  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.
