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Abstract #3621

High-resolution Diffusion Tensor Imaging with Deep Learning Reconstruction: Preliminary Results in Sub-cortical Fiber Tracking

Zhangxuan Hu1, Xiaocheng Wei1, Jie Lu2, and Bing Wu1
1GE Healthcare, Beijing, China, 2Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Diffusion Tensor ImagingDiffusion tensor imaging (DTI) is a well-established tool for providing insights into brain structural connectivity and detecting brain microstructure. High spatial resolution diffusion MRI can provide improved resolvability of fibers with high-curvature (u-fibers). Segmented k-space methods such as Multiplexed sensitivity-encoding (MUSE) are often used to achieve high resolution diffusion images, however the shortcomings, such as prolonged scan time and low signal-noise-ratio (SNR), still exist. In this study, we aim to further improve the image quality of high-resolution diffusion images acquired with MUSE by combing with a deep learning based reconstruction method and thus to improve the sub-cortical fiber tracking accuracy.

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