title: A BLIND COMPRESSIVE SENSING FRAMEWORK FOR ACCELERATED DYNAMIC MRI.
abstract: We propose a novel blind compressive sensing (BCS) frame work to recover dynamic images from under-sampled measurements. This scheme models the the dynamic signal as asparselinear combination of temporal basis functions, chosen from alarge dictionary. The dictionary and the sparse coefficients are simultaneously estimated from the under-sampled measurements. Since the number of degrees of freedom of this model is much smaller than that of current low-rank methods, this scheme is expected to provide improved reconstructions for datasets with considerable inter-frame motion. We develop an efficient majorize-minimize algorithm to solve for the dynamic images. We use a continuation strategy to minimize the convergence of the algorithm to local minima. Numerical comparisons of the BCS scheme with low-rank methods demonstrate the significant improvement in performance in the presence of motion.
authors.affiliation: Biomedical Engineering, The University of Iowa, Iowa city, IA USA.
authors.forename: Sajan
authors.initials: S
authors.lastname: Goud Lingala
authors.affiliation: Electrical and Computer Engineering, The University of Iowa, Iowa city, IA USA.
authors.forename: Mathews
authors.initials: M
authors.lastname: Jacob