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Background CMR perfusion images demonstrate complex dynamic behavior resulting from signal intensity changes during the first pass of gadolinium as well as motion from imperfect breathholding and gating. Reconstruction algorithms such as kt-PCA and compressed sensing (CS) techniques such as kt-Sparsity and Low-Rankness (kt-SLR) assume that a few spatiotemporal basis functions can model this intricate behavior [1,2]. However, these techniques are sensitive to motion, as the basis functions do not accurately describe the complete dynamics of the entire image set. We propose a novel method that utilizes regional sparsity by dividing the images into regions. With this approach, the simplified dynamics of smaller regions can be better described by a limited number of basis functions. This method was tested on in vivo images and simulated data, and the results were compared to kt-SLR [1], a CS method that uses global sparsity.

Accelerated first-pass perfusion CMR using compressed sensing with regional spatiotemporal sparsity