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Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning

Planchuelo-Gómez, Álvaro, Descoteaux, Maxime, Larochelle, Hugo, Hutter, Jana, Jones, Derek K. ORCID: and Tax, Chantal M.W. ORCID: 2024. Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning. Medical Image Analysis 94 , 103134. 10.1016/

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Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion- T 1 - T 2 ∗ -weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Physics and Astronomy
Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information: License information from Publisher: LICENSE 1: Title: This article is under embargo with an end date yet to be finalised.
Publisher: Elsevier
ISSN: 1361-8415
Date of First Compliant Deposit: 8 March 2024
Date of Acceptance: 4 March 2024
Last Modified: 08 Apr 2024 14:45

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