Abstract #2461

Spherical harmonics coefficients estimation using deep neural network

Zhangxuan Hu¹, Zhe Zhang², Yuhui Xiong¹, Chun Yuan^1,3, 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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