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