A Novel Coronary Artery Registration Method Based on Super-pixel Particle Swarm Optimization
Journal:
arXiv
Published Date:
May 30, 2025
Abstract
Percutaneous Coronary Intervention (PCI) is a minimally invasive procedure
that improves coronary blood flow and treats coronary artery disease. Although
PCI typically requires 2D X-ray angiography (XRA) to guide catheter placement
at real-time, computed tomography angiography (CTA) may substantially improve
PCI by providing precise information of 3D vascular anatomy and status. To
leverage real-time XRA and detailed 3D CTA anatomy for PCI, accurate multimodal
image registration of XRA and CTA is required, to guide the procedure and avoid
complications. This is a challenging process as it requires registration of
images from different geometrical modalities (2D -> 3D and vice versa), with
variations in contrast and noise levels. In this paper, we propose a novel
multimodal coronary artery image registration method based on a swarm
optimization algorithm, which effectively addresses challenges such as large
deformations, low contrast, and noise across these imaging modalities. Our
algorithm consists of two main modules: 1) preprocessing of XRA and CTA images
separately, and 2) a registration module based on feature extraction using the
Steger and Superpixel Particle Swarm Optimization algorithms. Our technique was
evaluated on a pilot dataset of 28 pairs of XRA and CTA images from 10 patients
who underwent PCI. The algorithm was compared with four state-of-the-art (SOTA)
methods in terms of registration accuracy, robustness, and efficiency. Our
method outperformed the selected SOTA baselines in all aspects. Experimental
results demonstrate the significant effectiveness of our algorithm, surpassing
the previous benchmarks and proposes a novel clinical approach that can
potentially have merit for improving patient outcomes in coronary artery
disease.
