Abstract #0435

Direct and Fast Learning of Fiber Orientation Distribution Function for Tractography

Ting Gong¹, Hongjian He¹, Zhichao Lin², Zhiwei Li², Qiqi Tong¹, 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

Multi-shell, multi-tissue, constrained spherical deconvolution is an appealing method for the reconstruction of fiber orientation distribution function (fODF), which is of great importance for solving complex fiber configurations to achieve reliable tractography. However, many diffusion measurements and multiple reconstruction steps are required. In this study, the deep neural network were employed to form a multi-output regression problem for establishing a fast and direct estimation of fODF. The proposed method offers a new streamlined reconstruction procedure which exhibits great potential for accelerating the reconstruction of fODF with whole-brain coverage, with satisfactory accuracy in two minutes.

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