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Correlating Chest Surface Motion to Motion of the Liver Using ε-SVR – A Porcine Study

  • Floris Ernst
  • Volker Martens
  • Stefan Schlichting
  • Armin Beširević
  • Markus Kleemann
  • Christoph Koch
  • Dirk Petersen
  • Achim Schweikard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

In robotic radiosurgery, the compensation of motion of internal organs is vital. This is currently done in two phases: an external surrogate signal (usually active optical markers placed on the patient’s chest) is recorded and subsequently correlated to an internal motion signal obtained using stereoscopic X-ray imaging. This internal signal is sampled very infrequently to minimise the patient’s exposure to radiation. We have investigated the correlation of the external signal to the motion of the liver in a porcine study using ε-support vector regression. IR LEDs were placed on the swines’ chest. Gold fiducials were placed in the swines’ livers and were recorded using a two-plane X-ray system. The results show that a very good correlation model can be built using ε-SVR, in this test clearly outperforming traditional polynomial models by at least 45 and as much as 74 %. Using multiple markers simultaneously can increase the new model’s accuracy.

Keywords

Polynomial Model Correlation Model Tracking Camera Porcine Study Robotic Radiosurgery 
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.

References

  1. 1.
    Adler, J.R., Schweikard, A., Murphy, M.J., Hancock, S.L.: Image-guided stereotactic radiosurgery: The CyberKnife. In: Image-guided neurosurgery: clinical applications of surgical navigation, pp. 193–204. Quality Medical Publishing (1998)Google Scholar
  2. 2.
    Schweikard, A., Glosser, G., Bodduluri, M., et al.: Robotic motion compensation for respiratory motion during radiosurgery. J. Comput. Aided Surg. 5(4), 263–277 (2000)CrossRefGoogle Scholar
  3. 3.
    Gierga, D.P., Brewer, J., Sharp, G.C., et al.: The correlation between internal and external markers for abdominal tumors: Implications for respiratory gating. Int. J. Radiat. Oncol. Biol. Phys. 61(5), 1551–1558 (2005)Google Scholar
  4. 4.
    Sayeh, S., Wang, J., Main, W.T., et al.: Respiratory Motion Tracking for Robotic Radiosurgery. In: Urschel, H.C. (ed.) Robotic Radiosurgery. Treating Tumors that Move with Respiration, 1st edn., pp. 15–30. Springer, Berlin (2007)Google Scholar
  5. 5.
    Drucker, H., Burges, C.J.C., Kaufman, L., et al.: Support vector regression machines. In: Advances in Neural Information Processing Systems. NIPS, vol. 9, pp. 155–161. MIT Press, Cambridge (1997)Google Scholar
  6. 6.
    Knöpke, M., Ernst, F.: Flexible Markergeometrien zur Erfassung von Atmungs und Herzbewegungen an der Körperoberfläche. In: 7. CURAC Jahrestagung, Leipzig, Germany, September 24-26, pp. 15–16 (2008)Google Scholar
  7. 7.
    DeMenthon, D.F., Davis, L.S.: Model-based object pose in 25 lines of code. Int. J. Comput. Vision 15(1-2), 123–141 (1995)CrossRefGoogle Scholar
  8. 8.
    Chang, C.C., Lin, C.J.: LibSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
  9. 9.
    Tang, J., Dieterich, S., Cleary, K.: Respiratory motion tracking of skin and liver in swine for CyberKnife motion compensation. In: Galloway Jr., R.L. (ed.) Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display. vol. 5367, pp. 729–734. SPIE, San Diego (2004)Google Scholar
  10. 10.
    Seppenwoolde, Y., Berbeco, R.I., Nishioka, S., et al.: Accuracy of tumor motion compensation algorithm from a robotic respiratory tracking system: A simulation study. Med. Phys. 34(7), 2774–2784 (2007)CrossRefGoogle Scholar
  11. 11.
    Khamene, A., Warzelhan, J.K., Vogt, S., Elgort, D., Chefd’Hotel, C., Duerk, J.L., Lewin, J.S., Wacker, F.K., Sauer, F.: Characterization of internal organ motion using skin marker positions. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. Part II. LNCS, vol. 3217, pp. 526–533. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Floris Ernst
    • 1
  • Volker Martens
    • 1
  • Stefan Schlichting
    • 2
  • Armin Beširević
    • 2
  • Markus Kleemann
    • 2
  • Christoph Koch
    • 3
  • Dirk Petersen
    • 3
  • Achim Schweikard
    • 1
  1. 1.Institute for Robotics and Cognitive SystemsUniversity of Lübeck
  2. 2.Clinic for SurgeryUniversity Hospital Schleswig-HolsteinLübeck
  3. 3.Institute for NeuroradiologyUniversity Hospital Schleswig-HolsteinLübeck

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