Existing methods often fail to capture sufficient anatomical details which lead to unsatisfactory 7T MRI predictions, especially for 3D prediction. We proposed a 3D prediction model which introduces high frequency information learned from 7T images into generative adversarial network. Specifically, the prediction model can effectively produce 7T-like images with sharper edges, better contrast and higher SNR than 3T images.
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