Advertisement

Fast Groupwise 4D Deformable Image Registration for Irregular Breathing Motion Estimation

  • Bartłomiej W. PapieżEmail author
  • Daniel R. McGowan
  • Michael Skwarski
  • Geoff S. Higgins
  • Julia A. Schnabel
  • Michael Brady
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10883)

Abstract

Tumor heterogeneity can be assessed quantitatively by analyzing dynamic contrast-enhanced imaging modalities potentially leading to improvement in the diagnosis and treatment of cancer, for example of the lung. However, the acquisition of standard lung sequences is often compromised by irregular breathing motion artefacts, resulting in unsystematic errors when estimating tissue perfusion parameters. In this work, we illustrate implicit deformable image registration that integrates the Demons algorithm using the local correlation coefficient as a similarity measure, and locally adaptive regularization that enables incorporation of both spatial sliding motions and irregular temporal motion patterns. We also propose a practical numerical approximation of the regularization model to improve both computational time and registration accuracy, which are important when analyzing long clinical sequences. Our quantitative analysis of 4D lung Computed Tomography and Computed Tomography Perfusion scans from clinical lung trial shows significant improvement over state-of-the-art pairwise registration approaches.

Notes

Acknowledgments

We acknowledge funding from the CRUK/EPSRC Cancer Imaging Centre in Oxford. The ATOM trial is sponsored by the University of Oxford and coordinated by the Oncology Clinical Trials Office. It is supported by the Howat Foundation, Oxford Cancer Imaging Centre, Cancer Research UK, National Institute of Health Research, Oxford Biomedical Research Centre and the ECMC. BWP acknowledges Oxford NIHR Biomedical Research Centre (Rutherford Fund).

References

  1. 1.
    Bai, W., Brady, M.: Regularized B-spline deformable registration for respiratory motion correction in PET images. Phys. Med. Biol. 54(9), 2719 (2009)CrossRefGoogle Scholar
  2. 2.
    Castillo, E., Castillo, R., Martinez, J., Shenoy, M., Guerrero, T.: Four-dimensional deformable image registration using trajectory modeling. Phys. Med. Biol. 55(1), 305 (2009)CrossRefGoogle Scholar
  3. 3.
    Castillo, R., Castillo, E., Guerra, R., Johnson, V., McPhail, T., Garg, A., Guerrero, T.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys. Med. Biol. 54, 1849–1870 (2009)CrossRefGoogle Scholar
  4. 4.
    Craciunescu, O.I., Yoo, D.S., Cleland, E., Muradyan, N., Carroll, M.D., MacFall, J.R., Barboriak, D.P., Brizel, D.M.: Dynamic contrast-enhanced MRI in head-and-neck cancer: the impact of region of interest selection on the intra-and interpatient variability of pharmacokinetic parameters. Int. J. Radiat. Oncol. Biol. Phys. 82(3), e345–e350 (2012)CrossRefGoogle Scholar
  5. 5.
    García-Figueiras, R., Goh, V.J., Padhani, A.R., Baleato-González, S., Garrido, M., León, L., Gómez-Caamaño, A.: CT perfusion in oncologic imaging: a useful tool? Am. J. Roentgenol. 200(1), 8–19 (2013)CrossRefGoogle Scholar
  6. 6.
    Geng, X., Christensen, G.E., Gu, H., Ross, T.J., Yang, Y.: Implicit reference-based group-wise image registration and its application to structural and functional MRI. Neuroimage 47(4), 1341–1351 (2009)CrossRefGoogle Scholar
  7. 7.
    Godenschweger, F., Kägebein, U., Stucht, D., Yarach, U., Sciarra, A., Yakupov, R., Lüsebrink, F., Schulze, P., Speck, O.: Motion correction in MRI of the brain. Phys. Med. Biol. 61(5), R32 (2016)CrossRefGoogle Scholar
  8. 8.
    He, K., Sun, J.: Fast guided filter. arXiv preprint arXiv:1505.00996 (2015)
  9. 9.
    He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)CrossRefGoogle Scholar
  10. 10.
    Koyama, H., Ohno, Y., Seki, S., Nishio, M., Yoshikawa, T., Matsumoto, S., Sugimura, K.: Magnetic resonance imaging for lung cancer. J. Thorac. Imaging 28(3), 138–150 (2013)CrossRefGoogle Scholar
  11. 11.
    Lorenzi, M., Ayache, N., Frisoni, G.B., Pennec, X., Alzheimer’s Disease Neuroimaging Initiative (ADNI): LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm. Neuroimage 81, 470–483 (2013)CrossRefGoogle Scholar
  12. 12.
    McClelland, J.R., Blackall, J.M., Tarte, S., Chandler, A.C., Hughes, S., Ahmad, S., Landau, D.B., Hawkes, D.J.: A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy. Med. Phys. 33(9), 3348–3358 (2006)CrossRefGoogle Scholar
  13. 13.
    Metz, C.T., Klein, S., Schaap, M., van Walsum, T., Niessen, W.J.: Nonrigid registration of dynamic medical imaging data using nD+ t B-splines and a groupwise optimization approach. Med. Image Anal. 15(2), 238–249 (2011)CrossRefGoogle Scholar
  14. 14.
    Papież, B.W., Franklin, J., Heinrich, M.P., Gleeson, F.V., Schnabel, J.A.: Liver motion estimation via locally adaptive over-segmentation regularization. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 427–434. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_51CrossRefGoogle Scholar
  15. 15.
    Papież, B.W., Heinrich, M.P., Fehrenbach, J., Risser, L., Schnabel, J.A.: An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration. Med. Image Anal. 18(8), 1299–1311 (2014)CrossRefGoogle Scholar
  16. 16.
    Schnabel, J.A., Heinrich, M.P., Papież, B.W., Brady, J.M.: Advances and challenges in deformable image registration: from image fusion to complex motion modelling. Med. Image Anal. 33, 145–148 (2016)CrossRefGoogle Scholar
  17. 17.
    Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013)CrossRefGoogle Scholar
  18. 18.
    Vandemeulebroucke, J., Rit, S., Kybic, J., Clarysse, P., Sarrut, D.: Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs. Med. Phys. 38(1), 166–178 (2011)CrossRefGoogle Scholar
  19. 19.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45, S61–S72 (2009)CrossRefGoogle Scholar
  20. 20.
    von Siebenthal, M., Szekely, G., Gamper, U., Boesiger, P., Lomax, A., Cattin, P.: 4D MR imaging of respiratory organ motion and its variability. Phys. Med. Biol. 52(6), 1547 (2007)CrossRefGoogle Scholar
  21. 21.
    Wu, G., Wang, Q., Shen, D., Alzheimer’s Disease NeuroImaging Initiative, et al.: Registration of longitudinal brain image sequences with implicit template and spatial-temporal heuristics. NeuroImage 59(1), 404–421 (2012)CrossRefGoogle Scholar
  22. 22.
    Yigitsoy, M., Wachinger, C., Navab, N.: Temporal groupwise registration for motion modeling. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 648–659. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22092-0_53CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Bartłomiej W. Papież
    • 1
    • 2
    Email author
  • Daniel R. McGowan
    • 3
    • 4
  • Michael Skwarski
    • 3
  • Geoff S. Higgins
    • 3
  • Julia A. Schnabel
    • 2
    • 5
  • Michael Brady
    • 3
  1. 1.Big Data Institute, Li Ka Shing Centre for Health Information and DiscoveryUniversity of OxfordOxfordUK
  2. 2.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
  3. 3.Department of OncologyUniversity of OxfordOxfordUK
  4. 4.Radiation Physics and ProtectionOxford University Hospitals NHS FTOxfordUK
  5. 5.Department of Biomedical Engineering, School of Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK

Personalised recommendations