NeRF-CA: Dynamic Reconstruction of X-Ray Coronary Angiography With Extremely Sparse-Views: Dynamic Reconstruction of X-ray Coronary Angiography with Extremely Sparse-views
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| Název: | NeRF-CA: Dynamic Reconstruction of X-Ray Coronary Angiography With Extremely Sparse-Views: Dynamic Reconstruction of X-ray Coronary Angiography with Extremely Sparse-views |
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| Autoři: | Kirsten W.H. Maas, Danny Ruijters, Anna Vilanova, Nicola Pezzotti |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics. 31:8782-8795 |
| Publication Status: | Preprint |
| Informace o vydavateli: | Institute of Electrical and Electronics Engineers (IEEE), 2025. |
| Rok vydání: | 2025 |
| Témata: | Coronary Angiography/methods, FOS: Computer and information sciences, Neural Radiance Field, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, Coronary Vessels/diagnostic imaging, Electrical Engineering and Systems Science - Image and Video Processing, Phantoms, Imaging, 4D reconstruction, X-ray coronary angiography, sparse-view, Three-Dimensional/methods, Computer Graphics, FOS: Electrical engineering, electronic engineering, information engineering, Humans, 3D reconstruction, Algorithms |
| Popis: | 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. |
| Druh dokumentu: | Article |
| ISSN: | 2160-9306 1077-2626 |
| DOI: | 10.1109/tvcg.2025.3579162 |
| DOI: | 10.48550/arxiv.2408.16355 |
| Přístupová URL adresa: | http://arxiv.org/abs/2408.16355 https://research.tue.nl/en/publications/538e0c5a-087e-4999-ae1e-21d1318a35a2 https://doi.org/10.1109/TVCG.2025.3579162 |
| Rights: | IEEE Copyright CC BY |
| Přístupové číslo: | edsair.doi.dedup.....498386b65ac7208436d99b1c9dcfd53d |
| Databáze: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | 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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| ISSN: | 21609306 10772626 |
| DOI: | 10.1109/tvcg.2025.3579162 |