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Abstract #4882

Improved Compressed Sensing Reconstruction for Equidistant K-Space by Sampling Decomposition and Its Application in Parallel MR Imaging

Jun Miao1,2, Weihong Guo3, David L. Wilson1,4

1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States; 2Siemens Corporate Research, Princeton, NJ, United States; 3Mathematics, Case Western Reserve University, Cleveland, OH, United States; 4Radiology, University Hospitals of Cleveland


Incoherent sampling requirement is a bottleneck for application of compressed sensing (CS) in parallel MR imaging. Thus, a direct plug-in of CS to parallel imaging, especially in the case of equidistant k-space sampling, is not feasible. We propose a simple method to eliminate this problem by sampling decomposition and illustrate the idea using GRAPPA reconstruction. Significant improvement in image quality can be achieved with even less k-space acquisition.

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