We propose a Conditional Wasserstein Generative Adversarial Network (cWGAN), trained with a novel Adaptive Loss Balancing (ALB) technique that stabilizes the training and minimizes the presence of artifacts, while maintaining a high-quality reconstruction with more natural appearance (compared to non-GAN techniques). Multi-channel 2D brain data with fourfold undersampling were used as inputs, and the corresponding fully-sampled reconstructed images as references for training. The algorithm produced higher-quality images than state-of-the-art deep learning-based models in terms of perceptual quality and realistic appearance.
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