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Abstract #1783

Optical Flow-based Data Augmentation and its Application in Deep Learning Super Resolution

Yu He1, Fangfang Tang1, Jin Jin1,2, and Feng Liu1
1School of Information Technology and Electrical Engineering, the University of Queensland, Brisbane, Australia, 2Research and Development MR, Siemens Healthcare, Brisbane, Australia

Deep learning (DL) methods have been a hot topic in MRI reconstruction, such as super-resolution. However, DL usually requires a substantial amount of training data, which may not always be accessible because of limited clinical cases, privacy limitation, the cross-vendor, and cross-scanner variation, etc. In this work, we propose an affine transformation data augmentation method to increase training data for MRI super-resolution. Comprehensive experiments were performed on real T2 brain images to validate the proposed method.

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