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Abstract #4881

A New Combination of Compressed Sensing and Data Driven Parallel Imaging

Kevin King1, Dan Xu1, Anja CS Brau2, Peng Lai2, Philip J. Beatty2, Luca Marinelli3

1Global Applied Science Lab, GE Healthcare, Waukesha, WI, United States; 2Global Applied Science Lab, GE Healthcare, Menlo Park, CA, United States; 3Global Research Center, General Electric, Niskayuna, NY, United States

Compressed sensing and data driven parallel imaging can be combined in a serial approach in which randomly undersampled data are reconstructed onto a uniformly undersampled k-space grid using compressed sensing. Parallel imaging uses this uniformly undersampled data plus the auto-calibration data to create a fully sampled k-space grid. The serial approach allows the acceleration to be split between compressed sensing and parallel imaging. Each method solves a problem with better conditioning than if the full acceleration were used. Any data driven parallel imaging method, such as GRAPPA, ARC or SPIRIT can be used without modification using this approach.