@inproceedings{3f679fc3d3374cb989115e2166bd9ea6,
title = "Current Applications and Future Promises of Machine Learning in{\^A} Diffusion MRI",
abstract = "Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) explores the random motion of diffusing water molecules in biological tissue and can provide information on the tissue structure at a microscopic scale. DW-MRI in used in many applications both in the brain and other parts of the body such as the breast and prostate, and novelcomputational methods are at the core of advancements in DW-MRI, both in terms of research and its clinical translation. This article reviews the ways in whichmachine learning anddeep learning is currently applied in DW-MRI. We will also discuss the more traditional methods used for processing diffusion MRI and the potential of deep learning in augmenting these existing methods in the future.",
keywords = "Deep learning, Diffusion-weighted, Machine learning, MRI",
author = "Daniele Ravi and Nooshin Ghavami and Alexander, {Daniel C.} and Andrada Ianus",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 ; Conference date: 20-09-2018 Through 20-09-2018",
year = "2019",
doi = "10.1007/978-3-030-05831-9_9",
language = "English",
isbn = "9783030058302",
series = "Mathematics and Visualization",
publisher = "Springer Nature Link",
number = "226249",
pages = "105--121",
editor = "Francesco Grussu and Farshid Sepehrband and Lipeng Ning and Tax, {Chantal M.W.} and Elisenda Bonet-Carne",
booktitle = "Mathematics and Visualization",
address = "Netherlands",
edition = "226249",
}