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Abstract #0392

Enhancing Diffusion MR Tractography Using a Deep Learning Model that Incorporates Anatomical Knowledge

Zifei Liang1, Patryk Filipiak1, Steven H. Baete1, Yulin Ge1, Leslie Ying2, and Jiangyang Zhang1
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA., New York, NY, United States, 2Department of Biomedical Engineering and Electrical Engineering, the State University of New York, Buffalo, NY, USA., Buffalo, NY, United States

Synopsis

Keywords: Tractography, Tractography & Fibre Modelling

Motivation: To develop reliable diffusion MRI tractography to study brain connectivity.

Goal(s): The study aims to improve the estimation of fiber orientation distribution (FOD), which is key to improve the specificity of tractography.

Approach: We created an augmented streamline dataset based on known white matter pathways to train a deep neural network to estimate FOD from diffusion MRI signals.

Results: Tractography based on the network estimated FODs showed reduced false-positives compared to conventional methods. The improvement remained for input data with reduced angular resolutions and added noise.

Impact: The proposed method can improve tractography by reducing false-positives and benefit studies on structural connectivity of the brain. Furthermore, it may shorten the acquisition time required for robust tractography, which is important for studies on children and seniors.

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