  Recent sparse 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 technique that stabilizes the training and
minimizes the degree of artifacts, while maintaining a high-quality
reconstruction that produces sharper images than other techniques.
