ArticleMagnetic resonance in medicine2021
MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI.
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of the normal diffusion tensor imaging values of the peripheral nerves in the upper limb.Scientific reports · 2023Pooled it
- Reproducibility of diffusion, shape, and connectivity metrics across scanners: Implications for multi-site tractography.NeuroImage · 2026Article
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
- Deep white matter MRI predicts outcomes in coma of various etiologies: a cohort study.Critical care (London, England) · 2026Article
- Longitudinal quantitative streamline tractography: robust estimation of white matter connectivity differences.bioRxiv : the preprint server for biology · 2026Article
- What needs to be standardized for reliable, reproducible, and robust tractography?GigaScience · 2026Review
- Article
- Challenges and best practices when using ComBAT to harmonize diffusion MRI data.Scientific reports · 2025Article
- Review
- Scalable, reproducible, and cost-effective processing of large-scale medical imaging datasets.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Deep learning-based automatic segmentation of brain structures on MRI: A test-retest reproducibility analysis.Computational and structural biotechnology journal · 2025Article
- Characterizing normal perinatal development of the human brain structural connectivity.Human brain mapping · 2024Article
- Anatomy-aware and acquisition-agnostic joint registration with SynthMorph.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Prior-guided individualized thalamic parcellation based on local diffusion characteristics.Human brain mapping · 2024Article
- Modeling the impact of MRI acquisition bias on structural connectomes: Harmonizing structural connectomes.Network neuroscience (Cambridge, Mass.) · 2024Article
- Robust fiber orientation distribution function estimation using deep constrained spherical deconvolution for diffusion-weighted magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Tractography from T1-weighted MRI: Empirically exploring the clinical viability of streamline propagation without diffusion MRI.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Efficient approximate signal reconstruction for correction of gradient nonlinearities in diffusion-weighted imaging.Magnetic resonance imaging · 2023Article
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Authors and funding
19 authors.
Funding
Abstract
purposeDiffusion-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.
methodsTo 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
resultsWe plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability.
conclusionsThis study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.
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