Skip to main content
SpringerLink
Log in

Low-Dose CT Image Denoising with a Residual Multi-scale Feature Fusion Convolutional Neural Network and Enhanced Perceptual Loss

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Computed tomography (CT) stands as a pivotal medical imaging technique, delivering timely and reliable clinical evaluations. Yet, its dependence on ionizing radiation raises health concerns. One mitigation strategy involves using reduced radiation for low-dose CT (LDCT) imaging; however, this often results in noise artifacts that undermine diagnostic precision. To address this issue, a distinctive CT image denoising technique has been introduced that utilizes deep neural networks to suppress image noise. This advanced CT image denoising network employs an attention mechanism for the feature extraction stage, facilitating the adaptive fusion of multi-scale local characteristics and channel-wide dependencies. Furthermore, a novel residual block has been incorporated, crafted to generate features with superior representational abilities, factoring in diverse spatial scales and eliminating redundant features. A unique loss function is also developed to optimize network parameters, focusing on preserving structural information by capturing high-frequency components and perceptually important details. Experimental results demonstrate the effectiveness of the proposed network in enhancing the quality of LDCT images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data Availability

The datasets of this study are available from https://opg.optica.org/oe/fulltext.cfm?uri=oe-18-14-15244 &id=203597#articleDatasets, https://github.com/xinario/SAGAN and https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=52758026.

References

  1. A. Abdelhamed, M. Afifi, R. Timofte, M.S. Brown, Ntire 2020 challenge on real image denoising: Dataset, methods and results, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 496–497 (2020)

  2. A.A. Abdulla, Efficient computer-aided diagnosis technique for leukaemia cancer detection. IET Image Proc. 14(17), 4435–4440 (2020)

    Article  Google Scholar 

  3. M.Z. Alom, T.M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M.S. Nasrin, B.C. Van Esesn, A.A.S. Awwal, V.K. Asari, The history began from alexnet: a comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164 (2018)

  4. Y. Behroozi, M. Yazdi, A.Z. Asli, Hyperspectral image denoising based on superpixel segmentation low-rank matrix approximation and total variation. Circuits Syst. Signal Process. 41(6), 3372–3396 (2022)

    Article  Google Scholar 

  5. D.J. Brenner, C.D. Elliston, E.J. Hall, W.E. Berdon et al., Estimated risks of radiation-induced fatal cancer from pediatric ct. Am. J. Roentgenol. 176(2), 289–296 (2001)

    Article  CAS  Google Scholar 

  6. L. Chao, P. Zhang, Y. Wang, Z. Wang, W. Xu, Q. Li, Dual-domain attention-guided convolutional neural network for low-dose cone-beam computed tomography reconstruction. Knowl. Based Syst. (2022). https://doi.org/10.1016/j.knosys.2022.109295

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  8. W. Chen, Y. Shao, Y. Wang, Q. Zhang, Y. Liu, L. Yao, Y. Chen, G. Yang, Z. Gui, A novel total variation model for low-dose ct image denoising. IEEE Access 6, 78892–78903 (2018)

    Article  Google Scholar 

  9. A. Ferrero, N. Takahashi, T.J. Vrtiska, A.E. Krambeck, J.C. Lieske, C.H. McCollough, Understanding, justifying, and optimizing radiation exposure for ct imaging in nephrourology. Nat. Rev. Urol. 16(4), 231–244 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  10. M.A. Gavrielides, L.M. Kinnard, K.J. Myers, J. Peregoy, W.F. Pritchard, R. Zeng, J. Esparza, J. Karanian, N. Petrick, A resource for the assessment of lung nodule size estimation methods: database of thoracic ct scans of an anthropomorphic phantom. Opt. Express 18(14), 15244–15255 (2010)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  11. M. Gholizadeh-Ansari, J. Alirezaie, P. Babyn, Deep learning for low-dose ct denoising using perceptual loss and edge detection layer. J. Digit. Imaging 33, 504–515 (2020)

    Article  PubMed  Google Scholar 

  12. I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A.C. Courville, Improved training of wasserstein gans. Adv. Neural Inf. Process. Syst. 30 (2017)

  13. D. Hart, M. Hillier, B. Wall, Doses to patients from medical X-ray examinations in the UK. 2000 review (2002)

  14. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  15. J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks

  16. Y. Huo, D. Wang, Y. Qi, P. Lian, A new gaussian kernel filtering algorithm involving the sparse criterion. Circuits Syst. Signal Process. 42(1), 522–539 (2023)

    Article  Google Scholar 

  17. L. Jia, A. Huang, X. He, Z. Li, J. Liang, A residual multi-scale feature extraction network with hybrid loss for low-dose computed tomography denoising. Available at SSRN 4327683

  18. W. Kim, J. Lee, J.-H. Choi, An unsupervised two-step training framework for low-dose computed tomography denoising. Med. Phys. (2023). https://doi.org/10.1002/mp.16628

    Article  PubMed  Google Scholar 

  19. Z. Li, L. Yu, J.D. Trzasko, D.S. Lake, D.J. Blezek, J.G. Fletcher, C.H. McCollough, A. Manduca, Adaptive nonlocal means filtering based on local noise level for ct denoising. Med. Phys. 41(1), 011908 (2014)

    Article  PubMed  Google Scholar 

  20. M. Li, Q. Du, L. Duan, X. Yang, J. Zheng, H. Jiang, M. Li, Incorporation of residual attention modules into two neural networks for low-dose ct denoising. Med. Phys. 48, 2973–2990 (2021). https://doi.org/10.1002/mp.14856

    Article  PubMed  Google Scholar 

  21. K. Li, J.R. Chen, M. Feng, Construction of a nearly unbiased statistical estimator of sinogram to address ct number bias issues in low-dose photon counting ct. IEEE Trans. Med. Imaging (2023). https://doi.org/10.1109/TMI.2023.3240840

    Article  PubMed  PubMed Central  Google Scholar 

  22. J. Liu, W. Zhang, Y. Tang, J. Tang, G. Wu, Residual feature aggregation network for image super-resolution

  23. Y. Liu, H. Chen, Y. Chen, W. Yin, C. Shen, Generic perceptual loss for modeling structured output dependencies

  24. Y. Liu, J. Ma, Y. Fan, Z. Liang, Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction. Phys. Med. Biol. 57(23), 7923 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  25. Y. Liu, Z. Gui, Q. Zhang, Noise reduction for low-dose x-ray ct based on fuzzy logical in stationary wavelet domain. Optik-Int. J. Light Electron Opt. 124(18), 3348–3352 (2013)

    Article  Google Scholar 

  26. J. Liu, J. Tang, G. Wu, Residual feature distillation network for lightweight image super-resolution, in Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16, pp. 41–55 (2020). Springer

  27. P. Luo, X. Qu, X. Qing, J. Gu, Ct image denoising using double density dual tree complex wavelet with modified thresholding, in 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA), pp. 287–290 (2018). IEEE

  28. J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, W. Chen, Low-dose computed tomography image restoration using previous normal-dose scan. Med. Phys. 38(10), 5713–5731 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

  29. L. Ma, H. Xue, G. Yang, Z. Zhang, C. Li, Y. Yao, Y. Teng, Scrdn: Residual dense network with self-calibrated convolutions for low dose ct image denoising. Nucl. Inst. Methods Phys. Res. 1045, 167625 (2023)

    Article  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  31. L. Marcos, J. Alirezaie, P. Babyn, Low dose ct denoising by resnet with fused attention modules and integrated loss functions. Front. Signal Process. 1, 1–11 (2022). https://doi.org/10.3389/frsip.2021.812193

    Article  Google Scholar 

  32. C.H. McCollough, A.N. Primak, N. Braun, J. Kofler, L. Yu, J. Christner, Strategies for reducing radiation dose in CT. Radiol. Clin. (2009). https://doi.org/10.1016/j.rcl.2008.10.006

    Article  Google Scholar 

  33. T.R. Moen, B. Chen, D.R. Holmes III., X. Duan, Z. Yu, L. Yu, S. Leng, J.G. Fletcher, C.H. McCollough, Low-dose ct image and projection dataset. Med. Phys. (2021). https://doi.org/10.7937/9NPB-2637

    Article  PubMed  Google Scholar 

  34. P.A. Oakley, D.E. Harrison, Death of the alara radiation protection principle as used in the medical sector. Dose-Response 18(2), 1559325820921641 (2020)

    Article  PubMed  PubMed Central  Google Scholar 

  35. Y. Pathak, K. Arya, S. Tiwari, Low-dose ct image reconstruction using gain intervention-based dictionary learning. Mod. Phys. Lett. B 32(14), 1850148 (2018)

    Article  ADS  MathSciNet  CAS  Google Scholar 

  36. K. Rao, M. Bansal, G. Kaur, An effective ct medical image enhancement system based on dt-cwt and adaptable morphology. Circuits Syst. Signal Process. 42(2), 1034–1062 (2023)

    Article  Google Scholar 

  37. D.S. Rigie, A.A. Sanchez, P.J. La Rivière, Assessment of vectorial total variation penalties on realistic dual-energy ct data. Phys. Med. Biol. 62(8), 3284 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  38. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in 3rd International Conference on Learning Representations, ICLR 2015—Conference Track Proceedings, pp. 1–14 (2015)

  39. M. Su, J. Zheng, Y. Yang, Q. Wu, A new multipath mitigation method based on adaptive thresholding wavelet denoising and double reference shift strategy. GPS Sol. 22, 1–12 (2018)

    Article  Google Scholar 

  40. Z. Tian, X. Jia, K. Yuan, T. Pan, S.B. Jiang, Low-dose ct reconstruction via edge-preserving total variation regularization. Phys. Med. Biol. 56(18), 5949 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

  41. P. Wang, Y. Li, A. Research, N. Vasconcelos, S. Diego, Rethinking and improving the robustness of image style transfer

  42. J. Wang, H. Lu, T. Li, Z. Liang, Sinogram noise reduction for low-dose ct by statistics-based nonlinear filters, in Medical Imaging 2005: Image Processing, vol. 5747, pp. 2058–2066 (2005). SPIE

  43. J. Wang, Y. Tang, Z. Wu, B.M.W. Tsui, W. Chen, X. Yang, J. Zheng, M. Li, Domain-adaptive denoising network for low-dose ct via noise estimation and transfer learning. Med. Phys. 50, 74–88 (2023). https://doi.org/10.1002/mp.15952

    Article  PubMed  Google Scholar 

  44. Q. Yang, P. Yan, S. Member, Y. Zhang, Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37, 1348–1357 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  45. X. Yi, P. Babyn, Sharpness-aware low-dose ct denoising using conditional generative adversarial network. J. Digit. Imaging 31, 655–669 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  46. Y. Zhang, J. Zhang, H. Lu, Statistical sinogram smoothing for low-dose ct with segmentation-based adaptive filtering. IEEE Trans. Nucl. Sci. 57(5), 2587–2598 (2010)

    Article  ADS  Google Scholar 

  47. J. Zhang, H.L. Zhou, Y. Niu, J.C. Lv, J. Chen, Y. Cheng, Cnn and multi-feature extraction based denoising of ct images. Biomed. Signal Process. Control 67, 102545 (2021). https://doi.org/10.1016/j.bspc.2021.102545

    Article  Google Scholar 

  48. Y.-D. Zhang, Z. Zhang, X. Zhang, S.-H. Wang, Midcan: a multiple input deep convolutional attention network for covid-19 diagnosis based on chest ct and chest X-ray. Pattern Recogn. Lett. 150, 8–16 (2021)

    Article  ADS  Google Scholar 

  49. J. Zhang, J. Lv, Y. Cheng, A novel denoising method for medical ct images based on moving decomposition framework. Circuits Syst. Signal Process. 41(12), 6885–6905 (2022)

    Article  Google Scholar 

  50. M. Zhang, S. Gu, Y. Shi, The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review. Compl. Intel. Syst. 8(6), 5545–5561 (2022)

    Article  Google Scholar 

  51. J. Zhang, Y. Niu, Z. Shangguan, W. Gong, Y. Cheng, A novel denoising method for ct images based on u-net and multi-attention. Comput. Biol. Med. 152, 106387 (2023). https://doi.org/10.1016/j.compbiomed.2022.106387

    Article  PubMed  Google Scholar 

  52. P. Zhang, Y. Liu, Z. Gui, Y. Chen, L. Jia, A region-adaptive non-local denoising algorithm for low-dose computed tomography images. Math. Biosci. Eng. 20(2), 2831–2846 (2023)

    Article  PubMed  Google Scholar 

  53. T. Zhang, D. Wu, X. Mo, The rank residual constraint model with weighted schatten p-norm minimization for image denoising. Circuits Syst. Signal Process. pp. 1–19 (2023)

  54. Y. Zhang, M.A. Khan, Z. Zhu, S. Wang, Snelm: Squeezenet-guided elm for covid-19 recognition. Comput. Syst. Sci. Eng. 46(1), 13 (2023)

    Article  PubMed  PubMed Central  Google Scholar 

  55. W. Zhao, H. Lu, Medical image fusion and denoising with alternating sequential filter and adaptive fractional order total variation. IEEE Trans. Instrum. Meas. 66(9), 2283–2294 (2017)

    Article  ADS  Google Scholar 

  56. T. Zhao, M. McNitt-Gray, D. Ruan, A convolutional neural network for ultra-low-dose ct denoising and emphysema screening. Med. Phys. 46(9), 3941–3950 (2019)

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

Download references

Funding

The funding was provided by NSERC Discovery grant (RGPIN-2020-04441).

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B2K3, Canada

    Farzan Niknejad Mazandarani & Javad Alirezaie

  2. Department of Medical Imaging, University of Saskatchewan and Saskatoon Health Region, Saskatoon, SK, Canada

    Paul Babyn

  3. Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada

    Javad Alirezaie

Authors
  1. Farzan Niknejad Mazandarani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul Babyn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Javad Alirezaie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Javad Alirezaie.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Niknejad Mazandarani, F., Babyn, P. & Alirezaie, J. Low-Dose CT Image Denoising with a Residual Multi-scale Feature Fusion Convolutional Neural Network and Enhanced Perceptual Loss. Circuits Syst Signal Process 43, 2533–2559 (2024). https://doi.org/10.1007/s00034-023-02575-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-023-02575-0

Keywords

Search

Navigation