Experimental design for MRI by greedy policy search

MRI experimental design

Abstract

In today’s clinical practice, magnetic resonance imaging (MRI) is routinely accelerated through subsampling of the associated Fourier domain. Currently, the construction of these subsampling strategies - known as experimental design - relies primarily on heuristics. We propose to learn experimental design strategies for accelerated MRI with policy gradient methods. Unexpectedly, our experiments show that a simple greedy approximation of the objective leads to solutions nearly on-par with the more general non-greedy approach. We offer a partial explanation for this phenomenon rooted in greater variance in the non-greedy objective’s gradient estimates, and experimentally verify that this variance hampers non-greedy models in adapting their policies to individual MR images. We empirically show that this adaptivity is key to improving subsampling designs.

Publication
In Advances in Neural Information Processing Systems 33 (NeurIPS, 2020)
Tim Bakker
Tim Bakker
PhD researcher in Machine Learning

My research interests include active learning/sensing, reinforcement learning, AI4Science and everything Bayesian.

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