  Magnetic resonance imaging (MRI) plays an important role in modern medical
diagnostic but suffers from prolonged scan time. Current deep learning methods
for undersampled MRI reconstruction exhibit good performance in image
de-aliasing which can be tailored to the specific k-space undersampling
scenario. But it is very troublesome to configure different deep networks when
the sampling setting changes. In this work, we propose a deep plug-and-play
method for undersampled MRI reconstruction, which effectively adapts to
different sampling settings. Specifically, the image de-aliasing prior is first
learned by a deep denoiser trained to remove general white Gaussian noise from
synthetic data. Then the learned deep denoiser is plugged into an iterative
algorithm for image reconstruction. Results on in vivo data demonstrate that
the proposed method provides nice and robust accelerated image reconstruction
performance under different undersampling patterns and sampling rates, both
visually and quantitatively.
