Towards Establishing Dense Correspondence on Multiview Coronary
  Angiography: From Point-to-Point to Curve-to-Curve Query Matching
Yifan Wu Rohit Jena Mehmet Gulsun Vivek Singh Puneet Sharma James C. Gee
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.