Xiao Chen1, Michael Salerno2, 3, Craig H. Meyer1, Frederick H. Epstein1
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States; 2Medicine, University of Virginia, Charlottesville, VA, United States; 3Radiology, University of Virginia, Charlottesville, VA, United States
Several accelerated imaging techniques utilizing k-t undersampling have been proposed to model dynamic CMR behavior with a few spatiotemporal basis functions to reconstruct images. These algorithms are sensitive to respiratory motion and perform poorly when both signal intensity and object position and shape change during image acquisition. We propose a novel method that divides the images into blocks and tracks the blocks motions to exploit increased sparsity (Block LOw-rank Sparsity with Motion guidance). The simplified dynamics in the smaller, motion-compensated blocks can be better described by a limited number of basis functions, making the method insensitive to complex dynamics.