NeRF-CA: Dynamic Reconstruction of X-ray Coronary Angiography with Extremely Sparse-views

• URL: http://arxiv.org/abs/2408.16355v2
• Date: Wed, 11 Jun 2025 08:37:43 GMT
• Title: NeRF-CA: Dynamic Reconstruction of X-ray Coronary Angiography with Extremely Sparse-views
• Authors: Kirsten W. H. Maas, Danny Ruijters, Anna Vilanova, Nicola Pezzotti,
• Abstract summary: Existing CA reconstruction methods often require extensive user interaction or large training datasets.<n>NeRF has successfully reconstructed high-fidelity scenes in natural and medical contexts without these requirements.<n>We introduce NeRF-CA, a first step toward a fully automatic 4D CA reconstruction that achieves reconstructions from sparse coronary angiograms.
• Score: 1.1999555634662633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dynamic three-dimensional (4D) reconstruction from two-dimensional X-ray coronary angiography (CA) remains a significant clinical problem. Existing CA reconstruction methods often require extensive user interaction or large training datasets. Recently, Neural Radiance Field (NeRF) has successfully reconstructed high-fidelity scenes in natural and medical contexts without these requirements. However, challenges such as sparse-views, intra-scan motion, and complex vessel morphology hinder its direct application to CA data. We introduce NeRF-CA, a first step toward a fully automatic 4D CA reconstruction that achieves reconstructions from sparse coronary angiograms. To the best of our knowledge, we are the first to address the challenges of sparse-views and cardiac motion by decoupling the scene into the moving coronary artery and the static background, effectively translating the problem of motion into a strength. NeRF-CA serves as a first stepping stone for solving the 4D CA reconstruction problem, achieving adequate 4D reconstructions from as few as four angiograms, as required by clinical practice, while significantly outperforming state-of-the-art sparse-view X-ray NeRF. We validate our approach quantitatively and qualitatively using representative 4D phantom datasets and ablation studies. To accelerate research in this domain, we made our codebase public: https://github.com/kirstenmaas/NeRF-CA.

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