3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning

• URL: http://arxiv.org/abs/2405.10705v1
• Date: Fri, 17 May 2024 11:23:33 GMT
• Title: 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning
• Authors: Zhentao Liu, Huangxuan Zhao, Wenhui Qin, Zhenghong Zhou, Xinggang Wang, Wenping Wang, Xiaochun Lai, Chuansheng Zheng, Dinggang Shen, Zhiming Cui,
• Abstract summary: Current commercial Digital Subtraction Angiography (DSA) systems typically demand hundreds of scanning views to perform reconstruction. The dynamic blood flow and insufficient input of sparse-view DSA images present significant challenges to the 3D vessel reconstruction task. We propose to use a time-agnostic vessel probability field to solve this problem effectively.
• Score: 79.60829508459753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital Subtraction Angiography (DSA) is one of the gold standards in vascular disease diagnosing. With the help of contrast agent, time-resolved 2D DSA images deliver comprehensive insights into blood flow information and can be utilized to reconstruct 3D vessel structures. Current commercial DSA systems typically demand hundreds of scanning views to perform reconstruction, resulting in substantial radiation exposure. However, sparse-view DSA reconstruction, aimed at reducing radiation dosage, is still underexplored in the research community. The dynamic blood flow and insufficient input of sparse-view DSA images present significant challenges to the 3D vessel reconstruction task. In this study, we propose to use a time-agnostic vessel probability field to solve this problem effectively. Our approach, termed as vessel probability guided attenuation learning, represents the DSA imaging as a complementary weighted combination of static and dynamic attenuation fields, with the weights derived from the vessel probability field. Functioning as a dynamic mask, vessel probability provides proper gradients for both static and dynamic fields adaptive to different scene types. This mechanism facilitates a self-supervised decomposition between static backgrounds and dynamic contrast agent flow, and significantly improves the reconstruction quality. Our model is trained by minimizing the disparity between synthesized projections and real captured DSA images. We further employ two training strategies to improve our reconstruction quality: (1) coarse-to-fine progressive training to achieve better geometry and (2) temporal perturbed rendering loss to enforce temporal consistency. Experimental results have demonstrated superior quality on both 3D vessel reconstruction and 2D view synthesis.

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