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Abstract #1955

Memory-Efficient Learning for High-Dimensional MR Reconstruction

Ke Wang1, Michael Kellman2, Christopher M. Sandino3, Kevin Zhang1, Shreyas S. Vasanawala4, Jonathan I. Tamir5, Stella X. Yu6, and Michael Lustig1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2Pharmaceutical Chemistry, University of California, San Francisco, Berkeley, CA, United States, 3Electrical Engineering, Stanford University, Stanford, CA, United States, 4Radiology, Stanford University, Stanford, CA, United States, 5Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States, 6International Computer Science Institute, University of California, Berkeley, Berkeley, CA, United States

High-dimensional Deep learning (DL) reconstructions (e.g. 3D, 2D+time, 3D+time) can exploit multi-dimensional information and achieve improved results over lower dimensional ones. However, the size of the network and its depth for these large-scale reconstructions are currently limited by GPU memory. Here, we use a memory-efficient learning (MEL) framework, which favorably trades off storage with minimal increased computation and enables deeper high-dimensional DL reconstruction on a single GPU. We demonstrate improved image quality with learned high-dimensional reconstruction enabled by MEL for in-vivo 3D MRI and 2D cardiac cine imaging applications. MEL uses much less GPU memory while minimally increasing training time.

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