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MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI.

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Diffusion-weighted imaging (DWI) variability increases across sessions, scanners, and subjects. Harmonizing scanner data is crucial for accurate group difference analysis in multisite studies.

Keywords:
DTINODDIbundle segmentationconnectomereproducibilityvariability

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Data Science

Background:

  • Diffusion-weighted imaging (DWI) is vital for identifying structural and connectivity differences.
  • Variability from session and scanner biases poses a significant challenge in DWI analysis.

Purpose of the Study:

  • To introduce MASiVar, a multisite dataset for investigating DWI variability.
  • To quantify intrasession, intersession, interscanner, and intersubject variability across common DWI processing methods.

Main Methods:

  • Developed MASiVar: a publicly available, preprocessed dataset of 319 DWI scans from 14 adults and 83 children across 3 sites and 4 scanners.
  • Evaluated variability in tensor models, multi-compartment models, white-matter bundle segmentation, and structural connectomics.

Main Results:

  • Variability consistently increased from intrasession to intersession, interscanner, and intersubject effects across all DWI processing approaches.
  • Interscanner variability was observed to approach intersubject variability in some cases.

Conclusions:

  • MASiVar enables global investigation of DWI variability across multiple levels and processing methods.
  • Scanner harmonization is recommended for multisite DWI analyses to ensure reliable group difference inference.