REFERENCES

1. Zhang, B.; Liu, S.; Xie, X.; et al. HDCAR: a 3D-2D registration network for abdominal aortic vessels based on CTA vessel models and DSA images. Biomim. Intell. Robot. 2026, 6, 100272.

2. Feldkamp, L. A.; Davis, L. C.; Kress, J. W. Practical cone-beam algorithm. J. Opt. Soc. Am. A. 1984, 1, 612-9.

3. Andersen, A. H.; Kak, A. C. Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm. Ultrason. Imaging. 1984, 6, 81-94.

4. Shin, H.; Kim, T.; Lee, J.; Chun, S. Y.; Cho, S.; Shin, D. Sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization. Comput. Biol. Med. 2025, 189, 109900.

5. Mildenhall, B.; Srinivasan, P. P.; Tancik, M.; Barron, J. T.; Ramamoorthi, R.; Ng, R. NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM. 2022, 65, 99-106.

6. Gordon, R.; Bender, R.; Herman, G. T. Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. J. Theor. Biol. 1970, 29, 471-81.

7. Huang, J.; Zhang, Y.; Ma, J.; et al. Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior. PLoS. One. 2013, 8, e79709.

8. Niu, S.; Gao, Y.; Bian, Z.; et al. Sparse-view x-ray CT reconstruction via total generalized variation regularization. Phys. Med. Biol. 2014, 59, 2997-3017.

9. Huang, Y.; Taubmann, O.; Huang, X.; Haase, V.; Lauritsch, G.; Maier, A. Scale-space anisotropic total variation for limited angle tomography. IEEE. Trans. Radiat. Plasma. Med. Sci. 2018, 2, 307-14.

10. Xu, M.; Hu, D.; Luo, F.; Liu, F.; Wang, S.; Wu, W. Limited-angle X-ray CT reconstruction using image gradient ℓ₀-norm with dictionary learning. IEEE. Trans. Radiat. Plasma. Med. Sci. 2021, 5, 78-87.

11. Rückert, D.; Wang, Y.; Li, R.; Idoughi, R.; Heidrich, W. NeAT: neural adaptive tomography. ACM. Trans. Graph. 2022, 41, 1-13.

12. Chen, A.; Xu, Z.; Geiger, A.; Yu, J.; Su, H. TensoRF: tensorial radiance fields. In Computer Vision - ECCV 2022. Cham: Springer Nature Switzerland; Avidan, S.; Brostow, G.; Cissé, M.; Farinella, G. M.; Hassner, T.; Eds.; Springer, Cham.; 2022. pp. 333-50.

13. Zha, R.; Zhang, Y.; Li, H. NAF: neural attenuation fields for sparse-view CBCT reconstruction. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2022. Wang, L.; Dou, Q.; Fletcher, P. T.; Speidel, S.; Li, S; Eds.; Springer, Cham; 2022. pp. 442-52.

14. Zang, G.; Idoughi, R.; Li, R.; Wonka, P.; Heidrich, W. IntraTomo: self-supervised learning-based tomography via sinogram synthesis and prediction. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, Canada. Oct 10-17, 2021. IEEE; 2021. pp. 1960-70.

15. Sidky, E. Y.; Pan, X. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys. Med. Biol. 2008, 53, 4777.

16. Barron, J. T.; Mildenhall, B.; Tancik, M.; Hedman, P.; Martin-Brualla, R.; Srinivasan, P. P. Mip-NeRF: a multiscale representation for anti-aliasing neural radiance fields. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, Canada. Oct 10-17, 2021. IEEE; 2021. pp. 5855-64.

17. Wang, S.; Cheng, Q.; Gasperini, S.; et al. VoxNeRF: bridging voxel representation and neural radiance fields for enhanced indoor view synthesis. IEEE. Robot. Autom. Lett. 2025, 10, 5903-10.

18. Irshad, M. Z.; Zakharov, S.; Guizilini, V.; Gaidon, A.; Kira, Z.; Ambrus, R. NeRF-MAE: masked AutoEncoders for self-supervised 3D representation learning for neural radiance fields. In Computer Vision - ECCV 2024, Milan, Italy. Sep 29 - Oct 4, 2024. Cham: Springer Nature Switzerland; 2024. pp. 434-53.

19. Wang, X.; Chen, Y.; Hu, S.; Fan, H.; Zhu, H.; Li, X. Neural radiance fields in medical imaging: a survey. arXiv 2024, arXiv:2402.17797. Available online: https://doi.org/10.48550/arXiv.2402.17797. (accessed 2026-10-29).

20. Cai, Y.; Wang, J.; Yuille, A.; Zhou, Z.; Wang, A. Structure-aware sparse-view X-ray 3D reconstruction. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA. Jun 16-22, 2024. IEEE; 2024. pp. 11174-83.

21. Lin, Y.; Wang, H.; Chen, J.; Li, X. Learning 3D Gaussians for extremely sparse-view cone-beam CT reconstruction. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2024. Springer Nature Switzerland; 2024. pp. 425-35.

22. Li, Y.; Fu, X.; Li, H.; Zhao, S.; Jin, R.; Zhou, S. K. 3DGR-CT: sparse-view CT reconstruction with a 3D Gaussian representation. Med. Image. Anal. 2025, 103, 103585.

23. Xia, C.; Gu, T.; Zheng, N.; Wei, H.; Tsai, T. Y. RNAF: regularization neural attenuation fields for sparse-view CBCT reconstruction. J. Xray. Sci. Technol. 2025, 33, 713-25.

24. Müller, T.; Evans, A.; Schied, C.; Keller, A. Instant neural graphics primitives with a multiresolution hash encoding. ACM. Trans. Graph. 2022, 41, 1-15.

25. Kerbl, B.; Kopanas, G.; Leimkuehler, T.; Drettakis, G. 3D Gaussian splatting for real-time radiance field rendering. ACM. Trans. Graph. 2023, 42, 1-14.

26. Fridovich-Keil, S.; Yu, A.; Tancik, M.; Chen, Q.; Recht, B.; Kanazawa, A. Plenoxels: radiance fields without neural networks. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA. Jun 18-24, 2022. IEEE; 2022. pp. 5491-500.

27. Sun, C.; Sun, M.; Chen, H. T. Direct voxel grid optimization: super-fast convergence for radiance fields reconstruction. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA. Jun 18-24, 2022. IEEE; 2022. pp. 5449-59.

28. Barron, J. T.; Mildenhall, B.; Verbin, D.; Srinivasan, P. P.; Hedman, P. Mip-NeRF 360: unbounded anti-aliased neural radiance fields. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA. Jun 18-24, 2022. IEEE; 2022. pp. 5460-9.

29. Jacobs, R. A.; Jordan, M. I.; Nowlan, S. J.; Hinton, G. E. Adaptive mixtures of local experts. Neural. Comput. 1991, 3, 79-87.

30. Wang, X.; Yu, F.; Dunlap, L.; et al. Deep mixture of experts via shallow embedding. In Proceedings of The 35th Uncertainty in Artificial Intelligence Conference. PMLR; 2020. pp. 552-62. https://proceedings.mlr.press/v115/wang20d.html. (accessed 2026-10-29).

31. Bengio, E.; Bacon, P. L.; Pineau, J.; Precup, D. Conditional computation in neural networks for faster models. arXiv 2015, arXiv:1511.06297. Available online: https://doi.org/10.48550/arXiv.1511.06297. (accessed 2026-10-29).

32. Lepikhin, D.; Lee, H.; Xu, Y.; et al. GShard: scaling giant models with conditional computation and automatic sharding. arXiv 2020, arXiv:2006.16668. Available online: https://doi.org/10.48550/arXiv.2006.16668. (accessed 2026-10-29).

33. Fedus, W.; Zoph, B.; Shazeer, N. Switch transformers: scaling to trillion parameter models with simple and efficient sparsity. J. Mach. Learn. Res. 2022, 23, 5232-70.

34. Zhang, R.; Isola, P.; Efros, A. A.; Shechtman, E.; Wang, O. The unreasonable effectiveness of deep features as a perceptual metric. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA. Jun 18-23, 2018. IEEE; 2018. pp. 586-95.

35. Armato, S. G. 3rd.; McLennan, G.; Bidaut, L.; et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 2011, 38, 915-31.

36. Iacono, M. I.; Neufeld, E.; Akinnagbe, E.; et al. MIDA: a multimodal imaging-based detailed anatomical model of the human head and neck. PLoS. One. 2015, 10, e0124126.

37. Roth, H. R.; Lu, L.; Farag, A.; et al. DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. Navab, N.; Hornegger, J.; Wells, W. M.; Frangi, A.; Eds.; Cham: Springer International Publishing; 2015. pp. 556-64.

38. Teem. Inspecting volvis.org datasets. https://teem.sourceforge.net/nrrd/volvis/index.html. (accessed 2026-10-29).