Advertisement

Modified Hybrid Bronchoscope Tracking Based on Sequential Monte Carlo Sampler: Dynamic Phantom Validation

  • Xióngbiāo Luó
  • Tobias Reichl
  • Marco Feuerstein
  • Takayuki Kitasaka
  • Kensaku Mori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

This paper presents a new hybrid bronchoscope tracking method that uses an electromagnetic position sensor, a sequential Monte Carlo sampler, and its evaluation on a dynamic motion phantom. Since airway deformation resulting from patient movement, respiratory motion, and coughing can significantly affect the rigid registration between electromagnetic tracking and computed tomography (CT) coordinate systems, a standard hybrid tracking approach that initializes intensity-based image registration with absolute pose data acquired by electromagnetic tracking fails when the initial camera pose is too far from the actual pose. We propose a new solution that combines electromagnetic tracking and a sequential Monte Carlo sampler to address this problem. In our solution, sequential Monte Carlo sampling is introduced to recursively approximate the posterior probability distributions of the bronchoscope camera motion parameters in accordance with the observation model based on electromagnetic tracking. We constructed a dynamic phantom that simulates airway deformation to evaluate our proposed solution. Experimental results demonstrate that the challenging problem of airway deformation can be robustly modeled and effectively addressed with our proposed approach compared to a previous hybrid method, even when the maximum simulated airway deformation reaches 23 mm.

Keywords

Camera Motion Sequential Monte Carlo Posterior Probability Distribution World Coordinate System Structure From Motion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bricault, I., et al.: Registration of real and CT-derived virtual bronchoscopic images to assist transbronchial biopsy. IEEE TMI 17, 703–714 (1998)Google Scholar
  2. 2.
    Deguchi, D., et al.: Selective image similarity measure for bronchoscope tracking based on image registration. MedIA 13, 621–633 (2009)Google Scholar
  3. 3.
    Solomon, S.B., et al.: Three-dimensionsal CT-guided bronchoscopy with a real-time electromagnetic position sensor: a comparison of two image registration methods. Chest 118, 1783–1787 (2000)CrossRefGoogle Scholar
  4. 4.
    Gergel, I., et al.: Particle filtering for respiratory motion compensation during navigated bronchoscopy. In: Proceedings of SPIE, vol. 7625 (2010) 76250WGoogle Scholar
  5. 5.
    Deguchi, D., et al.: A method for bronchoscope tracking by combining a position sensor and image registration. Proceedings of CARS 1281, 630–635 (2005)Google Scholar
  6. 6.
    Feuerstein, M., et al.: Magneto-optical tracking of flexible laparoscopic ultrasound: Model-based online detection and correction of magnetic tracking errors. IEEE TMI 28, 951–967 (2009)Google Scholar
  7. 7.
    Mori, K., Deguchi, D., Akiyama, K., Kitasaka, T., Maurer Jr., C.R., Suenaga, Y., Takabatake, H., Mori, M., Natori, H.: Hybrid Bronchoscope Tracking Using a Magnetic Tracking Sensor and Image Registration. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 543–550. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Soper, T.D., et al.: In vivo validation of a hybrid tracking system for navigation of an ultrathin bronchoscope within peripheral airways. IEEE TBME 57, 736–745 (2010)Google Scholar
  9. 9.
    Nagao, J., Mori, K., Enjouji, T., Deguchi, D., Kitasaka, T., Suenaga, Y., Hasegawa, J.-i., Toriwaki, J.-i., Takabatake, H., Natori, H.: Fast and Accurate Bronchoscope Tracking Using Image Registration and Motion Prediction. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 551–558. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Deligianni, F., Chung, A., Zhong, G.: Predictive Camera Tracking for Bronchoscope Simulation with CONDensation. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 910–916. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Luo, X., et al.: Towards hybrid bronchoscope tracking under respiratory motion: evaluation on a dynamic motion phantom. In: Proceedings of SPIE, vol. 7625 (2010) 76251B Google Scholar
  12. 12.
    Liu, J.S., et al.: Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association 93, 1032–1044 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Arulampalam, M., et al.: A tutorial on particle filters for nonlinear/non-gaussian Bayesian tracking. IEEE TSP 50, 174–188 (2002)Google Scholar
  14. 14.
    Moral, P.D., et al.: Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68, 411–436 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Forsyth, D., et al.: The joy of sampling. IJCV 41, 109–134 (2001)CrossRefzbMATHGoogle Scholar
  16. 16.
    Pupilli, M.: Particle filtering for real-time camera localisation. PhD thesis, University of Bristol, UK (2006)Google Scholar
  17. 17.
    Qian, G., et al.: Structure from motion using sequential Monte Carlo methods. IJCV 59, 5–31 (2004)CrossRefGoogle Scholar
  18. 18.
    Doucet, A., et al.: On sequential monte carlo sampling methods for Bayesian filtering. Statistics and Computing 10, 197–208 (2000)CrossRefGoogle Scholar
  19. 19.
    Soper, T.D., et al.: A model of respiratory airway motion for real-time tracking of an ultrathin bronchoscope. In: Proceedings of SPIE, vol. 6511 (2007) 65110M Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xióngbiāo Luó
    • 1
  • Tobias Reichl
    • 2
  • Marco Feuerstein
    • 2
  • Takayuki Kitasaka
    • 3
  • Kensaku Mori
    • 1
    • 4
  1. 1.Graduate School of Information ScienceNagoya UniversityJapan
  2. 2.Computer Aided Medical ProceduresTechnische Universität MünchenGermany
  3. 3.Faculty of Information ScienceAichi Institute of TechnologyJapan
  4. 4.Information and Communications HeadquartersNagoya UniversityJapan

Personalised recommendations