  Recent accelerated MRI reconstruction models have used Deep Neural Networks
(DNNs) to reconstruct relatively high-quality images from highly undersampled
k-space data, enabling much faster MRI scanning. However, these techniques
sometimes struggle to reconstruct sharp images that preserve fine detail while
maintaining a natural appearance. In this work, we enhance the image quality by
using a Conditional Wasserstein Generative Adversarial Network combined with a
novel Adaptive Gradient Balancing (AGB) technique that automates the process of
combining the adversarial and pixel-wise terms and streamlines hyperparameter
tuning. In addition, we introduce a Densely Connected Iterative Network, which
is an undersampled MRI reconstruction network that utilizes dense connections.
In MRI, our method minimizes artifacts, while maintaining a high-quality
reconstruction that produces sharper images than other techniques. To
demonstrate the general nature of our method, it is further evaluated on a
battery of image-to-image translation experiments, demonstrating an ability to
recover from sub-optimal weighting in multi-term adversarial training.
