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 Zhong^1,4

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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