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Abstract #0377

Accelerating SENSE Using Distributed Compressed Sensing

Dong Liang1, Kevin f. King2, Bo Liu3, Leslie Ying1

1Dept. of Electrical Engineering and Computer Science, Univ. of Wisconsin-Milwaukee, Milwaukee, WI, USA; 2Global Applied Science Lab, GE Healthcare, Waukesha, WI, USA; 3MR Engineering, GE Healthcare, Waukesha, WI, USA

Most existing methods apply compressed sensing (CS) to parallel MRI as a regularized SENSE reconstruction, where the regularization function is the L1 norm of the sparse representation. However, the CS conditions such as incoherence are not necessarily satisfied. To address the issue, a method is proposed which first reconstructs a set of aliased images from all channels simultaneously using distributed CS (DCS), and then the final image using Cartesian SENSE. The results on a set of eight-channel data show that the proposed method is able to achieve a higher reduction factor than the existing methods.