  Cardiac magnetic resonance imaging (CMR) is a noninvasive imaging modality
that provides a comprehensive evaluation of the cardiovascular system. The
clinical utility of CMR is hampered by long acquisition times, however. In this
work, we propose and validate a plug-and-play (PnP) method for CMR
reconstruction from undersampled multi-coil data. To fully exploit the rich
image structure inherent in CMR, we pair the PnP framework with a deep learning
(DL)-based denoiser that is trained using spatiotemporal patches from
high-quality, breath-held cardiac cine images. The resulting "PnP-DL" method
iterates over data consistency and denoising subroutines. We compare the
reconstruction performance of PnP-DL to that of compressed sensing (CS) using
eight breath-held and ten real-time (RT) free-breathing cardiac cine datasets.
We find that, for breath-held datasets, PnP-DL offers more than one dB
advantage over commonly used CS methods. For RT free-breathing datasets, where
ground truth is not available, PnP-DL receives higher scores in qualitative
evaluation. The results highlight the potential of PnP-DL to accelerate RT CMR.
