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Abstract #3112

Accelerating 7T susceptibility-weighted imaging with complex-valued convolutional neural network

Caohui Duan1, Xiangbing Bian1, Kun Cheng1, Xiaoyu Wang1, Jinhao Lyu1, Xueyang Wang1, 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: Image Reconstruction, Machine Learning/Artificial IntelligenceUltra-high field 7T susceptibility-weighted imaging (SWI) has shown great potential in visualizing and evaluating a broad range of pathology, but suffers from long acquisition times. In this study, a complex-valued convolutional neural network (ComplexNet) model was proposed to reconstruct highly accelerated 7T SWI data. The average reconstruction time of ComplexNet was 0.56 seconds per slice (45.16 seconds per participant). Meanwhile, ComplexNet can provide high-quality 7T SWI for visualizing subtle pathology, including cerebral microbleeds, asymmetric deep medullary veins, and swallow tail sign.

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