An intensity-based end-to-end tree alignment method for 3D/2D coronary artery registration

Abstract Objective. Real-time x-ray fluoroscopy (XRF) is widely used for navigating coronary interventions. X-ray coronary angiography (XCA) is needed to facilitate the interventional procedure, resulting in excessive consumption of iodinated contrast agents. To reduce the contrast consumption, the vessel masks derived from XCA images are overlaid on XRF images, assuming periodic heartbeats. To address the two-dimensional (2D) nature of XCA limits, using 3D/2D coronary artery registration, centerlines with depth information derived from three-dimensional (3D) coronary computed tomography angiography (CCTA) can provide the necessary depth guidance and discriminate vessel overlapping. However, the existing tree-aligning feature-based registration strategy suffers from the error accumulation introduced by cascading multi-step processes or time-consuming limitation of iterative optimization in the 3D/2D tree alignment step. Approach. In this work, we propose an intensity-based end-to-end 3D/2D tree alignment method to address these limitations by removing the image-to-graph modality transformation in the intraoperative pipeline. Specifically, we design AlignNet for the intensity-based 3D/2D tree alignment, RegisNet for the centerline registration with graph regularization. AlignNet and RegisNet are trained using a cost-efficient supervised learning strategy based on large-scale simulated coronary deformations. Three cascaded end-to-end networks including nnUnet, AlignNet, and RegisNet are integrated into the intraoperative pipeline. Main results. Numerical simulations involving qualitative and quantitative analyses of large-scale CCTA–XCA samples achieved a mean registration error of 0.42 mm. Clinical validation using paired CCTA–XCA data from six patients achieved a mean registration error of 0.359 mm and a mean processing speed of 16.3 fps. In addition, validation using one paired CCTA–XCA–XRF case demonstrated the feasibility of generating depth-guided coronary roadmaps for interventional navigation. Significance. These results demonstrate that the proposed technique has the potential to achieve accurate and real-time depth-guided coronary intervention navigation, and it is valuable for subsequent multi-center clinical studies.