Advertisement

A U-Nets Cascade for Sparse View Computed Tomography

  • Andreas KoflerEmail author
  • Markus Haltmeier
  • Christoph Kolbitsch
  • Marc Kachelrieß
  • Marc Dewey
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11074)

Abstract

We propose a new convolutional neural network architecture for image reconstruction in sparse view computed tomography. The proposed network consists of a cascade of U-nets and data consistency layers. While the U-nets address the undersampling artifacts, the data consistency layers model the specific scanner geometry and make direct use of measured data. We train the network cascade end-to-end on sparse view cardiac CT images. The proposed network’s performance is evaluated according to different quantitative measures and compared to the one of a cascade with fully convolutional neural networks with residual connections and to the one of a single U-net with approximately the same number of trainable parameters. While in both experiments the methods show similar performance in terms of quantitative measures, our proposed U-nets cascade yields superior visual results and better preserves the overall image structure as well as fine diagnostic details, e.g. the coronary arteries. The latter is also confirmed by a statistically significant increase of the Haar-wavelet-based perceptual similarity index measure in all the experiments.

Keywords

Deep learning Convolutional neural networks Data consistency Computed tomography Sparse sampling 

Notes

Acknowledgements

The authors would like to thank the reviewers for the helpful feedback. A. Kofler acknowledges support of the German Research Foundation (DFG), project number GRK 2260, BIOQIC. M. Haltmeier acknowledges support of the Austrian Science Fund (FWF), project P 30747-N32.

References

  1. 1.
    Jonas, A.: ODL - operator discretization library (2013). https://github.com/odlgroup/odl
  2. 2.
    Adler, J., Öktem, O.: Solving ill-posed inverse problems using iterative deep neural networks. Inverse Prob. 33(12), 124007 (2017)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Hammernik, K., et al.: Learning a variational network for reconstruction of accelerated MRI data. Magn. Reson. Med. 79(6), 3055–3071 (2018)CrossRefGoogle Scholar
  4. 4.
    Jin, K.H., McCann, M.T., Froustey, E., Unser, M.: Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process. 26(9), 4509–4522 (2017)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Reisenhofer, R., Bosse, S., Kutyniok, G., Wiegand, T.: A Haar wavelet-based perceptual similarity index for image quality assessment. Signal Process. Image Commun. 61, 33–43 (2018)CrossRefGoogle Scholar
  6. 6.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  7. 7.
    Schlemper, J., Caballero, J., Hajnal, J.V., Price, A., Rueckert, D.: A deep cascade of convolutional neural networks for MR image reconstruction. In: Niethammer, M. (ed.) IPMI 2017. LNCS, vol. 10265, pp. 647–658. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59050-9_51CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Andreas Kofler
    • 1
    Email author
  • Markus Haltmeier
    • 2
  • Christoph Kolbitsch
    • 3
    • 4
  • Marc Kachelrieß
    • 5
  • Marc Dewey
    • 1
  1. 1.Department of RadiologyCharité - Universitätsmedizin BerlinBerlinGermany
  2. 2.Department of MathematicsUniversity of InnsbruckInnsbruckAustria
  3. 3.Physikalisch-Technische BundesanstaltBraunschweig and BerlinGermany
  4. 4.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK
  5. 5.Medical Physics in Radiology, German Cancer Research CenterHeidelbergGermany

Personalised recommendations