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MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging

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MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging
MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging
  •   CHANGES
  •   dataset_description.json
  •   participants.json
  •   participants.tsv
  •   README
  • derivatives
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README

MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging

Authors and Reference

Leon Y. Cai, Qi Yang, Praitayini Kanakaraj, Vishwesh Nath, Allen T. Newton, Heidi A. Edmonson, Jeffrey Luci, Benjamin N. Conrad, Gavin R. Price, Colin B. Hansen, Cailey I. Kerley, Karthik Ramadass, Fang-Cheng Yeh, Hakmook Kang, Eleftherios Garyfallidis, Maxime Descoteaux, Francois Rheault, Kurt G. Schilling, and Bennett A. Landman. MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging. Magnetic Resonance in Medicine, 2021.

Medical-image Analysis and Statistical Interpretation (MASI) Lab, Vanderbilt University, Nashville, TN, USA

Overview

MASiVar is a dataset consisting of 319 diffusion scans acquired at 3T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners curated to promote investigation of diffusion MRI variability. Cohort I consists of one adult subject scanned repeatedly on one scanner. This subject underwent three separate imaging sessions and acquired 3-4 scans per session. Cohort II consists of 5 adult subjects each scanned on 3-4 different scanners across 3 institutions. Each subject underwent 1-2 sessions on each scanner and had one scan acquired per session. Cohort III consists of 8 adult subjects all scanned on one scanner. Each subject underwent 1-6 sessions on the scanner and had two scans acquired per session. Cohort IV consists of 83 child subjects all scanned on one scanner. Each subject underwent 1-2 sessions on the scanner and had two scans acquired per session.

The acquisitions acquired per scan are as follows:

CohortShell (b-value)Number of Directions
I100096
150096
200096
250096
300096
II100030 to 33
100096
150096
200096
2465 or 250096
III100040
200056
IV100040
200056

Naming Scheme

MASiVar is in BIDS format with the following naming scheme:

  • Subject: sub-c<cohort>s<subject>
  • Session: ses-s<site (and scanner if applicable)>x<session number>
  • Acquisition: acq-b<shell>n<number of directions>r<resolution>pe<phase encoding direction>
    • Shells are indicated by b-value (s/mm2)
    • Resolutions are presented in 10-1 mm to maintain BIDS compliance.
    • Phase encoding direction APP indicates posterior-to-anterior and APA indicates anterior-to-posterior.
  • Run: run-

Example: sub-cIs1_ses-s1Ax2_acq-b3000n96r25x25x25peAPP_run-105_dwi.nii.gz is the fifth acquisition in the first scan of session 2 at site 1A for subject 1 of cohort I acquired with 96 directions at b = 3000 s/mm2 and a resolution of 2.5mm isotropic in the posterior-to-anterior phase encoding direction.

Note: Most of the subjects and sessions are named sequentially, however, some are not due to missed or truncated imaging sessions.

Derivatives

Both raw and preprocessed MASiVar data are available de-faced and de-identified. Diffusion images were preprocessed with PreQual v1.0.0 under default settings. More information about PreQual can be found here: https://github.com/MASILab/PreQual. In short, all acquisitions per scan were denoised with the Marchenko-Pastur PCA technique, intensity normalized, and distortion corrected. Distortion correction included susceptibility-induced distortion correction using APA b = 0 s/mm2 volumes when available and the Synb0-DisCo deep learning framework and T1 images when not, eddy current-induced distortion correction, intervolume motion correction, and slice-wise signal drop out imputation.

Comments

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By phd@oscaresteban.es - 8 months ago
Thanks for such a useful dataset. I have two questions:
- Are all the preprocessed images under the `derivatives/` folder? In other words, are the DWIs under e.g. `/sub-cIs1/` unprocessed?
- May I ask that the dataset be updated to include the `EffectiveEchoSpacing` or the `TotalReadoutTime` metadata of all `_dwi` and `_epi` files? Otherwise, the dataset cannot be used to investigate susceptibility-induced distortions.