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Low-dose CT lung images denoising based on multiscale parallel convolution neural network

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Abstract

The continuous development and wide application of CT in medical practice have raised public concern over the associated radiation dose to the patient. However, reducing the radiation dose may result in increasing the noise and artifacts, which may adversely interfere with the judgment and belief of radiologists. Therefore, we propose a low-dose CT denoising model based on multiscale parallel convolution neural network to improve the visual effect. Residual learning is utilized to reduce the difficulty of network learning, and batch normalization is adopted to solve the problem of performance degradation due to the increase in neural network layers. Specifically, we introduce the dilated convolution to expand the receptive field by inserting weights of zero in the standard convolution kernel, while not increasing the extra parameters. Furthermore, the multiscale parallel method is utilized to extract multiscale detail features from lung images. Compared to the traditional methods such as Wiener filter, NLM, and models based on CNN, e.g., SCNN, DnCNN, our extensive experimental results demonstrate that our proposed model (CT-ReCNN) can not only reduce the LDCT lung images noise level, but also retain more exact information as well.

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References

  1. Smith-Bindman, R., Lipson, J., Marcus, R.: Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch. Intern. Med. 51(3), 783 (2010)

    Google Scholar 

  2. Naidich, D.P., Marshall, C.H., Gribbin, C., et al.: Low-dose CT of the lungs: preliminary observations. Radiology 175(3), 729–731 (1990)

    Article  Google Scholar 

  3. Xie, C.G., Huang, S.M., Beck, M.S., et al.: Electrical capacitance tomography for flow imaging: system model for development of image reconstruction algorithms and design of primary sensors. IEE Proc. G (Circuits Dev. Syst.) 139(1), 89–98 (1992)

    Article  Google Scholar 

  4. Ramírez, R.R., Kopell, B.H., Butson, C.R., et al.: Spectral signal space projection algorithm for frequency domain MEG and EEG denoising, whitening, and source imaging. NeuroImage 56(1), 78–92 (2011)

    Article  Google Scholar 

  5. Bhongade, S., Kourav, D., Rai, R.K., et al.: Review on image denoising based on contourlet domain using adaptive window algorithm. In: IEEE International Conference on Machine Intelligence and Research Advancement, Katra, India (2013)

  6. Smith, P.R., Peters, T.M., Bates, R.H.T.: Image reconstruction from finite numbers of projections. J. Phys. A: Math. Nucl. Gen. 6(6), 361 (2001)

    MATH  Google Scholar 

  7. Manduca, A., Yu, L., Trzasko, J.D., et al.: Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med. Phys. 36(11), 4911–4919 (2009)

    Article  Google Scholar 

  8. Hsieh, J.: Adaptive streak artifact reduction in computed tomography resulting from excessive x-ray photon noise. Med. Phys. 25(11), 2139 (1998)

    Article  Google Scholar 

  9. Kachelriess, M., Watzke, O., Kalender, W.A.: Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT. Med. Phys. 28(4), 475–490 (2001)

    Article  Google Scholar 

  10. Li, Z., Yu, L., Trzasko, J.D., et al.: Adaptive nonlocal means filtering based on local noise level for CT denoising. Med. Phys. 41(1), 011908 (2014)

    Article  Google Scholar 

  11. Jia, L., Zhang, Q., Liu, Y., et al.: A two-step denoising method for low dose computed tomography image via morphological component analysis and non-local means. J. Med. Imaging Health Inf. 9(1), 140–148 (2019)

    Article  Google Scholar 

  12. Liu, Y., Zhang, Y.: Low-dose CT restoration via stacked sparse denoising autoencoders. Neurocomputing. 284, 80–89 (2018)

    Article  Google Scholar 

  13. Yang, Q., Yan, P., Zhang, Y., et al.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348–1357 (2017)

    Article  Google Scholar 

  14. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  15. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Networks 61, 85–117 (2015)

    Article  Google Scholar 

  16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Neural Information Processing Systems (NIPS2012), Lake Tahoe, USA (2012)

  17. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations. Computer Vision & Pattern Recognition (CVPR2015), Boston, USA (2015)

  18. Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Computer Vision & Pattern Recognition (CVPR2015), Boston, USA (2015)

  19. Zhang, K., Zuo, W., Chen, Y., et al.: Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans. Image Pro. 26(7), 3142–3155 (2017)

    Article  MathSciNet  Google Scholar 

  20. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Computer Vision & Pattern Recognition (CVPR2016), Las Vegas, USA (2016)

  21. Salimans, T., Kingma, D.P.: Weight normalization: a simple reparameterization to accelerate training of deep neural networks. In: Neural Information Processing Systems (NIPS2016), Barcelona, Spain (2016)

  22. Isogawa, K., Ida, T., Shiodera, T., et al.: Deep shrinkage convolutional neural network for adaptive noise reduction. IEEE Signal Proc. Lett. 25(2), 224–228 (2017)

    Article  Google Scholar 

  23. Ding, Y., Hu, T.: Low-dose computed tomography scheme incorporating residual learning-based denoising with iterative reconstruction. Electron. Lett. 55(4), 174–176 (2018)

    Article  Google Scholar 

  24. Chen, H., Zhang, Y., Kalra, M.K., et al.: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(2), 2524–2535 (2017)

    Article  Google Scholar 

  25. Jifara, W., Jiang, F., Rho, S., et al.: Medical image denoising using convolutional neural network: a residual learning approach. J. Supercomput. 75(6), 704–718 (2017)

    Google Scholar 

  26. Jin, Y., Jiang, X.B., Wei, Z.K., et al.: Chest X-ray image denoising method based on deep convolution neural network. IET Image Proc. 13(11), 1970–1978 (2019)

    Article  Google Scholar 

  27. Yang, Q., Yan, P., Zhang, Y.: Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348–1357 (2018)

    Article  Google Scholar 

  28. Nah, S., Hyun Kim, T., Mu Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Computer Vision & Pattern Recognition (CVPR2017), Hawaii, USA (2017)

  29. Giachetti, A., Isaia, L., Garro, V.: Multiscale descriptors and metric learning for human body shape retrieval. Vis. Comput. 32(6), 693–703 (2016)

    Article  Google Scholar 

  30. Zhang, H., Liu, G.: Coupled-layer based visual tracking via adaptive kernelized correlation filters. Vis. Comput. 34(1), 41–54 (2018)

    Article  MathSciNet  Google Scholar 

  31. Xi, P., Guan, H., Shu, C., et al.: An integrated approach for medical abnormality detection using deep patch convolutional neural networks. Vis. Comput. 36, 1869–1882 (2020)

    Article  Google Scholar 

  32. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations. In: Computer Vision & Pattern Recognition (CVPR2016), Las Vegas, USA (2016)

  33. Li, H., Zhang, S., Kong, W.: Bilateral counting network for single-image object counting. Vis. Comput. 36, 1693–1704 (2020)

    Article  Google Scholar 

  34. AAPM. (2017). Low Dose CT Grand Challenge. [Online]. Available: http://www.aapm.org/GrandChallenge/LowDoseCT/

  35. Schulz, T.J., Snyder, D.L.: Imaging a randomly moving object from quantum-limited data: applications to image recovery from second-and third-order autocorrelations. J. Opt. Soc. Am. A 8(5), 801–807 (1991)

    Article  Google Scholar 

  36. Kingma, D.P., Salimans, T., Welling, M.: Variational dropout and the local reparameterization trick. In: Neural Information Processing Systems (NIPS2015), Montreal, Canada (2015)

  37. Buades, A., Coll, B., Morel, J.-M.: A review of image denoising algorithms, with a new one. SIAM J. Multiscale Model. Simul. 4(2), 490–530 (2005)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY17F010015.

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  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang Province, China

    Xiaoben Jiang, Yan Jin & Yu Yao

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  1. Xiaoben Jiang
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  2. Yan Jin
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  3. Yu Yao
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Correspondence to Yan Jin.

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The original online version of this article was revised: The photos of Xiaoben Jiang and Yu Yao were swapped.

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Jiang, X., Jin, Y. & Yao, Y. Low-dose CT lung images denoising based on multiscale parallel convolution neural network. Vis Comput 37, 2419–2431 (2021). https://doi.org/10.1007/s00371-020-01996-1

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