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Abstract #1653

Efficient Reconstruction of Diffusion Kurtosis Imaging Based on a Hierarchical Convolutional Neural Network

Ting Gong1, Hongjian He1, Zhiwei Li2, Zhichao Lin2, Qiqi Tong1, Chen Li1, Yi Sun3, Feng Yu2, and Jianhui Zhong1,4

1Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China, 2Department of Instrument Science & Technology, Zhejiang University, Hangzhou, China, 3MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China, 4Department of Imaging Sciences, University of Rochester, Rochester, NY, United States

Diffusion kurtosis imaging (DKI) captures more complex microstructural properties than the widely used diffusion tensor imaging (DTI) but requires a longer acquisition time. To accelerate its acquisition, and thus facilitate its practical clinical use, a hierarchical convolution neural network (H_CNN) reconstruction method was proposed. The results showed that the H_CNN method provides efficient reconstruction of all eight DTI and DKI measures using as few as nine DWIs, with improved robustness against noise and the retention of fine structures, compared to artificial neural network-based methods. The H_CNN method potentially enables DKI clinical applications with an acquisition time of one minute.

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