Leveraging Diffusion Model and Image Foundation Model for Improved Correspondence Matching in Coronary Angiography
Journal:
arXiv
Published Date:
Mar 31, 2025
Abstract
Accurate correspondence matching in coronary angiography images is crucial
for reconstructing 3D coronary artery structures, which is essential for
precise diagnosis and treatment planning of coronary artery disease (CAD).
Traditional matching methods for natural images often fail to generalize to
X-ray images due to inherent differences such as lack of texture, lower
contrast, and overlapping structures, compounded by insufficient training data.
To address these challenges, we propose a novel pipeline that generates
realistic paired coronary angiography images using a diffusion model
conditioned on 2D projections of 3D reconstructed meshes from Coronary Computed
Tomography Angiography (CCTA), providing high-quality synthetic data for
training. Additionally, we employ large-scale image foundation models to guide
feature aggregation, enhancing correspondence matching accuracy by focusing on
semantically relevant regions and keypoints. Our approach demonstrates superior
matching performance on synthetic datasets and effectively generalizes to
real-world datasets, offering a practical solution for this task. Furthermore,
our work investigates the efficacy of different foundation models in
correspondence matching, providing novel insights into leveraging advanced
image foundation models for medical imaging applications.
