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Abstract #1422

Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information

Yiqiong Yang1, Ye Wu2, Yanqiu Feng1, and Xinyuan Zhang1
1School of Biomedical Engineering, Southern Medical University, Guangzhou, China, Guangzhou, China, 2School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China, Nanjing, China

Synopsis

Keywords: Tractography, Tractography

Motivation: Tractography faces persistent issues with false positives and negatives. Studies suggest that adding novel information during reconstruction may improve sensitivity-specificity.

Goal(s): Our objective is to accurately predict propagation directions by integrating image-domain neighborhood information and contextual information along streamlines.

Approach: A network combining convolutional layer and several Transformer-decoders is proposed to integrate novel information.

Results: On the in-vivo dataset, our method achieves an average improvement of 5% in white matter coverage compared to existing methods, while maintaining a minimal increase of only 1% in overreach. On ISMRM2015 Challenge dataset, our method reconstructs 24 out of 25 bundles with 66% valid streamlines.

Impact: The proposed method successfully generates anatomically plausible streamlines across both synthetic and in-vivo human brain datasets. These promising results suggest that exploring additional novel information could further improve the anatomical reliability of white matter mapping.

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Keywords