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Abstract #2626

Improved Compressed Sensing Using Parallel Imaging: TGRAPPA-PRISM for Cardiac Cine MRI

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.

Keywords

acceleration acquisition actual additional adjacent alone amount applied arrows artifacts assess auto axis background better biomedical blue blurring boxes calibration cardiac channel chosen cine coil coils combination complete component compressed compromise correlation datasets decimated decomposed decomposition decreased degradation diastole distribution dynamic eddy encoding exploits extract feeding fidelity fold formulated frame frames full fully function gray great greatly heart helpful horizontal implementation improve improved improves incoherent inserting intensity investigated kernels limitations local located long matrix merged minimization missing model motion noise norm note novel nuclear orientations outer paired pairing parallel partial pattern patterns physics pixels prior prism problem problems promote proposed prospective proves quality radiological randomly rank rather reconstructed reconstruction reconstructs reduce reduces reducing remaining remains represents require resolution retrospectively routinely sampled sampling sensing sets sharpness short singular slice slices sliding sorted space sparsity spatial speed stage starting stationary step structure superior surface systole temporal term transform vector vertical wavelet whereas window