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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