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

uploaded by Leon Cai on 2020-12-03 - 7 months ago
last modified on 2020-12-03 - 7 months ago
authored by 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, Bennett A. Landman
222672
We found 1 Warning in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-cIIIsA01/ses-s1Bx1/anat/sub-cIIIsA01_ses-s1Bx1_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 256,256,184 (voxels), This file has the dimensions: 184,256,256 (voxels).

/sub-cIIIsA01/ses-s1Bx1/dwi/sub-cIIIsA01_ses-s1Bx1_acq-b1000n3r25x25x25peAPA_run-207_dwi.nii.gz

The most common set of dimensions is: 96,96,48,4 (voxels), This file has the dimensions: 96,96,50,4 (voxels). The most common resolution is: 2.50mm x 2.50mm x 2.50mm x 5.90s, This file has the resolution: 2.50mm x 2.50mm x 2.50mm x 3.00s.

/sub-cIIIsA01/ses-s1Bx1/dwi/sub-cIIIsA01_ses-s1Bx1_acq-b1000n3r25x25x25peAPA_run-413_dwi.nii.gz

The most common set of dimensions is: 96,96,48,4 (voxels), This file has the dimensions: 96,96,50,4 (voxels). The most common resolution is: 2.50mm x 2.50mm x 2.50mm x 5.90s, This file has the resolution: 2.50mm x 2.50mm x 2.50mm x 3.00s.

/sub-cIIIsA01/ses-s1Bx3/anat/sub-cIIIsA01_ses-s1Bx3_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 184,256,256 (voxels), This file has the dimensions: 256,256,184 (voxels).

/sub-cIIIsA03/ses-s1Bx1/anat/sub-cIIIsA03_ses-s1Bx1_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 256,256,184 (voxels), This file has the dimensions: 184,256,256 (voxels).

/sub-cIIIsA04/ses-s1Bx1/anat/sub-cIIIsA04_ses-s1Bx1_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 256,256,184 (voxels), This file has the dimensions: 184,256,256 (voxels).

/sub-cIIIsA04/ses-s1Bx3/anat/sub-cIIIsA04_ses-s1Bx3_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 184,256,256 (voxels), This file has the dimensions: 256,256,184 (voxels).

/sub-cIIIsA04/ses-s1Bx4/anat/sub-cIIIsA04_ses-s1Bx4_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 256,256,184 (voxels), This file has the dimensions: 184,256,256 (voxels).

/sub-cIIIsA04/ses-s1Bx5/anat/sub-cIIIsA04_ses-s1Bx5_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 184,256,256 (voxels), This file has the dimensions: 256,256,184 (voxels).

/sub-cIIIsC016/ses-s1Bx1/anat/sub-cIIIsC016_ses-s1Bx1_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 256,256,184 (voxels), This file has the dimensions: 184,256,256 (voxels).

and 84 more files

OpenNeuro Accession Number: ds003416
Files: 4483, Size: 17.42GB, Subjects: 97, Sessions: 12
No Available Tasks
Available Modalities: T1w, dwi

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. bioRxiv, 2020. Preprint.

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

Overview

MASiVar is a dataset designed to promote investigation of diffusion MRI variability consisting of 319 diffusion scans acquired at 3T from b = 1000 to 3000 s/mm2 across 97 different healthy subjects and four different scanners at three different sites. Cohort I consists of one 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 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 91 subjects all scanned on one scanner. Each subject underwent 1-5 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

Naming Scheme

MASiVar is in BIDS format with the following naming scheme:

  • Subject: sub-c<cohort>s<subject>
    • For cohort III, subjects with prefix A are adults and those with prefix C are children.
  • 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 sessions in MASiVar 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 technique, intensity normalized such that the average b = 0 s/mm2 intensity distributions within the brain maximally intersected, 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 when not, eddy current-induced distortion correction, intervolume motion correction, and slice-wise signal drop out imputation.

Changelog

  • v1.0.0: Initial Release

Authors

  • 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
  • Bennett A. Landman

Dataset DOI

Create a new snapshot to obtain a DOI for the snapshot.

License

Custom

Acknowledgements

The authors thank E. Brian Welch for his help with image acquisition and study design and Zachary J. Williams for his insight into bootstrapping. This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN.

How to Acknowledge

Please cite the included reference.

Funding

  • NIH 5R01EB017230
  • NIH 5T32EB001628
  • NIH 5T32GM007347
  • NIH 1UL1RR024975
  • NSF 1452485
  • NSF 1660816
  • NSF 1750213

References and Links

  • 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. bioRxiv, 2021. Preprint.

Ethics Approvals

  • All images were acquired only after informed consent under supervision of the project Institutional Review Board.

How To Cite

Copy
Leon Y. Cai and Qi Yang and Praitayini Kanakaraj and Vishwesh Nath and Allen T. Newton and Heidi A. Edmonson and Jeffrey Luci and Benjamin N. Conrad and Gavin R. Price and Colin B. Hansen and Cailey I. Kerley and Karthik Ramadass and Fang-Cheng Yeh and Hakmook Kang and Eleftherios Garyfallidis and Maxime Descoteaux and Francois Rheault and Kurt G. Schilling and Bennett A. Landman (2020). MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging. OpenNeuro. [Dataset] doi: null
More citation info

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

uploaded by Leon Cai on 2020-12-03 - 7 months ago
last modified on 2020-12-03 - 7 months ago
authored by 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, Bennett A. Landman
222672

OpenNeuro Accession Number: ds003416
Files: 4483, Size: 17.42GB, Subjects: 97, Sessions: 12
No Available Tasks
Available Modalities: T1w, dwi

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. bioRxiv, 2020. Preprint.

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

Overview

MASiVar is a dataset designed to promote investigation of diffusion MRI variability consisting of 319 diffusion scans acquired at 3T from b = 1000 to 3000 s/mm2 across 97 different healthy subjects and four different scanners at three different sites. Cohort I consists of one 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 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 91 subjects all scanned on one scanner. Each subject underwent 1-5 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

Naming Scheme

MASiVar is in BIDS format with the following naming scheme:

  • Subject: sub-c<cohort>s<subject>
    • For cohort III, subjects with prefix A are adults and those with prefix C are children.
  • 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 sessions in MASiVar 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 technique, intensity normalized such that the average b = 0 s/mm2 intensity distributions within the brain maximally intersected, 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 when not, eddy current-induced distortion correction, intervolume motion correction, and slice-wise signal drop out imputation.

Changelog

  • v1.0.0: Initial Release

Authors

  • 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
  • Bennett A. Landman

Dataset DOI

Create a new snapshot to obtain a DOI for the snapshot.

License

Custom

Acknowledgements

The authors thank E. Brian Welch for his help with image acquisition and study design and Zachary J. Williams for his insight into bootstrapping. This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN.

How to Acknowledge

Please cite the included reference.

Funding

  • NIH 5R01EB017230
  • NIH 5T32EB001628
  • NIH 5T32GM007347
  • NIH 1UL1RR024975
  • NSF 1452485
  • NSF 1660816
  • NSF 1750213

References and Links

  • 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. bioRxiv, 2021. Preprint.

Ethics Approvals

  • All images were acquired only after informed consent under supervision of the project Institutional Review Board.

How To Cite

Copy
Leon Y. Cai and Qi Yang and Praitayini Kanakaraj and Vishwesh Nath and Allen T. Newton and Heidi A. Edmonson and Jeffrey Luci and Benjamin N. Conrad and Gavin R. Price and Colin B. Hansen and Cailey I. Kerley and Karthik Ramadass and Fang-Cheng Yeh and Hakmook Kang and Eleftherios Garyfallidis and Maxime Descoteaux and Francois Rheault and Kurt G. Schilling and Bennett A. Landman (2020). MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging. OpenNeuro. [Dataset] doi: null
More citation info

Dataset File Tree

Git Hash: 54fb8de 

BIDS Validation

We found 1 Warning in your dataset. You are not required to fix warnings, but doing so will make your dataset more BIDS compliant.

/sub-cIIIsA01/ses-s1Bx1/anat/sub-cIIIsA01_ses-s1Bx1_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 256,256,184 (voxels), This file has the dimensions: 184,256,256 (voxels).

/sub-cIIIsA01/ses-s1Bx1/dwi/sub-cIIIsA01_ses-s1Bx1_acq-b1000n3r25x25x25peAPA_run-207_dwi.nii.gz

The most common set of dimensions is: 96,96,48,4 (voxels), This file has the dimensions: 96,96,50,4 (voxels). The most common resolution is: 2.50mm x 2.50mm x 2.50mm x 5.90s, This file has the resolution: 2.50mm x 2.50mm x 2.50mm x 3.00s.

/sub-cIIIsA01/ses-s1Bx1/dwi/sub-cIIIsA01_ses-s1Bx1_acq-b1000n3r25x25x25peAPA_run-413_dwi.nii.gz

The most common set of dimensions is: 96,96,48,4 (voxels), This file has the dimensions: 96,96,50,4 (voxels). The most common resolution is: 2.50mm x 2.50mm x 2.50mm x 5.90s, This file has the resolution: 2.50mm x 2.50mm x 2.50mm x 3.00s.

/sub-cIIIsA01/ses-s1Bx3/anat/sub-cIIIsA01_ses-s1Bx3_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 184,256,256 (voxels), This file has the dimensions: 256,256,184 (voxels).

/sub-cIIIsA03/ses-s1Bx1/anat/sub-cIIIsA03_ses-s1Bx1_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 256,256,184 (voxels), This file has the dimensions: 184,256,256 (voxels).

/sub-cIIIsA04/ses-s1Bx1/anat/sub-cIIIsA04_ses-s1Bx1_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 256,256,184 (voxels), This file has the dimensions: 184,256,256 (voxels).

/sub-cIIIsA04/ses-s1Bx3/anat/sub-cIIIsA04_ses-s1Bx3_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 184,256,256 (voxels), This file has the dimensions: 256,256,184 (voxels).

/sub-cIIIsA04/ses-s1Bx4/anat/sub-cIIIsA04_ses-s1Bx4_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 256,256,184 (voxels), This file has the dimensions: 184,256,256 (voxels).

/sub-cIIIsA04/ses-s1Bx5/anat/sub-cIIIsA04_ses-s1Bx5_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 184,256,256 (voxels), This file has the dimensions: 256,256,184 (voxels).

/sub-cIIIsC016/ses-s1Bx1/anat/sub-cIIIsC016_ses-s1Bx1_acq-r10x10x10_T1w.nii.gz

The most common set of dimensions is: 256,256,184 (voxels), This file has the dimensions: 184,256,256 (voxels).

and 84 more files

Dataset File Tree

Git Hash: 54fb8de 

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