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Abstract #0474

Leveraging conditional GANs with adaptive loss balancing for MRI sparse reconstruction.

Itzik Malkiel1, Sangtae ahn2, Valentina Taviani3, Anne Menini3, Zachary Slavens4, and Christopher Hardy2

1GE Global Research, Herzliya, Israel, 2GE Global Research, Niskayuna, NY, United States, 3GE Healthcare, Menlo Park, CA, United States, 4GE Healthcare, Waukesha, WI, United States

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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