Da Wang1, Stanislas Rapacchi, Hao Gao2, Peng Hu3
1Biomedical Physics/Radiological Sci, UCLA, Los Angeles, CA, United States; 2Emory University, Atlanta, GA, United States; 3University of California Los Angeles, Los Angeles, CA, United States
A novel compressed sensing MRI reconstruction method has been proposed for dynamic MRI using Prior Rank, Intensity and Sparsity Model (PRISM). By using a low rank decomposition, PRISM can extract the stationary background component from dynamic images to further promote sparsity of the motion component for L1 norm minimization. The combination of parallel MRI methods with compressed sensing methods has shown great potential to improve the reconstructed image quality and acceleration rate. We propose to further improve PRISM compressed sensing algorithm by using TGRAPPA to fill in additional data lines in the k-space before feeding to the PRISM algorithm.