Jiexun Xu1,
Nicolas Pannetier2, Ashish Raj3
1Department
of Computer Science, Cornell University, Ithaca, NY, United States; 2Department
of Radiology and Department of Veterans Affairs Medical Center, University of
California at San Francisco, San Francisco, CA, United States; 3Department
of Radiology, Weill Medical College of Cornell University, New York, NY,
United States
Among recent parallel imaging techniques, a Bayesian method that uses Cartesian under-sampling and sophisticated edge-preserving priors (EPP) have demonstrated its success in clinical applications. Recent compressive sensing related methods have proposed random under-sampling schemes that makes denoising and removing aliasing artifacts much easier. In this work we combine the strengths of both methods and propose a novel algorithm to solve the resulting problem, and demonstrate that our algorithm out performs popular existing methods.