This is the public, feature-limited version of the conference webpage. After Registration and login please visit the full version.

Experimental design for MRI by greedy policy search

Tim Bakker, Herke van Hoof, Max Welling

Spotlight presentation: Orals & Spotlights Track 15: COVID/Applications/Composition
on 2020-12-09T08:20:00-08:00 - 2020-12-09T08:30:00-08:00
Poster Session 4 (more posters)
on 2020-12-09T09:00:00-08:00 - 2020-12-09T11:00:00-08:00
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.

Preview Video and Chat

To see video, interact with the author and ask questions please use registration and login.