  Compressed Sensing MRI (CS-MRI) has provided theoretical foundations upon
which the time-consuming MRI acquisition process can be accelerated. However,
it primarily relies on iterative numerical solvers which still hinders their
adaptation in time-critical applications. In addition, recent advances in deep
neural networks have shown their potential in computer vision and image
processing, but their adaptation to MRI reconstruction is still in an early
stage. In this paper, we propose a novel deep learning-based generative
adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction. The
proposed model is a variant of fully-residual convolutional autoencoder and
generative adversarial networks (GANs), specifically designed for CS-MRI
formulation; it employs deeper generator and discriminator networks with cyclic
data consistency loss for faithful interpolation in the given under-sampled
k-space data. In addition, our solution leverages a chained network to further
enhance the reconstruction quality. RefineGAN is fast and accurate -- the
reconstruction process is extremely rapid, as low as tens of milliseconds for
reconstruction of a 256x256 image, because it is one-way deployment on a
feed-forward network, and the image quality is superior even for extremely low
sampling rate (as low as 10%) due to the data-driven nature of the method. We
demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a
large margin in terms of both running time and image quality via evaluation
using several open-source MRI databases.
