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XeMRI to CT Lung Image Registration Enhanced with Personalized 4DCT-Derived Motion Model

  • Adam SzmulEmail author
  • Tahreema Matin
  • Fergus V. Gleeson
  • Julia A. Schnabel
  • Vicente Grau
  • Bartłomiej W. Papież
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

This paper presents a novel method for multi-modal lung image registration constrained by a motion model derived from lung 4DCT. The motion model is estimated based on the results of intra-patient image registration using Principal Component Analysis. The approach with a prior motion model is particularly important for regions where there is not enough information to reliably drive the registration process, as in the case of hyperpolarized Xenon MRI and proton density MRI to CT registration. Simultaneously, the method addresses local variations between images in the supervoxel-based motion model parameters optimization step. We compare our results in terms of the plausibility of the estimated deformations and correlation coefficient with 4DCT-based estimated ventilation maps using state-of-the-art multi-modal image registration methods. Our method achieves higher average correlation scores, showing that the application of Principal Component Analysis-based motion model in the deformable registration, helps to drive the registration for the regions of the lungs with insufficient amount of information.

Keywords

Lung 4D CT XeMRI Multi-modal image registration Lung motion model Ventilation estimation 

Notes

Acknowledgments

AS and BWP would like to acknowledge funding from the CRUK and EPSRC Cancer Imaging Centre in Oxford. BWP acknowledges Oxford NIHR Biomedical Research Centre (Rutherford Fund Fellowship at HDR UK). JAS was supported by EP/P023509/1 and Wellcome Trust/EPSRC Centre for Medical Engineering.

References

  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Albert, M.S., et al.: Biological magnetic resonance imaging using laser-polarized \(^{129}Xe\). Nature 370(6486), 199–201 (1994)CrossRefGoogle Scholar
  3. 3.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  4. 4.
    Castillo, R., et al.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys. Med. Biol. 54(7), 1849–1870 (2009)CrossRefGoogle Scholar
  5. 5.
    Ehrhardt, J., Werner, R., Schmidt-Richberg, A., Handels, H.: Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration. IEEE Trans. Med. Imaging 30, 251–265 (2011)CrossRefGoogle Scholar
  6. 6.
    Glocker, B., Sotiras, A., Komodakis, N., Paragios, N.: Deformable medical image registration: Setting the state of the art with discrete methods. Ann. Rev. Biomed. Eng. 13(1), 219–244 (2011)CrossRefGoogle Scholar
  7. 7.
    Gorbunova, V., et al.: Mass preserving image registration for lung CT. Med. Image Anal. 16(4), 786–795 (2012)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Guerrero, T., et al.: Quantification of regional ventilation from treatment planning CT. Int. J. Radiat. Oncol.*Biol.*Phys. 62(3), 630–634 (2005)CrossRefGoogle Scholar
  9. 9.
    Han, L., Dong, H., McClelland, J.R., Han, L., Hawkes, D.J., Barratt, D.C.: A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs. Med. Image Anal. 39, 87–100 (2017)CrossRefGoogle Scholar
  10. 10.
    Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE Trans. Med. Imag. 32(7), 1239–1248 (2013)CrossRefGoogle Scholar
  11. 11.
    Heinrich, M.P., Simpson, I.J., Papież, B.W., Brady, S.M., Schnabel, J.A.: Deformable image registration by combining uncertainty estimates from supervoxel belief propagation. Med. Image Anal. 27, 57–71 (2016)CrossRefGoogle Scholar
  12. 12.
    Hermosillo, G., Chefd’Hotel, C., Faugeras, O.: Variational methods for multimodal image matching. Int. J. Comput. Vis. 50(3), 329–343 (2002)CrossRefGoogle Scholar
  13. 13.
    Liu, X., Oguz, I., Pizer, S.M., Mageras, G.S.: Shape-correlated deformation statistics for respiratory motion prediction in 4D lung (2010)Google Scholar
  14. 14.
    Matin, T.N., et al.: Chronic obstructive pulmonary disease: lobar analysis with hyperpolarized 129 Xe MR imaging. Radiology 282, 857–868 (2016)CrossRefGoogle Scholar
  15. 15.
    McClelland, J.R., Hawkes, D.J., Schaeffter, T., King, A.P.: Respiratory motion models: a review. Med. Image Anal. 17(1), 19–42 (2013)CrossRefGoogle Scholar
  16. 16.
    Mugler, J.P., Altes, T.A.: Hyperpolarized \(^{129}Xe\) MRI of the human lung. J. Magn. Reson. Imaging 37(2), 313–31 (2013)CrossRefGoogle Scholar
  17. 17.
    Murphy, K., et al.: Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans. Med. Imaging 30(11), 1901–1920 (2011)CrossRefGoogle Scholar
  18. 18.
    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), 1299–311 (2014)CrossRefGoogle Scholar
  19. 19.
    Papież, B.W., Szmul, A., Grau, V., Brady, J.M., Schnabel, J.A.: Non-local graph-based regularization for deformable image registration. In: MICCAI RAMBO, pp. 199–207 (2017)Google Scholar
  20. 20.
    Reinhardt, J.M., Ding, K., Cao, K., Christensen, G.E., Hoffman, E.A., Bodas, S.V.: Registration-based estimates of local lung tissue expansion compared to xenon ct measures of specific ventilation. Med. Image Anal. 12(6), 752–763 (2008)CrossRefGoogle Scholar
  21. 21.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–21 (1999)CrossRefGoogle Scholar
  22. 22.
    Schmidt-Richberg, A., Werner, R., Handels, H., Ehrhardt, J.: Estimation of slipping organ motion by registration with direction-dependent regularization. Med. Image Anal. 16, 150–159 (2012)CrossRefGoogle Scholar
  23. 23.
    Schnabel, J.A., Heinrich, M.P., Papież, B.W., Brady, S.M.: Advances and challenges in deformable image registration: From image fusion to complex motion modelling. Med. Image Anal. 33(10), 145–148 (2016)CrossRefGoogle Scholar
  24. 24.
    Szmul, A., Papież, B.W., Matin, T., Gleeson, F., Schnabel, J.A., Grau, V.: Regional lung ventilation estimation based on supervoxel tracking. In: Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10576, pp. 10576-1–10576-7 (2018)Google Scholar
  25. 25.
    Szmul, A., Papież, B.W., Hallack, A., Grau, V., Schnabel, J.A.: Supervoxels for graph cuts-based deformable image registration using guided image filtering. J. Electron. Imaging 26(6), 061607 (2017)CrossRefGoogle Scholar
  26. 26.
    Wild, J.M., et al.: Automatic image registration of lung CT and hyperpolarized helium-3 MRI via mutual information of proton MRI. NMR Biomed. 24(2), 130–134 (2011)CrossRefGoogle Scholar
  27. 27.
    Yamamoto, T., et al.: Investigation of four-dimensional computed tomography-based pulmonary ventilation imaging in patients with emphysematous lung regions. Phys. Med. Biol. 56(7), 2279–98 (2011)CrossRefGoogle Scholar
  28. 28.
    Yin, Y., Hoffman, E.A., Lin, C.L.: Mass preserving nonrigid registration of CT lung images using cubic B-spline. Med. Phys. 36(9), 4213–4222 (2009)CrossRefGoogle Scholar
  29. 29.
    Zhang, Q., et al.: A patient specific respiratory model of anatomical motion for radiation treatment planning. Med. Phys. 34(12), 4772–4781 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Adam Szmul
    • 1
    Email author
  • Tahreema Matin
    • 2
  • Fergus V. Gleeson
    • 2
    • 3
  • Julia A. Schnabel
    • 1
    • 4
  • Vicente Grau
    • 1
  • Bartłomiej W. Papież
    • 1
    • 5
  1. 1.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.Department of OncologyUniversity of OxfordOxfordUK
  3. 3.Department of RadiologyOxford University Hospitals NHS FTOxfordUK
  4. 4.Department of Biomedical Engineering, School of Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK
  5. 5.Big Data Institute, Li Ka Shing Centre for Health Information and DiscoveryUniversity of OxfordOxfordUK

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