Exploring three-dimensional reconstruction with Neural Radiance Field (NeRF) for coronary roadmap navigation and view-planning in X-ray coronary angiography: A feasibility study

BACKGROUND AND OBJECTIVES: Three-dimensional (3D) reconstruction from X-ray coronary angiograms could enhance diagnosis and guide treatment of coronary artery disease. This study investigates the feasibility of applying Neural Radiance Field (NeRF), a deep learning technique capable of automatic 3D reconstruction from a few views, to two clinical applications: (1) generating a coronary overlay ("roadmap") to assist navigation during interventions without contrast administration, and (2) predicting optimal viewing angles to support procedural planning. We will gather feedback by involving end users in the early evaluation of our approach, providing insights into its clinical relevance. METHODS: The 3D coronary tree was reconstructed using NeRF from various combinations of segmented angiographic views. The resulting 3D reconstructions were re-projected to the original viewing angles to generate 2D model-derived images. These were evaluated by four reviewers using a qualitative questionnaire focused on image quality, coronary topology, and visual clutter. RESULTS: Over 89% of NeRF-based roadmaps were rated as at least acceptable. In contrast, fewer than one-third of the predicted views for view-planning were considered minimally acceptable. Assessment varied between reviewers, particularly in scoring coronary topology and visual clutter, though these differences did not fully account for the variability in overall rated quality. CONCLUSION: NeRF-based 3D reconstruction from X-ray coronary angiography was adequate for generating coronary roadmaps, but inadequate for view-planning. Further improvements are needed in reconstructing accurate 3D coronary topology, especially in the robustness of the model under few angiographic projections, before clinical adoption of NeRF is feasible.