Frontiers in Neuroscience (May 2020)

Harmonization of Brain Diffusion MRI: Concepts and Methods

  • Maíra Siqueira Pinto,
  • Maíra Siqueira Pinto,
  • Roberto Paolella,
  • Roberto Paolella,
  • Thibo Billiet,
  • Pieter Van Dyck,
  • Pieter-Jan Guns,
  • Ben Jeurissen,
  • Annemie Ribbens,
  • Arnold J. den Dekker,
  • Jan Sijbers

DOI
https://doi.org/10.3389/fnins.2020.00396
Journal volume & issue
Vol. 14

Abstract

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MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.

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