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Sparse-view computed tomography (CT) reduces radiation exposure by acquiring fewer projections, making it a valuable tool in clinical scenarios where low-dose radiation is essential.
Simultaneous algebraic reconstruction technique (sart): a superior implementation of the art algorithm
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Practical cone-beam algorithm
Feldkamp, L.A., Davis, L.C., Kress, J.W., 1984 · 1984
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Nonlinear total variation based noise removal algorithms
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Investigation of the usability of conebeam CT data sets for dose calculation
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Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization
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Fundamentals of computerized tomography: image reconstruction from projections
Herman, G.T., 2009 · 2009
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Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam ct
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Optimized kaiser–bessel window functions for computed tomography
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Lose the views: Limited angle CT reconstruction via implicit sinogram completion, in: Proc. IEEE Conf. Comput. Vis. and Pattern Recog., pp. 6343–6352
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Dosenet: a volumetric dose prediction algorithm using 3d fully-convolutional neural networks
Kearney, V., Chan, J.W., Haaf, S., Descovich, M., Solberg, T.D., 2018 · 2018
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Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction
Lee, H., Lee, J., Kim, H., Cho, B., Cho, S., 2018 · 2018
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A sparse-view CT reconstruction method based on combination of densenet and deconvolution
Zhang, Z., Liang, X., Dong, X., Xie, Y., Cao, G., 2018 · 2018
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On the spectral bias of neural networks, in: ICML, pp. 5301–5310
Rahaman, N., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F., Bengio, Y., Courville, A., 2019 · 2019
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Denoising diffusion probabilistic models
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A vertebral segmentation dataset with fracture grading
Löffler, M.T., Sekuboyina, A., Jacob, A., Grau, A.L., Scharr, A., El Husseini, M., Kallweit, M., Zimmer, C., Baum, T., Kirschke, J.S., 2020 · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Tancik, M., Srinivasan, P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J., Ng, R., 2020 · 2020
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Learnable multi-scale fourier interpolation for sparse view CT image reconstruction, in: Lect. Notes Comput. Sci., Springer. Springer. pp. 286–295
Ding, Q., Ji, H., Gao, H., Zhang, X., 2021 · 2021
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A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data
Liebl, H., Schinz, D., Sekuboyina, A., Malagutti, L., Löffler, M.T., Bayat, A., El Husseini, M., Tetteh, G., Grau, K., Niederreiter, E., Baum, T., Wiestler, B., Menze, B., Braren, R., Zimmer, C., Kirschke, J.S., 2021 · 2021
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Low-dose ct image and projection dataset
Moen, T.R., Chen, B., Holmes III, D.R., Duan, X., Yu, Z., Yu, L., Leng, S., Fletcher, J.G., McCollough, C.H., 2021 · 2021
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Dynamic CT reconstruction from limited views with implicit neural representations and parametric motion fields, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2258–2268
Reed, A.W., Kim, H., Anirudh, R., Mohan, K.A., Champley, K., Kang, J., Jayasuriya, S., 2021 · 2021
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Verse: A vertebrae labelling and segmentation benchmark for multi-detector ct images
Sekuboyina, A., Husseini, M.E., Bayat, A., Löffler, M., Liebl, H., Li, H., Tetteh, G., Kukačka, J., Payer, C., Štern, D., Urschler, M., Chen, M., Cheng, D., Lessmann, N., Hu, Y., Wang, T., Yang, D., Xu, D., Ambellan, F., Amiranashvili, T., Ehlke, M., Lamecker, H., Lehnert, S., Lirio, M., de Olaguer, N.P., Ramm, H., Sahu, M., Tack, A., Zachow, S., Jiang, T., Ma, X., Angerman, C., Wang, X., Brown, K., Kirszenberg, A., Élodie Puybareau, Chen, D., Bai, Y., Rapazzo, B.H., Yeah, T., Zhang, A., Xu, S., Hou, F., He, Z., Zeng, C., Xiangshang, Z., Liming, X., Netherton, T.J., Mumme, R.P., Court, L.E., Huang, Z., He, C., Wang, L.W., Ling, S.H., Huỳnh, L.D., Boutry, N., Jakubicek, R., Chmelik, J., Mulay, S., Sivaprakasam, M., Paetzold, J.C., Shit, S., Ezhov, I., Wiestler, B., Glocker, B., Valentinitsch, A., Rempfler, M., Menze, B.H., Kirschke, J.S., 2021 · 2021
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Coil: Coordinate-based internal learning for imaging inverse problems
Sun, Y., Liu, J., Xie, M., Wohlberg, B., Kamilov, U.S., 2021 · 2021
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Task-oriented low-dose CT image denoising, in: Lect. Notes Comput. Sci., pp. 441–450
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Mip-nerf 360: Unbounded anti-aliased neural radiance fields, in: Proc. IEEE Int. Conf. Comput. Vis., pp. 5470–5479
Solving linear inverse problems provably via posterior sampling with latent diffusion models
Rout, L., Raoof, N., Daras, G., Caramanis, C., Dimakis, A., Shakkottai, S., 2023 · 2023
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Compact neural graphics primitives with learned hash probing, in: Proc. - SIGGRAPH Asia Conf. Pap., SA, pp. 120:1–120:10
Takikawa, T., Müller, T., Nimier-David, M., Evans, A., Fidler, S., Jacobson, A., Keller, A., 2023 · 2023
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Synthrad2023 grand challenge dataset: Generating synthetic CT for radiotherapy
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Mepnet: A model-driven equivariant proximal network for joint sparse-view reconstruction and metal artifact reduction in ct images, in: Lect. Notes Comput. Sci., Springer. pp. 109–120
Wang, H., Zhou, M., Wei, D., Li, Y., Zheng, Y., 2023 · 2023
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Radiative gaussian splatting for efficient x-ray novel view synthesis
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Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P., 2022 · 2022
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Diffusion posterior sampling for general noisy inverse problems
Chung, H., Kim, J., Mccann, M.T., Klasky, M.L., Ye, J.C., 2022 · 2022
Cited alongside, same era.
Snaf: Sparse-view cbct reconstruction with neural attenuation fields
Fang, Y., Mei, L., Li, C., Liu, Y., Wang, W., Cui, Z., Shen, D., 2022 · 2022
Cited alongside, same era.
Ddpnet: a novel dual-domain parallel network for low-dose CT reconstruction, in: Lect. Notes Comput. Sci., pp. 748–757
Ge, R., He, Y., Xia, C., Sun, H., Zhang, Y., Hu, D., Chen, S., Chen, Y., Li, S., Zhang, D., 2022 · 2022
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Automated segmentation of normal and diseased coronary arteries – the asoca challenge
Gharleghi, R., Adikari, D., Ellenberger, K., Ooi, S.Y., Ellis, C., Chen, C.M., Gao, R., He, Y., Hussain, R., Lee, C.Y., Li, J., Ma, J., Nie, Z., Oliveira, B., Qi, Y., Skandarani, Y., Vilaça, J.L., Wang, X., Yang, S., Sowmya, A., Beier, S., 2022a · 2022
Cited alongside, same era.
Patch-wise deep metric learning for unsupervised low-dose ct denoising, in: Lect. Notes Comput. Sci., pp. 634–643
Jung, C., Lee, J., You, S., Ye, J.C., 2022 · 2022
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Open scientific visualization datasets
Klacansky, P., 2022 · 2022
Cited alongside, same era.
Instant neural graphics primitives with a multiresolution hash encoding
Müller, T., Evans, A., Schied, C., Keller, A., 2022 · 2022
Cited alongside, same era.
Nerp: implicit neural representation learning with prior embedding for sparsely sampled image reconstruction
Shen, L., Pauly, J., Xing, L., 2022 · 2022
Cited alongside, same era.
Cai, Y., Liang, Y., Wang, J., Wang, A., Zhang, Y., Yang, X., Zhou, Z., Yuille, A., 2024 · 2024
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Gaussianeditor: Swift and controllable 3d editing with gaussian splatting, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21476–21485
Chen, Y., Chen, Z., Zhang, C., Wang, F., Yang, X., Wang, Y., Cai, Z., Yang, L., Liu, H., Lin, G., 2024 · 2024
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DDGS-CT: direction-disentangled gaussian splatting for realistic volume rendering
Gao, Z., Planche, B., Zheng, M., Chen, X., Chen, T., Wu, Z., 2024 · 2024
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Eagles: Efficient accelerated 3d gaussians with lightweight encodings, in: European Conference on Computer Vision, Springer. pp. 54–71
Girish, S., Gupta, K., Shrivastava, A., 2024 · 2024
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Maisi: Medical ai for synthetic imaging
Guo, P., Zhao, C., Yang, D., Xu, Z., Nath, V., Tang, Y., Simon, B., Belue, M., Harmon, S., Turkbey, B., et al., 2024 · 2024
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Endosparse: Real-time sparse view synthesis of endoscopic scenes using gaussian splatting
Li, C., Feng, B.Y., Liu, Y., Liu, H., Wang, C., Yu, W., Yuan, Y., 2024 · 2024
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Learning 3d gaussians for extremely sparse-view cone-beam CT reconstruction
Lin, Y., Wang, H., Chen, J., Li, X., 2024 · 2024
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LGS: A light-weight 4d gaussian splatting for efficient surgical scene reconstruction
Liu, H., Liu, Y., Li, C., Li, W., Yuan, Y., 2024 · 2024
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Gaspct: Gaussian splatting for novel CT projection view synthesis
Nikolakakis, E., Gupta, U., Vengosh, J., Bui, J., Marinescu, R., 2024 · 2024
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Adr-gaussian: Accelerating gaussian splatting with adaptive radius, in: SIGGRAPH Asia 2024 Conference Papers, pp. 1–10
Wang, X., Yi, R., Ma, L., 2024b · 2024
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Hyb-nerf: A multiresolution hybrid encoding for neural radiance fields, in: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE. pp. 3677–3686
Wang, Y., Gong, Y., Zeng, Y., 2024c · 2024
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Physgaussian: Physics-integrated 3d gaussians for generative dynamics
Xie, T., Zong, Z., Qiu, Y., Li, X., Feng, Y., Yang, Y., Jiang, C., 2024 · 2024
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R 2 {}^{\mbox{2}} -gaussian: Rectifying radiative gaussian splatting for tomographic reconstruction
Zha, R., Lin, T.J., Cai, Y., Cao, J., Zhang, Y., Li, H., 2024 · 2024
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TOGS: gaussian splatting with temporal opacity offset for real-time 4d DSA rendering
Zhang, S., Zhao, H., Zhou, Z., Wu, G., Zheng, C., Wang, X., Liu, W., 2024 · 2024
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Deformable endoscopic tissues reconstruction with gaussian splatting
Zhu, L., Wang, Z., Jin, Z., Lin, G., Yu, L., 2024 · 2024
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Does 3d gaussian splatting need accurate volumetric rendering?
Celarek, A., Kopanas, G., Drettakis, G., Wimmer, M., Kerbl, B., 2025 · 2025
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Eagles: Efficient accelerated 3d gaussians with lightweight encodings, in: European Conference on Computer Vision, Springer. pp. 54–71
Girish, S., Gupta, K., Shrivastava, A., 2025 · 2025
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Compgs: Smaller and faster gaussian splatting with vector quantization, in: European Conference on Computer Vision, Springer. pp. 330–349
Navaneet, K., Pourahmadi Meibodi, K., Abbasi Koohpayegani, S., Pirsiavash, H., 2025 · 2025
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