Keywords: Machine Learning/Artificial Intelligence, Brain, Super resolutionIn many medical applications, high resolution images are required to facilitate early and accurate diagnosis. In this paper, we investigate the potential of data augmentation using simulated brain magnetic resonance (MR) images for training a deep-learning (DL) super resolution model that can generalize to brain data from different publicly available sources. Our qualitative visual evaluation results suggest that data augmentation with simulated images can improve the robustness and generalization of the model and decrease the artifacts of the super-resolved images.
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