  Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical
applications in order to reduce the scanning cost and improve the patient
experience. This can also potentially increase the image quality by reducing
the motion artefacts and contrast washout. However, once an image field of view
and the desired resolution are chosen, the minimum scanning time is normally
determined by the requirement of acquiring sufficient raw data to meet the
Nyquist-Shannon sampling criteria. Compressive Sensing (CS) theory has been
perfectly matched to the MRI scanning sequence design with much less required
raw data for the image reconstruction. Inspired by recent advances in deep
learning for solving various inverse problems, we propose a conditional
Generative Adversarial Networks-based deep learning framework for de-aliasing
and reconstructing MRI images from highly undersampled data with great promise
to accelerate the data acquisition process. By coupling an innovative content
loss with the adversarial loss our de-aliasing results are more realistic.
Furthermore, we propose a refinement learning procedure for training the
generator network, which can stabilise the training with fast convergence and
less parameter tuning. We demonstrate that the proposed framework outperforms
state-of-the-art CS-MRI methods, in terms of reconstruction error and
perceptual image quality. In addition, our method can reconstruct each image in
0.22ms--0.37ms, which is promising for real-time applications.
