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Abstract #4731

High-quality reconstruction of diffusion tensor based on deep learning and multi-slice information sharing

Zunquan Chen1, Jiechao Wang1, Zhigang Wu2, Jianfeng Bao3, Jingliang Cheng3, Congbo Cai1, and Shuhui Cai1
1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China, 2MSC Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China, 3Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhenzhou, China


Diffusion tensor imaging (DTI) requires several diffusion-weighted image (DWI) acquisitions, leading to a long scan time. In this study, a deep-learning model based on multi-slice information sharing was implemented to obtain ultrafast DTI. This method uses the similarity of DWIs among neighboring slices to minimize the requirement of the number of diffusion-encoding directions. The results indicate that the proposed method can reconstruct high-quality diffusion and DTI-derived maps, exceptionally robust to noise in fractional anisotropy mapping even when only 3-direction DWIs.

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