  Coronary angiography is the gold standard imaging technique for studying and
diagnosing coronary artery disease. However, the resulting 2D X-ray projections
lose 3D information and exhibit visual ambiguities. In this work, we aim to
establish dense correspondence in multi-view angiography, serving as a
fundamental basis for various clinical applications and downstream tasks. To
overcome the challenge of unavailable annotated data, we designed a data
simulation pipeline using 3D Coronary Computed Tomography Angiography (CCTA).
We formulated the problem of dense correspondence estimation as a query
matching task over all points of interest in the given views. We established
point-to-point query matching and advanced it to curve-to-curve correspondence,
significantly reducing errors by minimizing ambiguity and improving topological
awareness. The method was evaluated on a set of 1260 image pairs from different
views across 8 clinically relevant angulation groups, demonstrating compelling
results and indicating the feasibility of establishing dense correspondence in
multi-view angiography.
