MRI Denoising Using Deep Learning

  • José V. ManjónEmail author
  • Pierrick Coupe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11075)


MRI denoising is a classical preprocessing step which aims at reducing the noise naturally present in MR images. In this paper, we present a new method for MRI denoising that combines recent advances in deep learning with classical approaches for noise reduction. Specifically, the proposed method follows a two-stage strategy. The first stage is based on an overcomplete patch-based convolutional neural network that blindly removes the noise without estimation of local noise level present in the images. The second stage uses this filtered image as a guide image within a rotationally invariant non-local means filter. The proposed approach has been compared with related state-of-the-art methods and showed competitive results in all the studied cases.



This research was supported by the Spanish DPI2017-87743-R grant from the Ministerio de Economia, Industria y Competitividad of Spain. This study has been also carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project) and Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57). The authors gratefully acknowledge the support of NVIDIA Corporation with their donation of the TITAN X GPU used in this research.


  1. 1.
    Mohan, J., Krishnaveni, V., Guo, Y.: A survey on the magnetic resonance image denoising methods. Biomed. Signal Process. Control 9, 56–69 (2014)CrossRefGoogle Scholar
  2. 2.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE International Conference on Computer Vision and Pattern Recognition (CPVR), vol. 2, pp. 60–65 (2005)Google Scholar
  3. 3.
    Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imaging 27, 425–441 (2008)CrossRefGoogle Scholar
  4. 4.
    Manjón, J.V., et al.: MRI denoising using non-local means. Med. Image Anal. 4, 514–523 (2008)CrossRefGoogle Scholar
  5. 5.
    He, L., Greenshields, I.R.: A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images. IEEE Trans. Med. Imaging 28, 165–172 (2009)CrossRefGoogle Scholar
  6. 6.
    Guleryuz, O.G.: Weighted overcomplete denoising. In: Proceedings of the Asilomar Conference on Signals and Systems (2003)Google Scholar
  7. 7.
    Manjón, J.V., Coupé, P., Buades, A.: MRI noise estimation and denoising using non-local PCA. Med. Image Anal. 22(1), 35–47 (2015)CrossRefGoogle Scholar
  8. 8.
    Gondara, L.: Medical image denoising using convolutional denoising autoencoders. In: Proceedings of the ICDMW, pp. 241–246 (2016)Google Scholar
  9. 9.
    Benou, A., Veksler, R., Friedman, A., Riklin, R.T.: Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences. Med. Image Anal. 42, 145–159 (2017)CrossRefGoogle Scholar
  10. 10.
    Jiang, D., Dou, W., Vosters, L., Xu, X., Sun, Y., Tan, T.: Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network (2018). arXiv:1712.08726v2
  11. 11.
    Collins, D.L., et al.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imaging 17, 463–468 (1998)CrossRefGoogle Scholar
  12. 12.
    Maggioni, M., Katkovnik, V., Egiazarian, K., Foi, A.: Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans. Image Process. 22(1), 119–133 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Crown Copyright is asserted by the Australian Government 2018

Authors and Affiliations

  1. 1.Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA)Universitat Politècnica de ValènciaValenciaSpain
  2. 2.Univ. Bordeaux, LaBRI, UMR 5800, PICTURATalenceFrance
  3. 3.CNRS, LaBRI, UMR 5800, PICTURATalenceFrance

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