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Abstract #4350

7T MRI prediction from 3T MRI via a high frequency generative adversarial network

Yuxiang Dai1, Wei Tang1, Ying-Hua Chu2, Chengyan Wang3, and He Wang1,3
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Siemens Healthineers Ltd., Shanghai, China, 3Human Phenome Institute, Fudan University, Shanghai, China


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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