Abstract #5029

Synthesized 7T MRI from 3T MRI using generative adversarial network: validation in clinical brain imaging

Caohui Duan¹, Xiangbing Bian¹, Kun Cheng¹, Jinhao Lyu¹, Yongqin Xiong¹, Jianxun Qu², Xin Zhou³, and Xin Lou¹

¹Department of Radiology, Chinese PLA General Hospital, Beijing, China, ²MR Collaboration, Siemens Healthineers Ltd., Beijing, China, ³Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences‒Wuhan National Laboratory for Optoelectronics, Wuhan, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, BrainUltra-high field 7T MRI provides exceptional tissue contrast and anatomical details but is often cost-prohibitive and not widely accessible in clinics. A generative adversarial network (SynGAN) was developed to generate synthetic 7T images from the widely used 3T images. The synthetic 7T images achieved improved tissue contrast and anatomical details compared to the 3T images. Meanwhile, the synthetic 7T images showed comparable diagnostic performance to the authentic 7T images for visualizing a wide range of pathology, including cerebral infarction, demyelination, and brain tumor.

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