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

Álvaro Planchuelo-Gómez, Maxime Descoteaux, Hugo Larochelle, Jana Hutter, Derek K. Jones, Chantal M.W. Tax*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Article number103134
JournalMedical Image Analysis
Volume94
DOIs
Publication statusPublished - May 2024

Keywords

  • Brain
  • Diffusion-relaxation
  • Machine learning
  • Quantitative MRI

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