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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