Abstract
Keeping the number of projection views constant and reducing the radiation dose at each view is an effective way to achieve low-dose CT. This will make the reconstructed image contain high-intensity noise, which will affect subsequent image processing, analysis and diagnosis. Currently, deep learning has shown promising performance in medical image denoising. However, Transformer solely relies on a single self-attention mechanism, which fails to consider attention computation from multiple perspectives, thus limiting the performance of the model. In this paper, we propose a multi-attention coupled, U-shaped Transformer (MA-Uformer) to achieve high-performance denoising of low-dose CT images. The MA-Uformer network comprehensively uses the local information association capability of convolutional neural network (CNN) and the global information capture capability of Transformer. It integrates pixel attention mechanism, channel attention mechanism, and spatial attention mechanism to construct a coupled architecture based on multiple attention mechanisms. Compared with the four existing representative denoising networks, the network has better denoising performance and stronger ability to preserve image details.
Similar content being viewed by others
Availability of data and materials
Data will be made available on request.
References
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. Magaz. 40, 89–100 (2023)
Zhou, B., Chen, X.C., Xie, H.D., Zhou, S.K., Duncan, J.S., Liu, C.: Dudoufnet: Dual-domain under-to-fully-complete progressive restoration network for simultaneous metal artifact reduction and low-dose ct reconstruction. IEEE Trans. Med. Imag. 41, 3587–3599 (2022)
Xia, W., Lu, Z., Huang, Y., Shi, Z., Liu, Y., Chen, H., Chen, Y., Zhou, J., Zhang, Y.: Magic: Manifold and graph integrative convolutional network for low-dose ct reconstruction. IEEE Trans. Med. Imag. 40, 3459–3472 (2021)
Shan, H., Padole, A., Homayounieh, F., Kruger, U., Khera, R.D., Nitiwarangkul, C., Kalra, M.K., Wang, G.: Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose ct image reconstruction. Nat. Mach. Imag 1, 269–276 (2019)
Jiao, F.Y., Gui, Z.G., Liu, Y., Yao, L.H., Zhang, P.C.: Low-dose ct image denoising via frequency division and encoder-dual decoder gan. Signal Image Video Process. 15, 1907–1915 (2021)
Liu, H., Liao, P.X., 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. Expresss 13, 5775–5793 (2022)
Wang, D., Fan, F., Wu, Z., Liu, R., Wang, F., Yu, H.: Ctformer: convolution-free token2token dilated vision transformer for low-dose ct denoising. Phys. Med. Biol. 68, 065012 (2023)
Zhu, L., Han, Y., Xi, X., Fu, H., Tan, S., Liu, M., Yang, S., Liu, C., Li, L., and Yan, B. J.: Stednet: Swin transformer‐based encoder–decoder network for noise reduction in low‐dose ct. Med. Phys. (2023)
Jin, K.H., Mccann, M.T., Froustey, E., Unser, M.J.: Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Imag. Process 26, 4509–4522 (2017)
Zhao, T.T., Mcnitt-Gray, M., Ruan, D.: A convolutional neural network for ultra-low-dose ct denoising and emphysema screening. Med. Phys. 46, 3941–3950 (2019)
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. Imag. 37, 1348–1357 (2018)
Liu, P., Fang, R. .G.: Sdcnet: Smoothed dense-convolution network for restoring low-dose cerebral ct perfusion. in 15th IEEE International Symposium on Biomedical Imaging (ISBI), pp 349–352 (2018)
Zhang, K., Zuo, W.M., Chen, Y.J., Meng, D.Y., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 26, 3142–3155 (2017)
Chen, H., Zhang, Y., Kalra, M.K., Lin, F., Chen, Y., Liao, P.X., Zhou, J.L., Wang, G.: Low-dose ct with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imag. 36, 2524–2535 (2017)
Zhong, A.N., Li, B., Luo, N., Xu, Y., Zhou, L.H., Zhen, X.: Image restoration for low-dose ct via transfer learning and residual network. IEEE Access 8, 112078–112091 (2020)
Chen, M., Pu, Y.F., Bai, Y.C.: Low-dose ct image denoising using residual convolutional network with fractional tv loss. Neurocomputing 452, 510–520 (2021)
Trung, N.T., Trinh, D.H., Trung, N.L., Luong, M.: Low-dose ct image denoising using deep convolutional neural networks with extended receptive fields. Signal Image Video Process. 16, 1963–1971 (2022)
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I.: Attention is all you need. in 31st Annual Conference on Neural Information Processing Systems (NIPS), pp 5999–6009 (2017)
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S.J.: An image is worth 16x16 words: Transformers for image recognition at scale. (2020)
Liu, Z., Lin, Y.T., Cao, Y., Hu, H., Wei, Y.X., Zhang, Z., Lin, S., Guo, B.N.: Swin transformer: Hierarchical vision transformer using shifted windows. In: 18th IEEE/CVF International Conference on Computer Vision (ICCV), pp 9992–10002 (2021)
Wang, Z.D., Cun, X.D., Bao, J.M., Zhou, W.G., Liu, J.Z., Li, H.Q.: Uformer: A general u-shaped transformer for image restoration. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 17662–17672 (2022)
Sobel, I., and Feldman, GJaTaTSaPI: A 3x3 isotropic gradient operator for image processing. a talk at the Stanford Artificial Project in 271–272 (1968)
Luthra, A., Sulakhe, H., Mittal, T., Iyer, A., and Yadav, SJaPA: Eformer: Edge enhancement based transformer for medical image denoising. arXiv preprint arXiv:2109.08044 (2021)
Liang, J.Y., Cao, J.Z., Sun, G.L., Zhang, K., Van Gool, L., Timofte, R., Soc IC: Swinir: Image restoration using swin transformer. in 18th IEEE/CVF International Conference on Computer Vision (ICCV), pp 1833–1844 (2021)
Peng, J., Li, X,, and Zhang, X: Mdnt: A multi-scale denoising transformer beyond real noisy image denoising. in 2022 7th International Conference on Electronic Technology and Information Science (ICETIS), pp 13–17 (2022)
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q.: Eca-net: Efficient channel attention for deep convolutional neural networks. in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 11531–11539 (2020)
Dong, X.Y., Bao, J.M., Chen, D.D., Zhang, W.M., Yu, N.H., Yuan, L., Chen, D., Guo, B.N.: Cswin transformer: A general vision transformer backbone with cross-shaped windows. in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 12114–12124 (2022)
Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer perceptron) - a review of applications in the atmospheric sciences. Atmos. Environ. 32, 2627–2636 (1998)
Sanghyun, W., Jongchan, P., Joon-Young, L., In S: Cbam: Convolutional block attention module. in 15th European Conference on Computer Vision (ECCV), pp 3–19 (2018)
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.: Low-dose ct for the detection and classification of metastatic liver lesions: results of the 2016 low dose ct grand challenge. Med. Phys. 44, e339–e352 (2017)
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE Trans. Pattern Anal. Mach. Intell. 41, 2599–2613 (2019)
Tian, Y.J., Zhang, Y.Q., Zhang, H.B.: Recent advances in stochastic gradient descent in deep learning. Mathematics 11, 23 (2023)
Huynh-Thu, Q., Ghanbari, M.: Scope of validity of psnr in image/video quality assessment. Electron. Lett. 44, 800-U835 (2008)
Hore, A., and Ziou, D.: Image quality metrics: Psnr vs. Ssim. in 2010 20th international conference on pattern recognition, pp 2366–2369 (2010)
Chai, T., Draxler, R.R.: Root mean square error (rmse) or mean absolute error (mae)? - arguments against avoiding rmse in the literature. Geosci. Model Dev. 7, 1247–1250 (2014)
Funding
This work was supported in part by the Natural Science Foundation of China under grant 62071281, by the Central Guidance on Local Science and Technology Development Fund Project under grant YDZJSX2021A003, and by the Research Project Supported by Shanxi Scholarship Council of China under grant 2020–008.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by HY, CF and ZQ. The first draft of the manuscript was written by HY, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Ethical approval
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yan, H., Fang, C. & Qiao, Z. A multi-attention Uformer for low-dose CT image denoising. SIViP 18, 1429–1442 (2024). https://doi.org/10.1007/s11760-023-02853-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-023-02853-z