Compressed Sensing using Prior Rank , Intensity and Sparsity Model ( PRISM ) : Applications in Cardiac Cine MRI

Introduction: Compressed sensing (CS) has recently been applied to MR image reconstruction to shorten data acquisition time. In this work, we propose a novel CS method for dynamic MRI applications using Prior Rank, Intensity and Sparsity Model (PRISM) 3,4 and evaluate this technique for cardiac cine MRI. Theory: A dynamic MR image is represented by a matrix X , where all the pixel values for a given temporal frame are in a column vector. X can be decomposed into a background component X L that does not change over time, and a changing residual X S , hence X = X L + X S . Let Y be the incoherently