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We introduce a model-based reconstruction framework with deep learned (DL)and smoothness regularization on manifolds (STORM) priors to recover freebreathing and ungated (FBU) cardiac MRI from highly undersampled measurements.The DL priors enable us to exploit the local correlations, while the STORMprior enables us to make use of the extensive non-local similarities that aresubject dependent. We introduce a novel model-based formulation that allows theseamless integration of deep learning methods with available prior information,which current deep learning algorithms are not capable of. The experimentalresults demonstrate the preliminary potential of this work in accelerating FBUcardiac MRI.

Model-based free-breathing cardiac MRI reconstruction using deep learned \& STORM priors: MoDL-STORM