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Abstract #0066

Coil Sketching for fast and memory-efficient iterative reconstruction

Julio A. Oscanoa1,2, Frank Ong3, Zhitao Li2,3, Christopher M. Sandino3, Daniel B. Ennis2,4, Mert Pilanci3, and Shreyas S. Vasanawala2
1Department of Bioengineering, Stanford University, Stanford, CA, United States, 2Department of Radiology, Stanford University, Stanford, CA, United States, 3Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 4Cardiovascular Institute, Stanford, CA, United States

Parallel imaging and compressed sensing reconstruction of large datasets has a high computational cost, especially for 3D non-Cartesian acquisitions. This work is motivated by the success of iterative Hessian sketching methods in machine learning. Herein, we develop Coil Sketching to lower computational burden by effectively reducing the number of coils actively used during iterative reconstruction. Tested with 2D radial and 3D cones acquisitions, our method yields considerably faster reconstructions (around 2x) with virtually no penalty on reconstruction accuracy.

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