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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