  Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that
provides excellent soft-tissue contrast without the use of ionizing radiation.
Compared to other clinical imaging modalities (e.g., CT or ultrasound),
however, the data acquisition process for MRI is inherently slow, which
motivates undersampling and thus drives the need for accurate, efficient
reconstruction methods from undersampled datasets. In this article, we describe
the use of "plug-and-play" (PnP) algorithms for MRI image recovery. We first
describe the linearly approximated inverse problem encountered in MRI. Then we
review several PnP methods, where the unifying commonality is to iteratively
call a denoising subroutine as one step of a larger optimization-inspired
algorithm. Next, we describe how the result of the PnP method can be
interpreted as a solution to an equilibrium equation, allowing convergence
analysis from the equilibrium perspective. Finally, we present illustrative
examples of PnP methods applied to MRI image recovery.
