2026-05-05T21:51:42Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/705312025-03-06T07:52:44Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

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. Tax, Chantal M.W. 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. 2024-10-08T10:07:08Z 2024-10-08T10:07:08Z 2024-10-08T10:07:08Z 2024 info:eu-repo/semantics/article Medical Image Analysis, 2024, vol. 94, 103134. 1361-8415 https://uvadoc.uva.es/handle/10324/70531 10.1016/j.media.2024.103134 103134 Medical Image Analysis 94 eng https://www.sciencedirect.com/science/article/pii/S1361841524000598 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ Atribución 4.0 Internacional Elsevier