A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective

Brenner DJ, Hall EJ. Computed tomography - an increasing source of radiation exposure. N Engl J Med. 2007;357(22):2277–84 (PMID: 18046031).

Article  Google Scholar 

Aharon M, Elad M, Bruckstein A. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process. 2006;54(11):4311–22.

Article  Google Scholar 

Li Z, Li Z, Yu L, Trzasko JD, Lake DS, Blezek DJ, Fletcher JG, McCollough CH, Manduca A. Adaptive nonlocal means filtering based on local noise level for ct denoising. Med Phys. 2014;41(1):011908. https://doi.org/10.1118/1.4851635.

Article  Google Scholar 

Dabov K, Foi A, Katkovnik V, Egiazarian K. Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans Image Process. 2007;16(8):2080–95.

Article  MathSciNet  Google Scholar 

Fumene PF, Vinegoni GJ, Sbarbati A, Weissleder R. Block matching 3d random noise filtering for absorption optical projection tomography. Phys Med Biol. 2010;55(18):5401.

Article  Google Scholar 

Sheng K, Gou S, Jiaolong W, Qi SX. Denoised and texture enhanced mvct to improve soft tissue conspicuity. Med Phys. 2014;41(10): 101916.

Article  Google Scholar 

Kang D, Slomka P, Nakazato R, Woo J, Berman DS, Kuo CCJ, Dey D. Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm. In: Ourselin S, Haynor DR (eds) Medical imaging 2013: image processing. volume 8669. SPIE: International Society for Optics and Photonics; 2013. p. 671–6.

Chen H, Zhang Y, Kalra MK, 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. 2017;36(12):2524–35 (ISSN 1558-254X.).

Article  Google Scholar 

Kang E, Chang W, Yoo J, Ye JC. Deep convolutional framelet denosing for low-dose ct via wavelet residual network. IEEE Trans Med Imaging. 2018;37(6):1358–69 (ISSN 1558-254X).

Article  Google Scholar 

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), 2017; pp 143–146.

Yang W, Zhang H, Yang J, Wu J, Yin X, Chen Y, Shu H, Luo L, Coatrieux G, Gui Z, Feng Q. Improving low-dose ct image using residual convolutional network. IEEE Access. 2017;5:24698–705.

Article  Google Scholar 

Kim B, Han M, Shim H, Baek J. A performance comparison of convolutional neural network-based image denoising methods: the effect of loss functions on low-dose ct images. Med Phys. 2019;46(9):3906–23.

Article  Google Scholar 

Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution. In: Bastian L, Jiri M, Nicu S, Max W (eds) Computer vision – ECCV 2016, pp. 694–711, Cham, 2016. Springer International Publishing. ISBN 978-3-319-46475-6.

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27, pp. 2672–2680. Curran Associates, Inc., 2014.

Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, Kalra MK, 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. 2018;37(6):1348–57.

Article  Google Scholar 

Chen Z, Niu C, Gao Q, Wang G, Shan H. Lit-former: linking in-plane and through-plane transformers for simultaneous ct image denoising and deblurring. IEEE Trans Med Imaging, 2024.

Francis L, Dileesh E. Low dose computed tomography denoising by concentrating noise distribution. In: 2023 14th international conference on computing communication and networking technologies (ICCCNT), pp. 1–6, 2023. https://doi.org/10.1109/ICCCNT56998.2023.10306784.

Su W, Qu Y, Deng C, Wang Y, Zheng F, Chen Z. Enhance generative adversarial networks by wavelet transform to denoise low-dose ct images. In: 2020 IEEE international conference on image processing (ICIP), 2020; pp. 350–354. https://doi.org/10.1109/ICIP40778.2020.9190766.

Zhang Y, Hao D, Lin Y, Sun W, Zhang J, Meng J, Ma F, Guo Y, Lu H, Li G, et al. Structure-preserving low-dose computed tomography image denoising using a deep residual adaptive global context attention network. Quant Imaging Med Surg. 2023;13(10):6528.

Article  Google Scholar 

Gao Q, Li Z, Zhang J, Zhang Y, Shan H. Corediff: contextual error-modulated generalized diffusion model for low-dose ct denoising and generalization. IEEE Trans Med Imaging. 2024;43(2):745–59. https://doi.org/10.1109/TMI.2023.3320812.

Article  Google Scholar 

Jia L, He X, Huang A, Jia B, Gui Z. A densely connected ldct image denoising network based on dual-edge extraction and multi-scale attention under compound loss. J X-Ray Sci Technol, (Preprint):2023; 1–20.

Ruan Y, Yuan Q, Niu C, Li C, Yao Y, Wang G, Teng Y. Qs-adn: quasi-supervised artifact disentanglement network for low-dose ct image denoising by local similarity among unpaired data. Phys Med Biol. 2023;68(20): 205001.

Article  Google Scholar 

Liu Y, Yan R, Liu Y, Zhang P, Chen Y, Gui Z. Enhancement based convolutional dictionary network with adaptive window for low-dose ct denoising. J X-Ray Sci Technol (Preprint):2023; 1–23.

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. 2020;8:67519–29. https://doi.org/10.1109/ACCESS.2020.2986388.

Article  Google Scholar 

Zhang L, Xiong J, Zhou Y. Edge-enhanced dense network based on attention for low-dose ct denoising. In: 2023 8th international conference on image, vision and computing (ICIVC), 2023; pp. 449–454. IEEE.

Yan R, Liu Y, Liu Y, Wang L, Zhao R, Bai Y, Gui Z. Image denoising for low-dose ct via convolutional dictionary learning and neural network. IEEE Trans Comput Imaging. 2023;9:83–93. https://doi.org/10.1109/TCI.2023.3241546.

Article  Google Scholar 

Ye X, Xu Y, Xu R, Kido S, Tomiyama N. Detail- revealing deep low-dose ct reconstruction. In 2020 25th international conference on pattern recognition (ICPR), 2021;pp. 8789–8796. https://doi.org/10.1109/ICPR48806.2021.9412327.

Wang Z, Liu M, Cheng X, Zhu J, Wang X, Gong H, Liu M, Xu L. Self-adaption and texture generation: a hybrid loss function for low-dose ct denoising. J Appl Clin Med Phys. 2023;24(9): e14113.

Article  Google Scholar 

Zhang H, Zhang P, Cheng W, Li S, Yan R, Hou R, Gui Z, Liu Y, Chen Y. Learnable pm diffusion coefficients and reformative coordinate attention network for low dose ct denoising. Phys Med Biol. 2023;68(24): 245017.

Article  Google Scholar 

Kim W, Lee J, Choi J-H. An unsupervised two-step training framework for low-dose computed tomography denoising. Med Phys. 2024;51(2):1127–44.

Article  Google Scholar 

Huang J, Chen K, Ren Y, Sun J, Wang Y, Tao T, Pu X. Cddnet: cross-domain denoising network for low-dose ct image via local and global information alignment. Comput Biol Med, 2023;p. 107219.

Yuan J, Zhou F, Guo Z, Li X, Yu H. Hcformer: hybrid cnn-transformer for ldct image denoising. J Digit Imaging. 2023;36(5):2290–305.

Article  Google Scholar 

Wang S, Liu Y, Zhang P, Chen P, Li Z, Yan R, Li S, Hou R, Gui Z. Compound feature attention network with edge enhancement for low-dose ct denoising. J X-Ray Sci Technol, (Preprint):2023;1–19.

Ma Y, Yan Q, Liu Y, Liu J, Zhang J, Zhao Y. Strunet: perceptual and low-rank regularized transformer for medical image denoising. Med Phys. 2023;50(12):7654–69.

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. 2023;161: 107029.

Article  Google Scholar 

Li Z, Liu Y, Chen Y, Shu H, Lu J, Gui Z. Dual-domain fusion deep convolutional neural network for low-dose ct denoising. J Xray Sci Technol. 2023;31(4):757–75.

Google Scholar 

Mazandarani FN, Babyn P, Alirezaie J. Unext: a low-dose ct denoising unet model with the modified convnext block. In: ICASSP 2023 - 2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 1–5, 2023. https://doi.org/10.1109/ICASSP49357.2023.10095645.

Yang L, Liu H, Shang F, Liu Y. Adaptive non-local generative adversarial networks for low-dose ct image denoising. In: ICASSP 2023 - 2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 1–5, 2023. https://doi.org/10.1109/ICASSP49357.2023.10096998.

Wang J, Tang Y, Wu Z, Du Q, Yao L, Yang X, Li M, Zheng J. A self-supervised guided knowledge distillation framework for unpaired low-dose ct image denoising. Comput Med Imaging Graph. 2023;107: 102237.

Article  Google Scholar 

Lee J, Jeon J, Hong Y, Jeong D, Jang Y, Jeon B, Baek HJ, Cho E, Shim H, Chang H-J. Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising. Comput Biol Med. 2023;159: 106931.

Article  Google Scholar 

Lei W, Yi L, Rui W, Rongbiao Y, Yuhang L, Yang C, Chunfeng Y, Zhiguo G. Improved deep convolutional dictionary learning with no noise parameter for low-dose ct image processing. J X-Ray Sci Technol (Preprint):1–17, 2023d.

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. 2023;36(4):1808–25.

Article  Google Scholar 

Li Q, Li R, Li S, Wang T, Cheng Y, Zhang S, Wu W, Zhao J, Qiang Y, Wang L. Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network. Med Phys. 2024;51(2):1289–312.

Article  Google Scholar 

Liu H, Liao P, Chen H, Zhang Y. Era-wgat: edge-enhanced residual autoencoder with a window-based graph attention convolutional network for low-dose ct denoising. Biomed Opt Express. 2022;13(11):5775–93.

Article  Google Scholar 

Zhu L, Han Y, Xi X, Fu H, Tan S, Liu M, Yang S, Liu C, Li L, Yan B. Stednet: Swin transformer-based encoder-decoder network for noise reduction in low-dose ct. Med Phys. 2023;50(7):4443–58.

Article  Google Scholar 

Huang Z, Chen Z, Quan G, Du Y, Yang Y, Liu X, Zheng H, Liang D, Zhanli H. Deep cascade residual networks (dcrns): optimizing an encoder-decoder convolutional neural network for low-dose ct imaging. IEEE Trans Radiat Plasma Med Sci. 2022;6(8):829–40.

Article  Google Scholar 

Gu J, Ye JC. Adain-based tunable cyclegan for efficient unsupervised low-dose ct denoising. IEEE Trans Comput Imaging. 2021;7:73–85. https://doi.org/10.1109/TCI.2021.3050266.

Article  Google Scholar 

Han M, Shim H, Baek J. Perceptual ct loss: implementing ct image specific perceptual loss for cnn-based low-dose ct denoiser. IEEE Access. 2022;10:62412–22. https://doi.org/10.1109/ACCESS.2022.3182821.

Article  Google Scholar 

Jeon S-Y, Kim W, Choi J-H. Mm-net: Multiframe and multimask-based unsupervised deep denoising for low-dose computed tomography. IEEE Trans Radiat Plasma Med Sci. 2023;7(3):296–306. https://doi.org/10.1109/TRPMS.2022.3224553.

Article  Google Scholar 

Shen J, Luo M, Liu H, Liao P, Chen H, Zhang Y. Mlf-iosc: multi-level fusion network with independent operation search cell for low-dose ct denoising. IEEE Trans Med Imaging. 2022;42(4):1145–58.

Article  Google Scholar 

Li H, Yang X, Yang S, Wang D, Jeon G. Transformer with double enhancement for low-dose ct denoising. IEEE J Biomed Health Inform. 2022;27(10):4660–71.

Article  Google Scholar 

Huang J, Chen K, Sun J, Pu X, Ren Y. Cross domain low-dose ct image denoising with semantic information alignment. In: 2022 IEEE international conference on image processing (ICIP), 2022; pp. 4228–4232. https://doi.org/10.1109/ICIP46576.2022.9897265.

Liu Y, Kang J, Li Z, Zhang Q, Gui Z. Low-dose ct noise reduction based on local total variation and improved wavelet residual cnn. J Xray Sci Technol. 2022;30(6):1229–42.

Google Scholar 

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), 2022; pp. 1548–1551. https://doi.org/10.1109/EMBC48229.2022.9870993.

Tang Y, Du Q, Wang J, Wu Z, Li Y, Li M, Yang X, Zheng J. Ccn-cl: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising. Comput Biol Med. 2022;147: 105759.

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