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Abstract #1777

Evaluation of Super Resolution Network for Universal Resolution Improvement

Zechen Zhou1, Yi Wang2, Johannes M. Peeters3, Peter Börnert4, Chun Yuan5, and Marcel Breeuwer3
1Philips Research North America, Cambridge, MA, United States, 2MR Clinical Science, Philips Healthcare North America, Gainesville, FL, United States, 3MR Clinical Science, Philips Healthcare, Best, Netherlands, 4Philips Research Hamburg, Hamburg, Germany, 5Vascular Imaging Lab, University of Washington, Seattle, WA, United States

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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