2021
DOI: 10.1002/mrm.28926
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MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI

Abstract: Purpose: Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge. Methods: To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm 2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and d… Show more

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Cited by 27 publications
(22 citation statements)
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“…Moreover, sonographically derived cross-sectional area still only provides morphological information (size and shape) which is inherently insensitive to nerve function and microstructure. Therefore, considerable effort has been directed towards the development of DTI because it characterises tissue microstructure and generates reproducible 4 8 proxy measures of nerve ‘health’ which are sensitive to myelination, axon diameter, fibre density and organisation 9 11 (Fig. 1 ).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Moreover, sonographically derived cross-sectional area still only provides morphological information (size and shape) which is inherently insensitive to nerve function and microstructure. Therefore, considerable effort has been directed towards the development of DTI because it characterises tissue microstructure and generates reproducible 4 8 proxy measures of nerve ‘health’ which are sensitive to myelination, axon diameter, fibre density and organisation 9 11 (Fig. 1 ).…”
Section: Discussionmentioning
confidence: 99%
“…Compression of peripheral nerves leads to distortion of the axonal architecture, demyelination with or without poor remyelination, loss of the intrinsic vasculature and ultimately, fibrosis of the perineurial and epineurial connective tissue 2 , 3 . Diffusion tensor imaging (DTI) characterises tissue microstructure and generates reproducible 4 8 proxy measures of nerve ‘health’ which are sensitive to myelination, axon diameter, fibre density and organisation 9 11 (Fig. 1 ).…”
Section: Introductionmentioning
confidence: 99%
“…Additional dataset was used acquired from the Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability (MASiVar) dataset 47 , consisting of 74 scans (removed 8 scans due to incomplete brain tissues in diffusion images) and 14 healthy adults (8 males and 6 females, age 27-47). This dataset was used to evaluate the stability of individualized cartography on multiple sessions, sites, and scanners.…”
Section: Methodsmentioning
confidence: 99%
“…For bundles, we identify 39 WM bundles from each tractogram with RecoBundlesX (Appendix C) (Garyfallidis et al, 2018;Rheault, 2020). We compare bundle streamline count, volume, length, span, and surface area between methodologies as well as geometric agreement with the bundle adjacency streamlines distance metric and Dice similarity coefficient (Rheault, 2020;Schilling et al, 2021;Yeh, 2020). For connectomics, we use 97 cortical regions defined by the SLANT BrainCOLOR framework and compute two types of connectomes, one with edges weighted by the streamline count between any two given regions and one weighted analogously by average streamline length (Huo et al, 2019;Sporns et al, 2005).…”
Section: Model Evaluationmentioning
confidence: 99%