TY - JOUR
T1 - Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning
AU - Planchuelo-Gómez, Álvaro
AU - Descoteaux, Maxime
AU - Larochelle, Hugo
AU - Hutter, Jana
AU - Jones, Derek K.
AU - Tax, Chantal M.W.
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/5
Y1 - 2024/5
N2 - 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-T1-T2∗-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.
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-T1-T2∗-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.
KW - Brain
KW - Diffusion-relaxation
KW - Machine learning
KW - Quantitative MRI
UR - http://www.scopus.com/inward/record.url?scp=85187237205&partnerID=8YFLogxK
U2 - 10.1016/j.media.2024.103134
DO - 10.1016/j.media.2024.103134
M3 - Article
C2 - 38471339
AN - SCOPUS:85187237205
SN - 1361-8415
VL - 94
JO - Medical Image Analysis
JF - Medical Image Analysis
M1 - 103134
ER -