Abstract #0066

Coil Sketching for fast and memory-efficient iterative reconstruction

Julio A. Oscanoa^1,2, Frank Ong³, Zhitao Li^2,3, Christopher M. Sandino³, Daniel B. Ennis^2,4, 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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