DWI simulation-assisted machine learning models for microstructure estimation

Rafael-Patino, Jonathan, Yu, Thomas, Delvigne, Victor, Barakovic, Muhamed, Pizzolato, Marco, Girard, Gabriel, Jones, Derek K. ORCID: https://orcid.org/0000-0003-4409-8049, Canales-Rodríguez, Erick J. and Thiran, Jean-Philippe 2020. DWI simulation-assisted machine learning models for microstructure estimation. Presented at: MICCAI Workshop, Shenzhen, China, Oct 2019. Published in: Bonet-Carne, Elisenda, Hutter, Jana, Palombo, Marco, Pizzolato, Marco, Sepehrband, Farshid and Zhang, Fan eds. Computational Diffusion MRI. Mathematics and Visualization. Mathematics and Visualization Springer, pp. 125-134. 10.1007/978-3-030-52893-5_11

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Abstract

Diffusion MRI (DW-MRI) allows for the detailed exploration of the brain white matter microstructure, with applications in both research and the clinic. However, state-of-the-art methods for microstructure estimation suffer from known limitations, such as the overestimation of the mean axon diameter, and the infeasibility of fitting diameter distributions. In this study, we propose to eschew current modeling-based approaches in favor of a novel, simulation-assisted machine learning approach. In particular, we train machine learning (ML) algorithms on a large dataset of simulated diffusion MRI signals from white matter regions with different axon diameter distributions and packing densities. We show, on synthetic data, that the trained models provide an accurate and efficient estimation of microstructural parameters in-silico and from DW-MRI data with moderately high b-values (4000 s/mm 2 ). Further, we show, on in-vivo data, that the estimators trained from simulations can provide parameter estimates which are close to the values expected from histology.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Psychology
Publisher:	Springer
ISBN:	9783030528928
ISSN:	1612-3786
Last Modified:	09 Nov 2022 11:31
URI:	https://orca.cardiff.ac.uk/id/eprint/143626

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