What’s new and what’s next in diffusion MRI preprocessing

• Published in: NeuroImage (Orlando, Fla.) Vol. 249; p. 118830
• Main Authors: Tax, Chantal M.W., Bastiani, Matteo, Veraart, Jelle, Garyfallidis, Eleftherios, Okan Irfanoglu, M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-04-2022; Elsevier Limited; Elsevier
• Subjects: Algorithms; Anisotropy; Brain - diagnostic imaging; Deep learning; Diffusion Magnetic Resonance Imaging - methods; Diffusion Magnetic Resonance Imaging - standards; Diffusion Magnetic Resonance Imaging - trends; Diffusion MRI; Distortion; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - standards; Image Processing, Computer-Assisted - trends; Integrated approach; Magnetic resonance imaging; Neural networks; Neuroimaging; Noise; Preprocessing; Quality control; Registration; Artifacts; Diffusion MRI; Distortion; Preprocessing
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.118830

•This review covers diffusion MRI artifacts and preprocessing steps.•Notable developments and new advances since the HCP are summarized.•Practical considerations and future developments are discussed.
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.