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Abstract #0947

Coil-sketched unrolled networks for computationally-efficient deep MRI reconstruction

Julio A Oscanoa1, Batu Ozturkler2, Siddharth S Iyer3,4, Zhitao Li2,4, Christopher M Sandino2, Mert Pilanci2, Daniel B Ennis4, and Shreyas S Vasanawala4
1Department of Bioengineering, Stanford University, Stanford, CA, United States, 2Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Boston, MA, United States, 4Department of Radiology, Stanford University, Stanford, CA, United States


Deep unrolled networks can outperform conventional compressed sensing reconstruction. However, training unrolled networks has intensive memory and computational requirements, and is limited by GPU-memory constraints. We propose to use our previously developed “coil-sketching” algorithm to lower the computational burden of the data consistency step. Our method reduced memory usage and training time by 18% and 15% respectively with virtually no penalty on reconstruction accuracy when compared to a state-of-the-art unrolled network.

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