Inspired by variational networks and adversarial training, we introduce variational adversarial networks for accelerated MR image reconstruction to overcome typical limitations of using simple image quality measures as loss functions for training. While simple loss functions, such as mean-squared-error and structural similarity index, result in low resolution and blurry images, we show that the proposed variational adversarial network leads to sharper images and preserves fine details for clinical low and high SNR patient data.
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