Learning Real Noise for Ultra-Low Dose Lung CT Denoising

  • Michael Green
  • Edith M. Marom
  • Eli Konen
  • Nahum Kiryati
  • Arnaldo MayerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11075)


Neural image denoising is a promising approach for quality enhancement of ultra-low dose (ULD) CT scans after image reconstruction. The availability of high-quality training data is instrumental to its success. Still, synthetic noise is generally used to simulate the ULD scans required for network training in conjunction with corresponding normal dose scans. This reductive approach may be practical to implement but ignores any departure of the real noise from the assumed model. In this paper, we demonstrate the training of denoising neural networks with real noise. For this purpose, a special training set is created from a pair of ULD and normal-dose scans acquired on each subject. Accurate deformable registration is computed to ensure the required pixel-wise overlay between corresponding ULD and normal-dose patches. To our knowledge, it is the first time real CT noise is used for the training of denoising neural networks. The benefits of the proposed approach in comparison to synthetic noise training are demonstrated both qualitatively and quantitatively for several state-of-the art denoising neural networks. The obtained results prove the feasibility and applicability of real noise learning as a way to improve neural denoising of ULD lung CT.


  1. 1.
    Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)Google Scholar
  2. 2.
    Green, M., Marom, E.M., Kiryati, N., Konen, E., Mayer, A.: A neural regression framework for low-dose Coronary CT Angiography (CCTA) denoising. In: Wu, G., Munsell, B.C., Zhan, Y., Bai, W., Sanroma, G., Coupé, P. (eds.) Patch-MI 2017. LNCS, vol. 10530, pp. 102–110. Springer, Cham (2017). Scholar
  3. 3.
    Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26, 3142–3155 (2017)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Ahn, B., Cho, N.I.: Block-matching convolutional neural network for image denoising. arXiv preprint arXiv:1704.00524 (2017)
  5. 5.
    Chen, H., et al.: Low-dose CT via convolutional neural network. Biomed. Opt. Express 8(2), 679–694 (2017)CrossRefGoogle Scholar
  6. 6.
    Yang, Q., Yan, P., Kalra, M.K., Wang, G.: CT image denoising with perceptive deep neural networks. arXiv preprint arXiv:1702.07019 (2017)
  7. 7.
    Chen, H., et al.: Low-dose CT with a residual encoder-decoder convolutional neural network (RED-CNN). arXiv preprint arXiv:1702.00288 (2017)
  8. 8.
    Yi, X., Babyn, P.: Sharpness-aware low dose CT denoising using conditional generative adversarial network. arXiv preprint arXiv:1708.06453 (2017)
  9. 9.
    Green, M., Marom, E.M., Kiryati, N., Konen, E., Mayer, A.: Efficient low-dose CT denoising by locally-consistent non-local means (LC-NLM). In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 423–431. Springer, Cham (2016). Scholar
  10. 10.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (2015)Google Scholar
  11. 11.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  13. 13.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)Google Scholar
  14. 14.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)CrossRefGoogle Scholar
  15. 15.
    Sokooti, H., Saygili, G., Glocker, B., Lelieveldt, B.P.F., Staring, M.: Accuracy estimation for medical image registration using regression forests. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 107–115. Springer, Cham (2016). Scholar
  16. 16.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  17. 17.
    van Aarle, W., et al.: Fast and flexible X-ray tomography using the ASTRA toolbox. Opt. Express 24(22), 25129–25147 (2016)CrossRefGoogle Scholar
  18. 18.
    Liu, P., Fang, R.: Wide inference network for image denoising. arXiv preprint arXiv:1707.05414 (2017)
  19. 19.
    Vu, C.T., Phan, T.D., Chandler, D.M.: S3: a spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans. Image Process. 21(3), 934–945 (2012)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Michelson, A.A.: Studies in Optics. Courier Corporation (1995)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Michael Green
    • 1
  • Edith M. Marom
    • 2
  • Eli Konen
    • 2
  • Nahum Kiryati
    • 1
  • Arnaldo Mayer
    • 2
    Email author
  1. 1.Department of Electrical EngineeringTel-Aviv UniversityTel AvivIsrael
  2. 2.Diagnostic Imaging, Sheba Medical Center, Affiliated to the Sackler School of MedicineTel-Aviv UniversityTel AvivIsrael

Personalised recommendations