Comparison of data-driven and physics-informed learning approaches for optimising multi-contrast MRI acquisition protocols
|Comparison of data-driven and physics-informed learning approaches for optimising multi-contrast MRI acquisition protocols
|Year of Conference
|Planchuelo-Gómez, Á., M. Descoteaux, S. Aja-Fernández, J. Hutter, D. K. Jones, and C. M. W. Tax
|2023 ISMRM & ISMRT Annual Meeting & Exhibition
Multi-contrast MRI is used to assess the biological properties of tissues, but excessively long times are required to acquire high-quality datasets. To reduce acquisition time, physics-informed Machine Learning approaches were developed to select the optimal subset of measurements, decreasing the number of volumes by approximately 63%, and predict the MRI signal and quantitative maps. These selection methods were compared to a full data-driven and two manual strategies. Synthetic and real 5D-Diffusion-T1-T2* data from five healthy participants were used. Feature selection via a combination of Machine Learning and physics modelling provides accurate estimation of quantitative parameters and prediction of MRI signal.