Towards Establishing Dense Correspondence on Multiview Coronary Angiography: From Point-to-Point to Curve-to-Curve Query Matching
This work addresses a fundamental challenge for clinical applications in coronary artery disease diagnosis by enabling dense correspondence in angiography, though it is incremental as it builds on existing query matching approaches.
The paper tackled the problem of establishing dense correspondence in multi-view coronary angiography to address the loss of 3D information and visual ambiguities in 2D X-ray projections, achieving compelling results with a method that reduced errors by advancing from point-to-point to curve-to-curve query matching, as evaluated on 1260 image pairs across 8 angulation groups.
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.