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Abstract #3432

Compressed Sensing Parallel Imaging Without Calibration

Nicholas Dwork1, Corey A. Baron2, Ethan M. I. Johnson3, Daniel O'Connor4, Adam B. Kerr5, Peder E. Z. Larson1, Jeremy Gordon1, and John M. Pauly6
1Radiology and Biomedical Imaging, University of California in San Francisco, San Francisco, CA, United States, 2Medical Biophysics, Western University, London, ON, Canada, 3Biomedical Engineering, Northwestern University, Evanston, IL, United States, 4Mathematics and Statistics, University of San Francisco, San Francisco, CA, United States, 5Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, United States, 6Electrical Engineering, Stanford University, Stanford, CA, United States

In this paper, the reconstructed image is the result of a compressed sensing optimization problem that includes constraints based on fundamental physics. The problem is solved using an alternating minimization approach: two convex optimization problems are alternately solved, one with the Fast Iterative Shrinkage Threshold Algorithm (FISTA) and the other with the Primal-Dual Hybrid Gradient method. Results show improved detail when compared to conventional SENSE results.

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