Complex Lung Motion Estimation via Adaptive Bilateral Filtering of the Deformation Field

  • Bartlomiej W. Papież
  • Mattias Paul Heinrich
  • Laurent Risser
  • Julia A. Schnabel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8151)

Abstract

Estimation of physiologically plausible deformations is critical for several medical applications. For example, lung cancer diagnosis and treatment requires accurate image registration which preserves sliding motion in the pleural cavity, and the rigidity of chest bones. This paper addresses these challenges by introducing a novel approach for regularisation of non-linear transformations derived from a bilateral filter. For this purpose, the classic Gaussian kernel is replaced by a new kernel that smoothes the estimated deformation field with respect to the spatial position, intensity and deformation dissimilarity. The proposed regularisation is a spatially adaptive filter that is able to preserve discontinuity between the lungs and the pleura and reduces any rigid structures deformations in volumes. Moreover, the presented framework is fully automatic and no prior knowledge of the underlying anatomy is required. The performance of our novel regularisation technique is demonstrated on phantom data for a proof of concept as well as 3D inhale and exhale pairs of clinical CT lung volumes. The results of the quantitative evaluation exhibit a significant improvement when compared to the corresponding state-of-the-art method using classic Gaussian smoothing.

Keywords

nonrigid registration respiratory motion sliding motion modeling adaptive bilateral filtering 

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References

  1. 1.
    Baluwala, H., Risser, L., Schnabel, J.A., Saddi, K.: Toward physiologically motivated registration of diagnostic CT and PET/CT of lung volumes. Med. Phys. 40, 021903 (2013)Google Scholar
  2. 2.
    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
  3. 3.
    Ehrhardt, J., Werner, R., Schmidt-Richberg, A., Handels, H.: Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration. IEEE Trans. Med. Imag. 30, 251–265 (2011)CrossRefGoogle Scholar
  4. 4.
    Hermosillo, G., Chefd’Hotel, C., Faugeras, O.: Variational Methods for Multimodal Image Matching. Int. J. Comput. Vision 50, 329–343 (2002)CrossRefMATHGoogle Scholar
  5. 5.
    Mansi, T., Pennec, X., Sermesant, M., Delingette, H., Ayache, N.: iLogDemons: A Demons-Based Registration Algorithm for Tracking Incompressible Elastic Biological Tissues. Int. J. Comput. Vision 92, 92–111 (2011)CrossRefGoogle Scholar
  6. 6.
    Pace, D.F., Enquobahrie, A., Yang, H., Aylward, S.R., Niethammer, M.: Deformable image registration of sliding organs using anisotropic diffusive regularization. In: IEEE ISBI, pp. 407–413 (2011)Google Scholar
  7. 7.
    Risser, L., Vialard, F.X., Baluwala, H., Schnabel, J.A.: Piecewise-diffeomorphic image registration: Application to the motion estimation between 3D CT lung images with sliding conditions. Med. Image Anal. 17, 182–193 (2013)CrossRefGoogle Scholar
  8. 8.
    Ruan, D., Esedoglu, S., Fessler, J.A.: Discriminative Sliding Preserving Regularization in Medical Image Registration. In: IEEE ISBI, pp. 430–433 (2009)Google Scholar
  9. 9.
    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
  10. 10.
    Segars, W.P.: Development and application of the new dynamic NURBS-based cardiac-torso (NCAT) phantom. PhD thesis, University of North Carolina (2001)Google Scholar
  11. 11.
    Staring, M., Klein, S., Pluim, J.P.W.: Nonrigid registration with tissue-dependent filtering of the deformation field. Phys. Med. Biol. 52, 6879–6892 (2007)CrossRefGoogle Scholar
  12. 12.
    Thirion, J.P.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2, 243–260 (1998)CrossRefGoogle Scholar
  13. 13.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: IEEE ICCV, pp. 839–846 (1998)Google Scholar
  14. 14.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic Demons: Efficient non-parametric image registration. NeuroImage 45, 61–72 (2009)CrossRefGoogle Scholar
  15. 15.
    Xiao, J., Cheng, H., Sawhney, H., Rao, C., Isnardi, M.: Bilateral filtering-based optical flow estimation with occlusion detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 211–224. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Zimmer, H., Bruhn, A., Weickert, J.: Optic Flow in Harmony. Int. J. Comput. Vision 93, 368–388 (2011)CrossRefMathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bartlomiej W. Papież
    • 1
  • Mattias Paul Heinrich
    • 1
  • Laurent Risser
    • 2
  • Julia A. Schnabel
    • 1
  1. 1.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordUK
  2. 2.CNRSInstitut de Mathématiques de Toulouse (UMR5219)France

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