Abstract #0947

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

Julio A Oscanoa¹, Batu Ozturkler², Siddharth S Iyer^3,4, Zhitao Li^2,4, 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

Synopsis

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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