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Harmonization of Brain Diffusion MRI: Concepts and Methods

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

2020Frontiers in Neuroscience133 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

HarmonizationDiffusion MRIDiffusionComputer scienceNeurosciencePsychologyMagnetic resonance imagingMedicinePhysicsRadiologyThermodynamicsAcousticsAdvanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsMRI in cancer diagnosis
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