Basea Gomez, M., Thierry-Chef, I., Harbron, R., Hauptmann, M., Byrnes, G., Bernier, M.-O., Cornet, L.: Risk of hematological malignancies from ct radiation exposure in children, adolescents and young adults. Nat. Med. 29(12), 3111–3119 (2023). https://doi.org/10.1038/s41591-023-02620-0

Chen, H., Li, Q., Zhou, L., Li, F.: Deep learning-based algorithms for low-dose ct imaging: A review. Eur. J. Radiol., pp., 111355 (2024)

Ghane, B., Karimian, A., Mostafapour, S., Gholamiankhak, F., Shojaerazavi, S., Arabi, H.: Quantitative analysis of image quality in low-dose computed tomography imaging for covid-19 patients. J. Med. Signals Sens. 13(2), 118–128 (2023)

Article  PubMed  PubMed Central  Google Scholar 

Zhang, H., Ouyang, L., Ma, J., Huang, J., Chen, W., Wang, J.: Noise correlation in cbct projection data and its application for noise reduction in low-dose cbct. Med. Phys. 41(3), 031906 (2014)

Article  PubMed  Google Scholar 

Kachelriess, M., Watzke, O., Kalender, W.A.: Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam ct. Med. Phys. 28(4), 475–490 (2001)

Article  CAS  PubMed  Google Scholar 

Zhang, Q., Gui, Z., Chen, Y., Li, Y., Luo, L.: Bayesian sinogram smoothing with an anisotropic diffusion weighted prior for low-dose x-ray computed tomography. Optik 124(17), 2811–2816 (2013). https://doi.org/10.1016/j.ijleo.2012.08.045

Article  Google Scholar 

Cui, X., Gui, Z., Zhang, Q., Liu, Y., Ma, R.: The statistical sinogram smoothing via adaptive-weighted total variation regularization for low-dose x-ray ct. Optik 125(18), 5352–5356 (2014)

Article  Google Scholar 

Lu, S., Yang, B., Xiao, Y., Liu, S., Liu, M., Yin, L., Zheng, W.: Iterative reconstruction of low-dose ct based on differential sparse. Biomed. Signal Process. Control 79, 104204(2023)

Zhao, F., Liu, M., Gao, Z., Jiang, X., Wang, R., Zhang, L.: Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose ct denoising. Comput. Biol. Med. 161,107029 (2023)

Article  PubMed  Google Scholar 

Zhao, F., Li, D., Luo, R., Liu, M., Jiang, X., Hu, J.: Self-supervised deep learning for joint 3d low-dose pet/ct image denoising. Comput. Biol. Med. 165,107391 (2023)

Article  PubMed  Google Scholar 

Krupinski, E.A.: Artificial intelligence: Lessons learned from radiology. Healthc. Transform. 0(0), 5–10 (2019). https://doi.org/10.1089/heat.2019.0008

Moassefi, M., Singh, Y., Conte, G.M., Khosravi, B., Rouzrokh, P., Vahdati, S., Safdar, N., Moy, L., Kitamura, F., Gentili, A., et al.: Checklist for reproducibility of deep learning in medical imaging. J. Imaging Inform. Med., pp. 1–10 (2024)

Jung, C., Lee, J., You, S., Ye, J.C.: Patch-wise deep metric learning for unsupervised low-dose ct denoising. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022, pp. 634–643 (2022).Springer

Liu, X., Xie, Y., Diao, S., Tan, S., Liang, X.: A diffusion probabilistic prior for low-dose ct image denoising. arXiv:2305.15887 (2023)

Niu, C., Li, M., Fan, F., Wu, W., Guo, X., Lyu, Q., Wang, G.: Noise suppression with similarity-based self-supervised deep learning. IEEE Trans. Med. Imaging 42(6), 1590–1602 (2023). https://doi.org/10.1109/TMI.2022.3231428

Article  PubMed  PubMed Central  Google Scholar 

Wu, Q., Ji, X., Gu, Y., Xiang, J., Quan, G., Li, B., Zhu, J., Coatrieux, G., Coatrieux, J.-L., Chen, Y.: Unsharp structure guided filtering for self-supervised low-dose ct imaging. IEEE Trans. Med. Imaging 42(11), 3283–3294 (2023). https://doi.org/10.1109/TMI.2023.3280217

Article  PubMed  Google Scholar 

Choi, K., Kim, S.H., Kim, S.: Self-supervised denoising of projection data for low-dose cone-beam ct. Med. Phys. 50(10), 6319–6333 (2023)

Article  PubMed  Google Scholar 

Hong, Z., Zeng, D., Tao, X., Ma, J.: Learning ct projection denoising from adjacent views. Med. Phys. 50(3), 1367–1377 (2023)

Article  CAS  PubMed  Google Scholar 

Eulig, E., Ommer, B., Kachelrieß, M.: Benchmarking deep learning-based low dose ct image denoising algorithms. arXiv:2401.04661 (2024)

Batson, J., Royer, L.: Noise2self: Blind denoising by self-supervision. In: International Conference on Machine Learning, pp. 524–533 (2019).PMLR

Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., Lin, C.-W.: Deep learning on image denoising: An overview. Neural Netw. 131,251–275 (2020)

Article  PubMed  Google Scholar 

Danielyan, A., Katkovnik, V., Egiazarian, K.: Bm3d frames and variational image deblurring. IEEE Trans. Image Process. 21(4), 1715–1728 (2011)

Article  PubMed  Google Scholar 

Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Bm3d image denoising with shape-adaptive principal component analysis. In: SPARS’09-Signal Processing with Adaptive Sparse Structured Representations (2009)

Sun, K., Simon, S.: Bilateral spectrum weighted total variation for noisy-image super-resolution and image denoising. IEEE Trans. Signal Process. 69,6329–6341 (2021)

Article  Google Scholar 

Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M., Aila, T.: Noise2noise: Learning image restoration without clean data. In: International Conference on Machine Learning, pp. 2965–2974 (2018). PMLR

Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)

Article  PubMed  Google Scholar 

Chen, H., Zhang, Y., Kalra, M.K., Lin, F., Chen, Y., Liao, P., Zhou, J., Wang, G.: Low-dose ct with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(12), 2524–2535 (2017). https://doi.org/10.1109/TMI.2017.2715284

Article  PubMed  PubMed Central  Google Scholar 

Li, Z., Liu, Y., Shu, H., Lu, J., Kang, J., Chen, Y., Gui, Z.: Multi-scale feature fusion network for low-dose ct denoising. J. Digit. Imaging 36(4), 1808–1825 (2023)

Article  PubMed  PubMed Central  Google Scholar 

Jin, K.H., McCann, M.T., Froustey, E., Unser, M.: Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process. 26(9), 4509–4522 (2017). https://doi.org/10.1109/TIP.2017.2713099

Article  Google Scholar 

Fan, F., Shan, H., Kalra, M.K., Singh, R., Qian, G., Getzin, M., Teng, Y., Hahn, J., Wang, G.: Quadratic autoencoder (q-ae) for low-dose ct denoising. IEEE Trans. Med. Imaging 39(6), 2035–2050(2020). https://doi.org/10.1109/TMI.2019.2963248

Article  PubMed  Google Scholar 

Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra, M.K., Zhang, Y., Sun, L., Wang, G.: Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348–1357 (2018)

Article  PubMed  PubMed Central  Google Scholar 

Ma, Y., Wei, B., Feng, P., He, P., Guo, X., Wang, G.: Low-dose ct image denoising using a generative adversarial network with a hybrid loss function for noise learning. IEEE Access 8,67519–67529 (2020)

Article  Google Scholar 

Wolterink, J.M., Leiner, T., Viergever, M.A., Išgum, I.: Generative adversarial networks for noise reduction in low-dose ct. IEEE Trans. Med. Imaging 36(12), 2536–2545 (2017)

Article  PubMed  Google Scholar 

Wang, G., Hu, X.: Low-dose ct denoising using a progressive wasserstein generative adversarial network. Comput. Biol. Med. 135, 104625 (2021)

Article  PubMed  Google Scholar 

Kim, W., Lee, M., Lee, H., Cho, H.: Wasserstein-gan-based noise reduction for preserving anatomical structures inlow-dose ct. Nucl. Instrum. Methods Phys. Res. A, 169338 (2024)

Marcos, L., Babyn, P., Alirezaie, J.: Pure vision transformer (ct-vit) with noise2neighbors interpolation forlow-dose ct image denoising. J. Imaging Inform. Med., 1–19 (2024)

Wang, H., Chi, J., Wu, C., Yu, X., Wu, H.: Degradation adaption local-to-global transformer for low-dose ct image denoising. J. Digit. Imaging 36(4), 1894–1909 (2023)

Article  PubMed  PubMed Central  Google Scholar 

Ding, Q., Nan, Y., Gao, H., Ji, H.: Deep learning with adaptive hyper-parameters for low-dose ct image reconstruction. IEEE Trans. Comput. Imaging 7,648–660 (2021)

Article  Google Scholar 

Xia, W., Shan, H., Wang, G., Zhang, Y.: Physics-/model-based and data-driven methods for low-dose computed tomography: A survey. IEEE Signal Process. Mag. 40(2), 89–100 (2023). https://doi.org/10.1109/MSP.2022.3204407

Zhang, Z., Yu, L., Liang, X., Zhao, W., Xing, L.: Transct: dual-path transformer for low dose computed tomography. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24, pp. 55–64 (2021).Springer

Ge, R., He, Y., Xia, C., Sun, H., Zhang, Y., Hu, D., Chen, S., Chen, Y., Li, S., Zhang, D.: Ddpnet: a novel dual-domain parallel network for low-dose ct reconstruction. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 748–757 (2022). Springer

Kang, E., Koo, H.J., Yang, D.H., Seo, J.B., Ye, J.C.: Cycle-consistent adversarial denoising network for multiphase coronary ct angiography. Med. Phys. 46(2), 550–562 (2019). https://doi.org/10.1002/mp.13284

Article  PubMed  Google Scholar 

Kwon, T., Ye, J.C.: Cycle-free cyclegan using invertible generator for unsupervised low-dose ct denoising. IEEE Trans. Comput. Imaging 7, 1354–1368(2021)

Article  Google Scholar 

Zhao, F., Liu, M., Gao, Z., Jiang, X., Wang, R., Zhang, L.: Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose ct denoising. Comput. Biol. Med. 161, 107029 (2023)

Lee, K., Jeong, W.-K.: Iscl: Interdependent self-cooperative learning for unpaired image denoising. IEEE Trans. Med. Imaging 40(11), 3238–3248 (2021)

Article  PubMed  Google Scholar 

Gu, J., Yang, T.S., Ye, J.C., Yang, D.H.: Cyclegan denoising of extreme low-dose cardiac ct using wavelet-assisted noise disentanglement. Med. Image Anal. 74, 102209 (2021)

Liu, Y., Pang, S., Zeng, D., Xie, G., Ma, J., He, J.: Unpaired learning with a data-dependent noise-generative model for low-dose ct sinogram restoration. In: Medical Imaging 2023: Physics of Medical Imaging, vol. 12463, pp. 407–413 (2023).SPIE

Chen, K., Pu, X., Ren, Y., Qiu, H., Li, H., Sun, J.: Low-dose ct image blind denoising with graph convolutional networks. In: International Conference on Neural Information Processing, pp. 423–435 (2020).Springer

Hong, Z., Fan, X., Jiang, T., Feng, J.: End-to-end unpaired image denoising with conditional adversarial networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 4140–4149 (2020)

He, Z., Zhang, Y., Guan, Y., Guan, B., Niu, S., Zhang, Y., Chen, Y., Liu, Q.: Iterative reconstruction for low-dose ct using deep gradient priors of generative model. IEEE Trans. Radiat. Plasma Med. Sci. 6(7), 741–754 (2022)

Article  Google Scholar 

Liu, Y., Chen, G., Pang, S., Zeng, D., Ding, Y., Xie, G., Ma, J., He, J.: Cross-domain unpaired learning for low-dose ct imaging. IEEE J. Biomed. Health Inform. (2023)

Book  Google Scholar