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Abstract #2603

Scalable and Accurate Variance Estimation (SAVE) for Joint Bayesian Compressed Sensing

Stephen F. Cauley1, Yuanzhe Xi2, Berkin Bilgic3, Kawin Setsompop4, 5, Jianlin Xia2, Elfar Adalsteinsson, 46, V. Ragu Balakrishnan7, Lawrence L. Wald4, 8

1A.A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; 2Department of Mathematics, Purdue University, West Lafayette, IN, United States; 3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States; 4A.A. Martinos Center for Biomedical Imaging, Dept. of Radiology, MGH, Charlestown, MA, United States; 5Harvard Medical School, Boston, MA, United States; 6Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, United States; 7School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States; 8Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States

The far reaching adoption of compressed sensing for clinic MRI hinges on the ability to accurately produce images in a reasonable time-frame. Multiple contrast studies have been successfully combined with joint Bayesian reconstruction for improved image quality. However, current techniques have prohibitive computational requirements. We consider a joint Bayesian approach that approximates point spread functions to exploit sparse matrix methods. We leverage hierarchical matrix analysis and compression schemes to facilitate scalable and accurate CS reconstruction. Our approach is over 100x faster than other multiple contrast approaches while still improving image accuracy by over 35% compared to single image CS techniques.

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Keywords

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