  Compressed sensing (CS) leverages the sparsity prior to provide the
foundation for fast magnetic resonance imaging (fastMRI). However, iterative
solvers for ill-posed problems hinder their adaption to time-critical
applications. Moreover, such a prior can be neither rich to capture complicated
anatomical structures nor applicable to meet the demand of high-fidelity
reconstructions in modern MRI. Inspired by the state-of-the-art methods in
image generation, we propose a novel attention-based deep learning framework to
provide high-quality MRI reconstruction. We incorporate large-field contextual
feature integration and attention selection in a generative adversarial network
(GAN) framework. We demonstrate that the proposed model can produce superior
results compared to other deep learning-based methods in terms of image
quality, and relevance to the MRI reconstruction in an extremely low sampling
rate diet.
