RT info:eu-repo/semantics/article
T1 Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning
A1 Planchuelo Gómez, Álvaro
A1 Descoteaux, Maxime
A1 Larochelle, Hugo
A1 Hutter, Jana
A1 Jones, Derek K.
A1 Tax, Chantal M.W.
K1 Quantitative MRI, Machine Learning, Brain, Diffusion-relaxation
AB 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 consistentlyaccurate 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 approachto develop shorter imaging protocols without compromising the quality of parameter estimates and signalpredictions.
PB Elsevier
SN 1361-8415
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/70531
UL https://uvadoc.uva.es/handle/10324/70531
LA eng
NO Medical Image Analysis, 2024, vol. 94, 103134.
NO Producción Científica
DS UVaDOC
RD 05-abr-2025