Two-stage robust 3D CTA-2D DSA alignment via vascular-aware rigid and pyramid-based hierarchical non-rigid...

Accurate vascular structural alignment between 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) can significantly enhance visualization during percutaneous coronary intervention (PCI), thereby improving procedural success and reducing surgical risks. Existing methods typically rely on 2D/3D rigid, deformable, or hybrid registration driven by handcrafted features and manually designed matching strategies, which are inefficient and often fail in the challenging clinical scenarios involving vessel overlaps, missing branches, and complex deformations. Although deep learning has demonstrated superior performance in registration, its adoption in this domain is constrained by the lack of algorithms tailored to the unique vascular topology and the scarcity of high-quality paired training data. To address current limitations, we propose a two-stage deep learning registration framework with rigid and deformable stages for robust, efficient vascular structural alignment between 3D CTA and 2D DSA. In the rigid stage, we develop a vascular topology-aware matching network that uses hybrid attention and branch missing prediction to establish correspondences, leveraging a specialized tree attention for robustness in overlapping regions. In the deformable stage, we estimate complex deformations in a coarse-to-fine manner using a pyramid-based hierarchical module, guided by soft correspondence scores from the rigid stage to preserve anatomical consistency. To train and evaluate the framework, we collect 4983 CTA scans and 783 DSA sequences, and further generate 1,050,000 synthetic CTA-DSA pairs via simulations with controlled geometric transformations, anatomical variations such as branch trimming, and diverse imaging artifacts, thereby laying the groundwork for the deep learning-based method in this domain. Extensive experiments on real and simulated datasets demonstrate the effectiveness of our method and highlight its potential to enhance intraoperative PCI visualization. A public repository has been created at Link. The simulation pipeline, pretrained inference weights, and evaluation scripts are being organized for public release to support reproducible testing.