What's new and what's next in diffusion MRI preprocessing
Tax, Chantal M.W.; Bastiani, Matteo; Veraart, Jelle; Garyfallidis, Eleftherios; Okan Irfanoglu, M.
(2022) NeuroImage, volume 249, pp. 1 - 35
(Article)
Abstract
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
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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.
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Keywords: Artifacts, Diffusion MRI, Distortion, Preprocessing, Brain/diagnostic imaging, Humans, Image Processing, Computer-Assisted/methods, Diffusion Magnetic Resonance Imaging/methods, Neurology, Cognitive Neuroscience, Review, Research Support, Non-U.S. Gov't, Journal Article, Research Support, N.I.H., Extramural
ISSN: 1053-8119
Publisher: Academic Press Inc.
Note: Funding Information: CMWT was supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant (17331) from the Dutch Research Council (NWO). MOI is supported by the Intramural Research Program of the National Institute of Biomedical Imaging and Bioengineering in the National Institutes of Health. The contents of this work do not necessarily reflect the position or the policy of the US government, and no official endorsement should be inferred. Research was performed as part of the Center of Advanced Imaging Innovation and Research (CAI2R, www.cai2r.net), an NIBIB Biomedical Technology Resource Center (NIH P41 EB017183) and was partially supported by the NINDS of the NIH (R01 NS088040). Work performed by EG for this paper was supported by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R01EB027585. Funding Information: CMWT was supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant (17331) from the Dutch Research Council (NWO). MOI is supported by the Intramural Research Program of the National Institute of Biomedical Imaging and Bioengineering in the National Institutes of Health. The contents of this work do not necessarily reflect the position or the policy of the US government, and no official endorsement should be inferred. Research was performed as part of the Center of Advanced Imaging Innovation and Research (CAI2R, www.cai2r.net ), an NIBIB Biomedical Technology Resource Center (NIH P41 EB017183) and was partially supported by the NINDS of the NIH (R01 NS088040). Work performed by EG for this paper was supported by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R01EB027585. Publisher Copyright: © 2021 Copyright © 2021. Published by Elsevier Inc.
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