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Abstract #2461

Spherical harmonics coefficients estimation using deep neural network

Zhangxuan Hu1, Zhe Zhang2, Yuhui Xiong1, Chun Yuan1,3, and Hua Guo1

1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, 3Vascular Imaging Laboratory, Department of Radiology, University of Washington, Seattle, WA, United States

Diffusion-weighted imaging can be used to detect orientations of fibers to study human brain connectivity using tractography techniques. Spherical deconvolution based techniques have been widely used for the estimation of fiber orientation distribution (FOD), in which FODs are represented using spherical harmonics coefficients. However, high quality FOD estimation still requires large number of measurements. In this study, a deep neural network based method is proposed to estimate high quality FODs using highly q-space undersampled measurements thus to improve the acquisition efficiency.

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