In this work, we investigated whether deep learning based super resolution (SR) network trained from a brain dataset can generalize well to other applications (i.e. knee and abdominal imaging). Our preliminary results imply that 1) the perceptual loss function can improve the generalization performance of SR network across different applications; 2) the multi-scale network architecture can better stabilize the SR results particularly for training dataset with lower quality. In addition, the SR improvement from increased data diversity can be saturated, indicating that a single trained SR network might be feasible for universal MR image resolution improvement.
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