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

Synopsis

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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