Evaluation of Denoising Architectures on a Reconstructed Ultra-Low-Dose CT Dataset
Resumo
Advances in low-radiation-dose Computed Tomography bring the need to explore denoising methods. Recent techniques adopt Ultra-Low-Dose Computed Tomography (ULDCT) to improve patient safety; however, achieving diagnostically sufficient images requires removing a substantially larger amount of noise. This work evaluates different denoising methods using a ULDCT reconstructed dataset. The evaluated architectures range from convolutional networks to diffusion-based models. Among all evaluated models, CoreDiff achieved the best image quality, reaching an SSIM of 0.8385, a result also confirmed by visual inspection. Our results can serve as benchmarks for future studies on ULDCT denoising approaches.Referências
American Association of Physicists in Medicine (2016). Low Dose CT Grand Challenge. [link]. Accessed: Apr. 24, 2026.
Chen, H., Zhang, Y., Kalra, M. K., Lin, F., Chen, Y., Liao, P., Zhou, J., and Wang, G. (2017). Low-dose ct with a residual encoder-decoder convolutional neural network. IEEE transactions on medical imaging, 36(12):2524–2535.
Chen, Z., Gao, Q., Li, Z., Zhang, J., Zhang, Y., Zhao, J., and Shan, H. (2026). Founddiff: Foundational diffusion model for generalizable low-dose ct denoising. IEEE Transactions on Medical Imaging.
Demir, A. et al. (2023). Low-dose ct image enhancement using deep learning. arXiv preprint arXiv:2310.20265.
dos Santos Junior, J. A., JansonNey, M. S. C., and de Souza Fonseca, G. V. (2020). Dose efetiva de radiação nos exames de tomografia computadorizada: um estudo retrospectivo e descritivo. Diagnóstico e Tratamento, 25(2):46–51.
Du, W. et al. (2024). Structure-aware diffusion for low-dose ct imaging. Physics in Medicine & Biology, 69(15):155008.
Gao, Q., Chen, Z., Zeng, D., Zhang, J., Ma, J., and Shan, H. (2025). Noise-inspired diffusion model for generalizable low-dose ct reconstruction. Medical image analysis, 105:103710.
Gao, Q., Li, Z., Zhang, J., Zhang, Y., and Shan, H. (2023). Corediff: Contextual error-modulated generalized diffusion model for low-dose ct denoising and generalization. IEEE Transactions on Medical Imaging, 43(2):745–759.
Hu, Q.-j. et al. (2021). Prospective study of low-and standard-dose chest ct for pulmonary nodule detection: a comparison of image quality, size measurements and radiation exposure. Current Medical Science, 41:966–973.
Jo, G. D. et al. (2023). 75% radiation dose reduction using deep learning reconstruction on low-dose chest ct. BMC Medical Imaging, 23(1):121.
Junda, M., Muller, H., and Friedrich-Nel, H. (2021). Local diagnostic reference levels for routine chest x-ray examinations at a public sector hospital in central south africa. Health SA Gesondheid (Online), 26:1–8.
Li, Y., Shao, H.-C., Liang, X., Chen, L., Li, R., Jiang, S., Wang, J., and Zhang, Y. (2023). Zero-shot medical image translation via frequency-guided diffusion models. IEEE transactions on medical imaging, 43(3):980–993.
Liang, T., Jin, Y., Li, Y., and Wang, T. (2020). Edcnn: Edge enhancement-based densely connected network with compound loss for low-dose ct denoising. In 2020 15th IEEE International conference on signal processing (ICSP), volume 1, pages 193–198. IEEE.
Palenstijn, W. J., Batenburg, K. J., and Sijbers, J. (2011). Performance improvements for iterative electron tomography reconstruction using graphics processing units (gpus). Journal of Structural Biology, 176(2):250–253.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
Sun, Y., Schneider, L.-S., Mei, S., Wang, J., Hu, G., Gu, M., Ye, C., Wagner, F., Song, L., Bayer, S., et al. (2026). Filter2noise: a framework for interpretable and zero-shot low-dose ct image denoising. Journal of Medical Imaging, 13(2):024004.
Usui, K., Ogawa, K., Goto, M., Sakano, Y., Kyougoku, S., and Daida, H. (2021). Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography. Visual Computing for Industry, Biomedicine, and Art, 4(1):21.
van Aarle, W., Palenstijn, W. J., Cant, J., Janssens, E., Bleichrodt, F., Dabravolski, A., De Beenhouwer, J., Batenburg, K. J., and Sijbers, J. (2016). Fast and flexible x-ray tomography using the astra toolbox. Optics Express, 24(22):25129–25147.
van Aarle, W., Palenstijn, W. J., De Beenhouwer, J., Altantzis, T., Bals, S., Batenburg, K. J., and Sijbers, J. (2015). The astra toolbox: A platform for advanced algorithm development in electron tomography. Ultramicroscopy, 157:35–47.
Wang, B., Deng, F., Jiang, P., Wang, S., Han, X., and Zhang, Z. (2024). Witunet: A u-shaped architecture integrating cnn and transformer for improved feature alignment and local information fusion. Scientific Reports, 14(1):25525.
Wang, D., Fan, F., Wu, Z., Liu, R., Wang, F., and Yu, H. (2023). Ctformer: convolutionfree token2token dilated vision transformer for low-dose ct denoising. Physics in Medicine & Biology, 68(6):065012.
Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra, M. K., Zhang, Y., Sun, L., and Wang, G. (2018). Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE transactions on medical imaging, 37(6):1348–1357.
Yang, S., Pu, Q., Lei, C., Zhang, Q., Jeon, S., and Yang, X. (2023). Low-dose ct denoising with a high-level feature refinement and dynamic convolution network. Medical Physics, 50(6):3597–3611.
Zhang, S.-Y., Wang, Z.-X., Yang, H.-B., Chen, Y.-L., Li, Y., Pan, Q., Wang, H.-K., and Zhao, C.-X. (2023). Hformer: highly efficient vision transformer for low-dose ct denoising. Nuclear Science and Techniques, 34(4):61.
Zhao, H., Gu, Y., Zhao, Z., Du, B., Xu, Y., and Yu, R. (2024). Wia-ld2nd: Wavelet-based image alignment for self-supervised low-dose ct denoising. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 764–774. Springer.
Chen, H., Zhang, Y., Kalra, M. K., Lin, F., Chen, Y., Liao, P., Zhou, J., and Wang, G. (2017). Low-dose ct with a residual encoder-decoder convolutional neural network. IEEE transactions on medical imaging, 36(12):2524–2535.
Chen, Z., Gao, Q., Li, Z., Zhang, J., Zhang, Y., Zhao, J., and Shan, H. (2026). Founddiff: Foundational diffusion model for generalizable low-dose ct denoising. IEEE Transactions on Medical Imaging.
Demir, A. et al. (2023). Low-dose ct image enhancement using deep learning. arXiv preprint arXiv:2310.20265.
dos Santos Junior, J. A., JansonNey, M. S. C., and de Souza Fonseca, G. V. (2020). Dose efetiva de radiação nos exames de tomografia computadorizada: um estudo retrospectivo e descritivo. Diagnóstico e Tratamento, 25(2):46–51.
Du, W. et al. (2024). Structure-aware diffusion for low-dose ct imaging. Physics in Medicine & Biology, 69(15):155008.
Gao, Q., Chen, Z., Zeng, D., Zhang, J., Ma, J., and Shan, H. (2025). Noise-inspired diffusion model for generalizable low-dose ct reconstruction. Medical image analysis, 105:103710.
Gao, Q., Li, Z., Zhang, J., Zhang, Y., and Shan, H. (2023). Corediff: Contextual error-modulated generalized diffusion model for low-dose ct denoising and generalization. IEEE Transactions on Medical Imaging, 43(2):745–759.
Hu, Q.-j. et al. (2021). Prospective study of low-and standard-dose chest ct for pulmonary nodule detection: a comparison of image quality, size measurements and radiation exposure. Current Medical Science, 41:966–973.
Jo, G. D. et al. (2023). 75% radiation dose reduction using deep learning reconstruction on low-dose chest ct. BMC Medical Imaging, 23(1):121.
Junda, M., Muller, H., and Friedrich-Nel, H. (2021). Local diagnostic reference levels for routine chest x-ray examinations at a public sector hospital in central south africa. Health SA Gesondheid (Online), 26:1–8.
Li, Y., Shao, H.-C., Liang, X., Chen, L., Li, R., Jiang, S., Wang, J., and Zhang, Y. (2023). Zero-shot medical image translation via frequency-guided diffusion models. IEEE transactions on medical imaging, 43(3):980–993.
Liang, T., Jin, Y., Li, Y., and Wang, T. (2020). Edcnn: Edge enhancement-based densely connected network with compound loss for low-dose ct denoising. In 2020 15th IEEE International conference on signal processing (ICSP), volume 1, pages 193–198. IEEE.
Palenstijn, W. J., Batenburg, K. J., and Sijbers, J. (2011). Performance improvements for iterative electron tomography reconstruction using graphics processing units (gpus). Journal of Structural Biology, 176(2):250–253.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
Sun, Y., Schneider, L.-S., Mei, S., Wang, J., Hu, G., Gu, M., Ye, C., Wagner, F., Song, L., Bayer, S., et al. (2026). Filter2noise: a framework for interpretable and zero-shot low-dose ct image denoising. Journal of Medical Imaging, 13(2):024004.
Usui, K., Ogawa, K., Goto, M., Sakano, Y., Kyougoku, S., and Daida, H. (2021). Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography. Visual Computing for Industry, Biomedicine, and Art, 4(1):21.
van Aarle, W., Palenstijn, W. J., Cant, J., Janssens, E., Bleichrodt, F., Dabravolski, A., De Beenhouwer, J., Batenburg, K. J., and Sijbers, J. (2016). Fast and flexible x-ray tomography using the astra toolbox. Optics Express, 24(22):25129–25147.
van Aarle, W., Palenstijn, W. J., De Beenhouwer, J., Altantzis, T., Bals, S., Batenburg, K. J., and Sijbers, J. (2015). The astra toolbox: A platform for advanced algorithm development in electron tomography. Ultramicroscopy, 157:35–47.
Wang, B., Deng, F., Jiang, P., Wang, S., Han, X., and Zhang, Z. (2024). Witunet: A u-shaped architecture integrating cnn and transformer for improved feature alignment and local information fusion. Scientific Reports, 14(1):25525.
Wang, D., Fan, F., Wu, Z., Liu, R., Wang, F., and Yu, H. (2023). Ctformer: convolutionfree token2token dilated vision transformer for low-dose ct denoising. Physics in Medicine & Biology, 68(6):065012.
Yang, Q., Yan, P., Zhang, Y., Yu, H., Shi, Y., Mou, X., Kalra, M. K., Zhang, Y., Sun, L., and Wang, G. (2018). Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE transactions on medical imaging, 37(6):1348–1357.
Yang, S., Pu, Q., Lei, C., Zhang, Q., Jeon, S., and Yang, X. (2023). Low-dose ct denoising with a high-level feature refinement and dynamic convolution network. Medical Physics, 50(6):3597–3611.
Zhang, S.-Y., Wang, Z.-X., Yang, H.-B., Chen, Y.-L., Li, Y., Pan, Q., Wang, H.-K., and Zhao, C.-X. (2023). Hformer: highly efficient vision transformer for low-dose ct denoising. Nuclear Science and Techniques, 34(4):61.
Zhao, H., Gu, Y., Zhao, Z., Du, B., Xu, Y., and Yu, R. (2024). Wia-ld2nd: Wavelet-based image alignment for self-supervised low-dose ct denoising. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 764–774. Springer.
Publicado
19/10/2026
Como Citar
ZAGO, Diogo L.; CONTASSOT, Raíssa X.; RAVAZIO, Rafaela C.; UGHINI, Augusto O.; KUPSSINSKÜ, Lucas S.; BARROS, Rodrigo C..
Evaluation of Denoising Architectures on a Reconstructed Ultra-Low-Dose CT Dataset. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 3143-3154.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.31193.
