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

TL;DR

This paper introduces a novel vessel probability guided attenuation learning method for 3D vessel reconstruction from sparse-view dynamic DSA images, effectively reducing radiation exposure while maintaining high reconstruction quality.

Contribution

The study proposes a vessel probability guided attenuation learning framework that decomposes static and dynamic components, enabling self-supervised 3D vessel reconstruction from sparse-view DSA images.

Findings

Achieves superior 3D vessel reconstruction quality from sparse-view DSA images.

Effectively decomposes static background and dynamic blood flow components.

Improves temporal consistency and geometric accuracy in reconstructions.

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.