Experimental design for MRI by greedy policy search

• URL: http://arxiv.org/abs/2010.16262v2
• Date: Tue, 15 Dec 2020 11:12:46 GMT
• Title: Experimental design for MRI by greedy policy search
• Authors: Tim Bakker, Herke van Hoof, Max Welling
• Abstract summary: We propose to learn experimental design strategies for accelerated MRI with policy methods. We show that a simple greedy approximation of the objective leads to solutions nearly on-par with the more general non-greedy approach. We empirically show that this adaptivity is key to improving subsampling designs.
• Score: 88.02271826127219
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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