DWI simulation-assisted machine learning models for microstructure estimation
Conference paper
Rafael-Patino, Jonathan, Yu, Thomas, Delvigne, Victor, Barakovic, Muhamed, Pizzolato, Marco, Girard, Gabriel, Jones, Derek K., Canales-Rodríguez, Erick J. and Thiran, Jean-Philippe. (2020). DWI simulation-assisted machine learning models for microstructure estimation. MICCAI 2019. Shenzhen, China 13 - 17 Oct 2023 Springer. pp. 125-134 https://doi.org/10.1007/978-3-030-52893-5_11
Authors | Rafael-Patino, Jonathan, Yu, Thomas, Delvigne, Victor, Barakovic, Muhamed, Pizzolato, Marco, Girard, Gabriel, Jones, Derek K., Canales-Rodríguez, Erick J. and Thiran, Jean-Philippe |
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Type | Conference paper |
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/mm2 |
Keywords | diffusion MRI; machine learning; Monte-Carlo simulations |
Year | 2020 |
Publisher | Springer |
ISSN | 1612-3786 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-52893-5_11 |
Scopus EID | 2-s2.0-85095864335 |
Open access | Published as green open access |
Author's accepted manuscript | License All rights reserved File Access Level Open |
Publisher's version | License All rights reserved File Access Level Controlled |
Book title | Computational Diffusion MRI : MICCAI Workshop, Shenzhen, China, October 2019 |
Page range | 125-134 |
Book editor | Bonet-Carne, Elisenda |
Hutter, Jana | |
Palombo, Marco | |
Pizzolato, Marco | |
Sepehrband, Farshid | |
Zhang, Fan | |
ISBN | 9783030528928 |
9783030528959 | |
9783030528935 | |
Web address (URL) of conference proceedings | https://www.miccai2019.org/ |
Output status | Published |
Publication dates | |
07 Nov 2020 | |
Online | 06 Nov 2020 |
Publication process dates | |
Deposited | 29 Mar 2023 |
https://acuresearchbank.acu.edu.au/item/8yy95/dwi-simulation-assisted-machine-learning-models-for-microstructure-estimation
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Author's accepted manuscript
AM_Rafael_Patino_2020_DWI_simulation_assisted_machine_learning_models.pdf | |
License: All rights reserved | |
File access level: Open |
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