  Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand
and scan time directly depends on the number of acquired k-space samples.
Conventional MRI reconstruction methods for fast MRI acquisition mostly relied
on different regularizers which represent analytical models of sparsity.
However, recent data-driven methods based on deep learning has resulted in
promising improvements in image reconstruction algorithms. In this paper, we
propose a deep plug-and-play prior framework for parallel MRI reconstruction
problems which utilize a deep neural network (DNN) as an advanced denoiser
within an iterative method. This, in turn, enables rapid acquisition of MR
images with improved image quality. The proposed method was compared with the
reconstructions using the clinical gold standard GRAPPA method. Our results
with undersampled data demonstrate that our method can deliver considerably
higher quality images at high acceleration factors in comparison to clinical
gold standard method for MRI reconstructions. Our proposed reconstruction
enables an increase in acceleration factor, and a reduction in acquisition time
while maintaining high image quality.
