  Magnetic resonance imaging (MRI) is one of the best medical imaging
modalities as it offers excellent spatial resolution and soft-tissue contrast.
But, the usage of MRI is limited by its slow acquisition time, which makes it
expensive and causes patient discomfort. In order to accelerate the
acquisition, multiple deep learning networks have been proposed. Recently,
Generative Adversarial Networks (GANs) have shown promising results in MRI
reconstruction. The drawback with the proposed GAN based methods is it does not
incorporate the prior information about the end goal which could help in better
reconstruction. For instance, in the case of cardiac MRI, the physician would
be interested in the heart region which is of diagnostic relevance while
excluding the peripheral regions. In this work, we show that incorporating
prior information about a region of interest in the model would offer better
performance. Thereby, we propose a novel GAN based architecture, Reconstruction
Global-Local GAN (Recon-GLGAN) for MRI reconstruction. The proposed model
contains a generator and a context discriminator which incorporates global and
local contextual information from images. Our model offers significant
performance improvement over the baseline models. Our experiments show that the
concept of a context discriminator can be extended to existing GAN based
reconstruction models to offer better performance. We also demonstrate that the
reconstructions from the proposed method give segmentation results similar to
fully sampled images.

Image and Video Processing;Computer Vision and Pattern Recognition