A Hybrid Framework of Dual-Domain Signal Restoration and Multi-depth Feature Reinforcement for Low-Dose Lung CT Denoising

Pearce, M.S., Salotti, J.A., Little, M.P., McHugh, K., Lee, C., Kim, K.P., Howe, N.L., Ronckers, C.M., Rajaraman, P., Craft, A.W., et al: Radiation exposure from ct scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study. The Lancet 380(9840), 499–505 (2012)

Balda, M., Hornegger, J., Heismann, B.: Ray contribution masks for structure adaptive sinogram filtering. IEEE transactions on medical imaging 31(6), 1228–1239 (2012)

Manduca, A., Yu, L., Trzasko, J.D., Khaylova, N., Kofler, J.M., McCollough, C.M., Fletcher, J.G.: Projection space denoising with bilateral filtering and ct noise modeling for dose reduction in ct. Medical physics 36(11), 4911–4919 (2009)

Wang, J., Li, T., Lu, H., Liang, Z.: Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose x-ray computed tomography. IEEE transactions on medical imaging 25(10), 1272–1283 (2006)

Yin, X., Zhao, Q., Liu, J., Yang, W., Yang, J., Quan, G., Chen, Y., Shu, H., Luo, L., Coatrieux, J.-L.: Domain progressive 3d residual convolution network to improve low-dose ct imaging. IEEE transactions on medical imaging 38(12), 2903–2913 (2019)

Bruno, D.M., Samit, B.: Distance-driven projection and backprojection in three dimensions. Physics in Medicine and Biology 49(11), 2463–2475 (2004)

Ramani, S., Fessler, J.A.: A splitting-based iterative algorithm for accelerated statistical x-ray ct reconstruction. IEEE Transactions on Medical Imaging 31(3), 677–688 (2012)

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 transactions on medical imaging 37(6), 1348–1357 (2018)

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 transactions on medical imaging 36(12), 2524–2535 (2017)

Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence 12(7), 629–639 (1990)

Aharon, M., Elad, M., Bruckstein, A.: K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on signal processing 54(11), 4311–4322 (2006)

Ren, C., He, X., Wang, C., Zhao, Z.: Adaptive consistency prior based deep network for image denoising. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8596–8606 (2021)

Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence 38(2), 295–307 (2015)

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer

Huang, Z., Zhang, J., Zhang, Y., Shan, H.: Du-gan: Generative adversarial networks with dual-domain u-net based discriminators for low-dose ct denoising. arXiv preprint arXiv:2108.10772 (2021)

Wang, D., Fan, F., Wu, Z., Liu, R., Wang, F., Yu, H.: Ctformer: Convolution-free token2token dilated vision transformer for low-dose ct denoising. arXiv preprint arXiv:2202.13517 (2022)

Feng, Z., Cai, A., Wang, Y., Li, L., Tong, L., Yan, B.: Dual residual convolutional neural network (drcnn) for low-dose ct imaging. Journal of X-Ray Science and Technology 29(1), 91–109 (2021)

Li, M., Hsu, W., Xie, X., Cong, J., Gao, W.: Sacnn: Self-attention convolutional neural network for low-dose ct denoising with self-supervised perceptual loss network. IEEE transactions on medical imaging 39(7), 2289–2301 (2020)

Huang, Z., Liu, Z., He, P., Ren, Y., Li, S., Lei, Y., Luo, D., Liang, D., Shao, D., Hu, Z., et al.: Segmentation-guided denoising network for low-dose ct imaging. Computer Methods and Programs in Biomedicine, 107199 (2022)

Chen, H., Zhang, Y., Zhang, W., Liao, P., Li, K., Zhou, J., Wang, G.: Low-dose ct denoising with convolutional neural network. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 143–146 (2017)

Sidky, E.Y., Pan, X.: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Physics in Medicine & Biology 53(17), 4777 (2008)

Kang, E., Min, J., Ye, J.C.: A deep convolutional neural network using directional wavelets for low-dose x-ray ct reconstruction. Medical physics 44(10), 360–375 (2017)

Marcos, L., Quint, F., Babyn, P., Alirezaie, J.: Dilated convolution resnet with boosting attention modules and combined loss functions for ldct image denoising. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1548–1551 (2022). IEEE

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)

Kulathilake, K., Abdullah, N.A., Sabri, A.Q.M., Lai, K.W.: A review on deep learning approaches for low-dose computed tomography restoration. Complex & Intelligent Systems, 1–33 (2021)

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

Kang, E., Koo, H.J., Yang, D.H., Seo, J.B., Ye, J.C.: Cycle-consistent adversarial denoising network for multiphase coronary ct angiography. Medical Physics 46(2), 550–562 (2019)

Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2242–2251 (2017)

Shan, H., Zhang, Y., Yang, Q., Kruger, U., Kalra, M.K., Sun, L., Cong, W., Wang, G.: 3-d convolutional encoder-decoder network for low-dose ct via transfer learning from a 2-d trained network. IEEE transactions on medical imaging 37(6), 1522–1534 (2018)

Green, M., Marom, E.M., Konen, E., Kiryati, N., Mayer, A.: 3-d neural denoising for low-dose coronary ct angiography (ccta). Computerized Medical Imaging and Graphics 70, 185–191 (2018)

Gunduzalp, D., Cengiz, B., Unal, M.O., Yildirim, I.: 3d u-netr: Low dose computed tomography reconstruction via deep learning and 3 dimensional convolutions. arXiv preprint arXiv:2105.14130 (2021)

Wang, H., Zhao, X., Liu, W., Li, L.C., Ma, J., Guo, L.: Texture-aware dual domain mapping model for low-dose ct reconstruction. Medical Physics (2022)

Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 424–432 (2016). Springer

Bera, S., Biswas, P.K.: Self supervised low dose computed tomography image denoising using invertible network exploiting inter slice congruence. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 5614–5623 (2023)

Chi, J., Sun, Z., Wang, H., Lyu, P., Yu, X., Wu, C.: Ct image super-resolution reconstruction based on global hybrid attention. Computers in Biology and Medicine 150, 106112 (2022)

Tong, T., Li, G., Liu, X., Gao, Q.: Image super-resolution using dense skip connections. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4799–4807 (2017)

Zhang, J., Cao, L., Wang, T., Fu, W., Shen, W.: Nhnet: A non-local hierarchical network for image denoising. IET Image Processing 16(9), 2446–2456 (2022)

Setio, A.A.A., Traverso, A., De Bel, T., Berens, M.S., Van Den Bogaard, C., Cerello, P., Chen, H., Dou, Q., Fantacci, M.E., Geurts, B., et al: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. Medical image analysis 42, 1–13 (2017)

McCollough, C.H., Bartley, A.C., Carter, R.E., Chen, B., Drees, T.A., Edwards, P., Holmes III, D.R., Huang, A.E., Khan, F., Leng, S., et al: Low-dose ct for the detection and classification of metastatic liver lesions: results of the 2016 low dose ct grand challenge. Medical physics 44(10), 339–352 (2017)

Xu, Q., Zhang, C., Zhang, L.: Denoising convolutional neural network. In: 2015 IEEE International Conference on Information and Automation, pp. 1184–1187 (2015). IEEE

Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep cnn denoiser prior for image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3929–3938 (2017)

Tai, Y., Yang, J., Liu, X., Xu, C.: Memnet: A persistent memory network for image restoration. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4539–4547 (2017)

Anwar, S., Barnes, N.: Real image denoising with feature attention. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3155–3164 (2019)

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