Abstract
Systolic and diastolic registration of coronary arteries is a critical yet challenging step in coronary artery disease analysis. Most existing methods ignore the important relationship between vascular geometric shape and image contextual information in the two phases, leading to limited performance. In this paper, we propose a novel structural point registration network, which comprehensively captures both point-level geometric features and image-level semantic features as enriched feature representations to assist coronary registration. Specifically, given the systolic and diastolic CCTA images, our method improves coronary artery registration from three aspects. First, the point cloud encoder learns the spatial geometric features of the points in the 3D coronary mask to effectively capture the vascular shape representation. Second, a vision transformer (ViT) is employed to extract the image semantic information as a complementary condition of the geometric features to identify the bi-phasic correspondence of different vascular branches. Third, we design a transformer module to fuse the features across points and images to obtain the corresponding structural points in the two phases and then use structural points to guide the coronary artery registration via the thin-plate spline (TPS) method. We evaluated our method on a real-clinical dataset. Extensive experiments show that our proposed method significantly outperforms the state-of-the-art methods in coronary artery registration.
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Acknowledgment
This work was supported in part by National Natural Science Foundation of China (grant number 62131015, 62073260, 62203355), and Science and Technology Commission of Shanghai Municipality (STCSM) (grant number 21010502600).
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Zhang, X. et al. (2023). SPR-Net: Structural Points Based Registration for Coronary Arteries Across Systolic and Diastolic Phases. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_74
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