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 Wang^1,3

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

Synopsis

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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