University of Minnesota
Tuesday, February 1, 2020
1:30PM – 2:30PM – HEC 101
Lengthy data acquisition times remain a bottleneck in magnetic resonance imaging (MRI). Thus accelerated imaging methodologies have received great interest over the last three decades. Recently, deep learning (DL) techniques have gathered interest as a means to improve reconstruction quality for accelerated MRI. DL-based reconstruction techniques can be broadly divided into two categories, data-driven and physics-driven. The former methods learn a mapping from aliased images/k-space to artifact-free images/k-space. In the physics-driven approaches, the knowledge of the forward encoding operator is taken into account in solving an inverse problem.
In this talk, we will concentrate on physics-driven approaches. We will specifically focus on novel self-supervised training strategies for such reconstruction algorithms when ground-truth data is not available, which is a common problem in MRI. We will also discuss how we can use insights from optimization theory to design better neural network structures.
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