our understanding of the software and improve the efficiency and effectiveness of our work. We sincerely hope this proposal interests you and aligns with your mission to further knowledge and research in neuroscience. If so, we would be delighted to discuss this further and provide you with any more details you may need. Thank you for considering our proposal. We look forward to the potential of working together for a common goal. Best regards, *Pukovisa Prawiroharjo, M.D., Sp.N, Subsp. NGD (K), PhD* Neurobehavior Consultant, Neurology Specialist Neurology Department, Faculty of Medicine, Universitas Indonesia Chair of Bioethics Cluster IMERI UI Chair of International Policy Div, Board of Development Med Services IDI

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recon-all-clinical Longitudinal workflow
by Andrew Dedinas Geoly 10 Jul '23

10 Jul '23

External Email - Use Caution Hello Freesurfer experts! I am conducting a longitudinal GMV and CT analysis of mTBI participants who received an experimental single-dose pharmacological treatment. We collected GE Bravo (0.9mm isotropic) T1w data pre, immediately after (~ 4 days after intervention) and 1-month following the intervention. I have been working with the recon-all longitudinal pipeline stream (7.1.1) but have noticed pretty significant errors in terms of skull stripping and segmentation (participants are veterans with somewhat random lesion / atrophy patterns). I have tested your new recon-all-clinical program (7.4.0) on some of my worst cases and it is performing remarkably well. I am doing some more digging into the various synth segmentation packages available in FS newest versions and was wondering what the best course of action would be for integrating recon-all-clinical or synthseg into a longitudinal workflow. 1.Could I use the recon-all-clinical outputs as the "cross" for later base and long template construction? 2. SynthSeg and SCLimbic seem to have very good test-retest reliability-- would it be feasible to use the independent segmentation extractions from those and bypass the longitudinal stream entirely (not saying I would want to but I am curious)? Any and all help here would be greatly appreciated, and I thank you very much for your time. Warmest regards, Andrew --- Andrew D. Geoly, MS Research Data Analyst 1 Brain Stimulation Lab Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine http://secure-web.cisco.com/1QSBlrn3-LhYcwEz_Sjst4ZqWKpoVeamcijnxImhD8Wxefq… [cid:40f0dd9c-7a7e-4ec0-83d1-c42f63d1a100]

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Neuroimaging Postdoctoral Fellowship Position: Bronte-Stewart Lab at Stanford University
by Kevin Bryant Wilkins 10 Jul '23

10 Jul '23

External Email - Use Caution The Human Motor Control and Neuromodulation Lab under Dr. Helen Bronte-Stewart (https://secure-web.cisco.com/15Hq88HuNDDnZQmpUkIStHpG3Ewp-EmDoZs51P12SQmLNq…) part of the Stanford Movement disorders Center within the Department of Neurology and Neurological Sciences at Stanford University’s School of Medicine, is seeking a postdoctoral scholar with neuroimaging expertise to investigate cognitive-motor dysfunction in Parkinson’s disease. The current position is for a project where we will be conducting a pilot clinical trial using a novel deep brain stimulation approach for cognitive and cognitive-motor dysfunction in Parkinson’s disease. The candidate will help validate the use of a novel target for deep brain stimulation using a combination of tractography and DBS modeling (e.g., volume of tissue activated). You will work together with neurosurgeons, radiologists, and neurologists on the trial to validate various trajectories and targets. You will work with stimulation modeling software to individualize stimulation parameters for participants to optimize efficacy. In addition to your role on this project, you will get the chance to investigate structural differences in brain morphology between different neurodegenerative diseases. The current position offers an exceptional opportunity for neuroscientists with a background in structural neuroimaging and/or cognition to apply these skills in a clinically-relevant domain. The desired candidate would have a PhD in Neuroscience or a related discipline with experience in neuroimaging, cognition, and/or motor control. Preferred start date is before September 2023 but is flexible. Job Requirements Required: PhD in Neuroscience or related disciplines with expertise in data science Experience in structural neuroimaging (tractography, voxel-based morphometry, etc.) Experience working in the area of cognition and/or motor control Comfortable analyzing data in Matlab, Python, or similar languages Strong data science skills Ability to work well in a diverse team Effective oral and written communication skills Excellent organization skills and demonstrated ability to complete detailed work accurately Excellent problem-solving ability Preferred: Experience with Parkinson’s disease or other clinical populations such as Alzheimer’s disease Experience with deep brain stimulation modeling (volumes of tissue activated, etc.) Experience with larger data sets How to Apply: Apply by sending the following to Kevin Wilkins (wilkins2 at stanford.edu) CV Cover letter describing interest and relevant experience for the project Three potential reference contacts (We will not contact references until confirming with you) The expected base pay for this position is the Stanford University required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY23 minimum is $68,238. Stanford University is an affirmative action and equal opportunity employer, committed to increasing the diversity of its workforce. It welcomes applications from women, members of minority groups, veterans, persons with disabilities, and others who would bring additional dimensions to the university's research and teaching mission

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some thalamic subregions are not shown in image of ThalamicNuclei.v13.T1.mgz
by zlseu2109 10 Jul '23

10 Jul '23

External Email - Use Caution TO: freesurfer(a)nmr.mgh.harvard.edu FROM: zlseu2109(a)163.com SUBJECT: segmentation of thalamic nuclei error: some thalamic subregions are not shown in image of ThalamicNuclei.v13.T1.mgz Hello FreeSurfer Developers, I’ve segmented thalamic nuclei via thalamic nuclei atlas (https://secure-web.cisco.com/1WjR2s5Zpo0C0UoA2TqjDmB6EwtzK0RI_lg2slhweQFKPQ…) and gained volume result of 25 subregions of each thalamus, but some subregions (eg. Right Pt) cannot be shown by load ThalamicNuclei.v13.T1.mgz in freeview. I'm attempting to use this thalamic nuclei atlas for functional ROIs analysis, using other softwares (eg.SPM). Could you provide .nii file of this atlas? Or can you help me show this missed thalamic subregions? Or how can I transform the file of thalamic nuclei atlas in FreeSurfer pipeline to .nii file. I've searched the list and no similar errors have been reported. Does anyone have any thoughts on how to trouble-shoot this one? 1) FreeSurfer version: freesurfer-Linux-ubuntu20_x86_64-7.3.2 2) Platform: Ubuntu Release 20.04.6 (focal) | | zlseu2109 | | zlseu2109(a)163.com |

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ill-conditioned matrix problem
by 조상아 09 Jul '23

09 Jul '23

External Email - Use Caution Hello FreeSurfer Developers, I have a question. I'm attempting to run an estimation with my subjects (tic patients versus controls) and get stuck with an ERROR: matrix is ill-conditioned or badly scaled, condno = 31219.4 As covariates, we included three variables ( age, IQ, Total intracranial volume ). We added our matrix file as an attached file - lh-Avg-thickness-age-Cor.mtx. The number of regressors are 16 (The number of classes 4) * ((The number of continuous covariates=3) + 1)=16. I've searched the list for the similar cases and found no answers reported. Does anyone have any thoughts on how to trouble-shoot this one? Also, Ive attached the fsgd , matrix file, and error screen-captured. 1) FreeSurfer version: freesurfer-linux-ubuntu22_x86_64-7.3.2-20220804-6354275 2) Platform: ubuntu 22.04 3) uname -a: Linux capneurodata-5950x 5.19.0-46-generic #47~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Wed Jun 21 15:35:31 UTC 2 x86_64 x86_64 x86_64 GNU/Linux

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problem with infantfreesurfer
by Knut J Bjuland 09 Jul '23

09 Jul '23

External Email - Use Caution Dear Freesurfer expert I have found this problem when I am running infant freesurfer on newborn mri image. Best regards Knut Jørgen Bjuland mri_convert --conform /home/knutjb/subjects/EPOP510_fs/work/mprage.nu.conf.nii.gz /home/knutjb/subjects/EPOP510_fs/work/sscnn/conformed.nii.gz WARNING ==================++++++++++++++++++++++++======================================= The physical sizes are (316.00 mm, 316.00 mm, 316.00 mm), which cannot fit in 256^3 mm^3 volume. The resulting volume will have 316 slices. If you find problems, please let us know (freesurfer(a)nmr.mgh.harvard.edu) ==================================================++++++++++++++++++++++++=============== reading from /home/knutjb/subjects/EPOP510_fs/work/mprage.nu.conf.nii.gz... TR=2400.00, TE=0.00, TI=0.00, flip angle=0.00 i_ras = (-0.998888, 0.0471505, 5.29093e-11) j_ras = (-0.0107408, -0.227545, -0.973708) k_ras = (0.0459108, 0.972625, -0.227799) Reslicing using trilinear interpolation writing to /home/knutjb/subjects/EPOP510_fs/work/sscnn/conformed.nii.gz... [<tf.Tensor 'bnorm_D_1_00_1/cond/Merge:0' shape=(?, 256, 256, 32) dtype=float32>, <tf.Tensor 'bnorm_D_1_01_1/cond/Merge:0' shape=(?, 256, 256, 32) dtype=float32>, <tf.Tensor 'bnorm_D_1_10_1/cond/Merge:0' shape=(?, 128, 128, 64) dtype=float32>, <tf.Tensor 'bnorm_D_1_11_1/cond/Merge:0' shape=(?, 128, 128, 64) dtype=float32>, <tf.Tensor 'bnorm_D_1_20_1/cond/Merge:0' shape=(?, 64, 64, 128) dtype=float32>, <tf.Tensor 'bnorm_D_1_21_1/cond/Merge:0' shape=(?, 64, 64, 128) dtype=float32>, <tf.Tensor 'bnorm_D_1_30_1/cond/Merge:0' shape=(?, 32, 32, 256) dtype=float32>, <tf.Tensor 'bnorm_D_1_31_1/cond/Merge:0' shape=(?, 32, 32, 256) dtype=float32>, <tf.Tensor 'bnorm_D_1_40_1/cond/Merge:0' shape=(?, 16, 16, 512) dtype=float32>, <tf.Tensor 'bnorm_D_1_41_1/cond/Merge:0' shape=(?, 16, 16, 512) dtype=float32>, <tf.Tensor 'bnorm_D_1_50_1/cond/Merge:0' shape=(?, 8, 8, 1024) dtype=float32>, <tf.Tensor 'bnorm_D_1_51_1/cond/Merge:0' shape=(?, 8, 8, 1024) dtype=float32>] 12 Loaded model file: /usr/local/infantsfreesurfer/average/sscnn_skullstripping/cor_sscnn.h5 2.073170731707317 Image max is :2.0650406504065044 Traceback (most recent call last): File "/usr/local/infantsfreesurfer/python/scripts/sscnn_skullstrip", line 74, in <module> cor_img_data = predict('cor') File "/usr/local/infantsfreesurfer/python/scripts/sscnn_skullstrip", line 70, in predict net.predict_slice_segmentation([conformed_file], [args.contrast], direction_fullnames[direction], out_membership_file, out_hard_file) File "/usr/local/infantsfreesurfer/python/packages/sscnn_skullstripping/deeplearn_utils/DeepImageSynth.py", line 807, in predict_slice_segmentation out_slices = self.model.predict(input_feature_array) File "/usr/local/infantsfreesurfer/python/lib/python3.6/site-packages/keras/engine/training.py", line 1147, in predict x, _, _ = self._standardize_user_data(x) File "/usr/local/infantsfreesurfer/python/lib/python3.6/site-packages/keras/engine/training.py", line 749, in _standardize_user_data exception_prefix='input') File "/usr/local/infantsfreesurfer/python/lib/python3.6/site-packages/keras/engine/training_utils.py", line 137, in standardize_input_data str(data_shape)) ValueError: Error when checking input: expected input_ax to have shape (256, 256, 1) but got array with shape (316, 316, 1)

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