Keywords: Diffusion Acquisition, Diffusion Acquisition
Motivation: To maximise scanning time efficiency in diffusion MRI and quantitative MRI.
Goal(s): To demonstrate a new deep learning approach for experimental design of acquisition protocols for MRI parameter mapping.
Approach: We utilise TADRED (TAsk-DRiven Experiment Design) to simultaneously train two networks: one to optimise the acquisition protocol and one to optimise a neural network for parameter estimation. We demonstrate on three diffusion MRI applications – NODDI, VERDICT, and ADC mapping – and T1 inversion recovery. Code is available at https://github.com/sbb-gh/ED_MRI.
Results: TADRED demonstrates superior or comparable performance in estimating model parameters compared to the Cramer-Rao lower bound (CRLB) baseline across all experiments.
Impact: TADRED enables shorter, more efficient diffusion and quantitative MRI protocols. This can reduce scan time and costs, reduce motion artifacts, and enhance patient comfort.
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