Real Time Simulation of Organ Motions Induced by Breathing: First Evaluation on Patient Data

  • A. Hostettler
  • S. A. Nicolau
  • C. Forest
  • L. Soler
  • Y. Remond
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4072)


In this paper we present a new method to predict in real time from a preoperative CT image the internal organ motions of a patient induced by his breathing. This method only needs the segmentation of the bones, viscera and lungs in the preoperative image and a tracking of the patient skin motion. Prediction of internal organ motions is very important for radiotherapy since it can allow to reduce the healthy tissue irradiation. Moreover, guiding system for punctures in interventional radiology would reduce significantly their guidance inaccuracy. In a first part, we analyse physically the breathing motion and show that it is possible to predict internal organ motions from the abdominal skin position. Then, we propose an original method to compute from the skin position a deformation field to the internal organs that takes mechanical properties of the breathing into account. Finally, we show on human data that our simulation model can provide a prediction of several organ positions (liver, kidneys, lungs) at 14 Hz with an accuracy within 7 mm.


Augmented Reality Organ Motion Real Time Simulation Breathing Motion Deep Inspiration Breath Hold 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arun, K.S., Huang, T.S., Blostein, S.D.: Least squares fitting of two 3d point sets. IEEE Transactions on Pattern analysis and machine intelligence 9(5), 698–700 (1987)CrossRefGoogle Scholar
  2. 2.
    Balter, J.M., Lam, K.L., McGinn, C.J., Lawrence, T.S., Ten Haken, R.K.: Improvement of CT-based treatment-planning models of abdominals targets using static exhale imaging. Int. J. Radiation Oncology Biol. Phys. 41(4), 939–943 (1998)CrossRefGoogle Scholar
  3. 3.
    Bornemann, L., Kuhnigk, J., Dicken, V., Zidowitz, S., Wormanns, D.: OncoTREAT: A software assistant for oncological therapy monitoring. In: Computer Assisted Radiology and Surgery, pp. 429–434 (2005)Google Scholar
  4. 4.
    Clifford, M., Banovac, F., Levy, E., Cleary, K.: Assessment of hepatic motion secondary to respiration for computer assisted interventions. Computer Aided Surgery 7, 291–299 (2002)CrossRefGoogle Scholar
  5. 5.
    Cotin, S., Delingette, H., Ayache, N.: A hybrid elastic model allowing real-time cutting, deformations and force-feedback for surgery training and simulation. The Visual Computer 16(8), 437–452 (2000)MATHCrossRefGoogle Scholar
  6. 6.
    Fiala, M.: Artag, an improved marker system based on artoolkit. NRC/ERB-1111 NRC 47166, National Research Council Canada (July 2004)Google Scholar
  7. 7.
    Delingette, H.: Efficient linear elastic models of soft tissues for real-time surgery simulation. In: MMVR 7 (Medicine Meets Virtual Reality), pp. 139–151 (1999)Google Scholar
  8. 8.
    Schwartz, J.-M.: Modelling liver tissue properties using a non-linear visco-elastic model for surgery simulation. Medical Image Analysis 9(2), 103–112 (2005)CrossRefGoogle Scholar
  9. 9.
    Nicolau, S., Goffin, L., Soler, L.: A low cost and accurate guidance system for laparoscopic surgery: Validation on an abdominal phantom. In: Proceedings of ACM Symposium on Virtual Reality Software and Technology (VRST 2005), Monterey (page Accepted, 2005)Google Scholar
  10. 10.
    Nicolau, S., Pennec, X., Soler, L., Ayache, N.: A complete augmented reality guidance system for liver punctures: First clinical evaluation. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Remouchamps, V., Vicini, F., Sharpe, M., Kestin, L., Martinez, A., Wong, J.: Significant reductions in heart and lung doses using deep inspiration breath hold with active breathing control and intensity-modulated radiation therapy for patients treated with locoregional breast irradiation. Int. J. Radiation Oncology Biol. Phys. 55, 392–406 (2003)CrossRefGoogle Scholar
  12. 12.
    Sarrut, D., Boldea, V., Miguet, S., Ginestet, C.: Simulation of 4d ct images from deformable registration between inhale and exhale breath-hold ct scans. Medical physics 33(3), 605–617 (2006)CrossRefGoogle Scholar
  13. 13.
    Secomb, T.: A theoretical model for the elastic properties of very soft tissues. Biorheology 38(4), 305–317 (2001)Google Scholar
  14. 14.
    Soler, L., Nicolau, S., Schmid, J., Pennec, X., Koehl, C., Ayache, N., Marescaux, J.: Virtual reality and augmented reality in digestive surgery. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2004) (November 2004)Google Scholar
  15. 15.
    Kühnapfel, U.: Endoscopic surgery training using virtual reality and deformable tissue simulation. Computer and Graphics 24(5), 671–682 (2000)CrossRefGoogle Scholar
  16. 16.
    Wacker, F., Vogt, S., Khamene, A., Jesberger, J., Nour, S., Elghort, D., Sauer, F., Duerk, J., Lewin, J.: An augmented reality system for mr image-guided needle biopsy: Initial results in a swine model. Radiology 238(2), 497–504 (2006)CrossRefGoogle Scholar
  17. 17.
    Wong, J., Sharpe, M., Jaffray, D., Kini, V., Robertson, J., Stromberg, J., Martinez, A.: The use of active breathing control (abc) to reduce margin for breathing motion. Int. J. Radiation Oncology Biol. Phys. 44(4), 911–919 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. Hostettler
    • 1
  • S. A. Nicolau
    • 1
  • C. Forest
    • 1
  • L. Soler
    • 1
  • Y. Remond
    • 2
  1. 1.IRCAD-Hopital CivilStrasbourg
  2. 2.Institut de Mécanique des Fluides et des SolidesStrasbourg

Personalised recommendations