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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