  The slow acquisition speed of magnetic resonance imaging (MRI) has led to the
development of two complementary methods: acquiring multiple views of the
anatomy simultaneously (parallel imaging) and acquiring fewer samples than
necessary for traditional signal processing methods (compressed sensing). While
the combination of these methods has the potential to allow much faster scan
times, reconstruction from such undersampled multi-coil data has remained an
open problem. In this paper, we present a new approach to this problem that
extends previously proposed variational methods by learning fully end-to-end.
Our method obtains new state-of-the-art results on the fastMRI dataset for both
brain and knee MRIs.
