Optimizing External Surface Sensor Locations for Respiratory Tumor Motion Prediction

  • Yusuf ÖzbekEmail author
  • Zoltan Bardosi
  • Srdjan Milosavljevic
  • Wolfgang Freysinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11076)


Real-time tracking of tumor motion due to the patient’s respiratory cycle is a crucial task in radiotherapy treatments. In this work a proof-of-concept setup is presented where real-time tracked external skin attached sensors are used to predict the internal tumor locations. The spatiotemporal relationships between external sensors and targets during the respiratory cycle are modeled using Gaussian Process regression and trained on a preoperative 4D-CT image sequence of the respiratory cycle. A large set (\(N \approx 25\)) of computer-tomography markers are attached on the patient’s skin before CT acquisition to serve as candidate sensor locations from which a smaller subset (\( N \approx 6 \)) is selected based on their combined predictive power using a genetic algorithm based optimization technique. A custom 3D printed sensor-holder design is used to allow accurate positioning of optical or electromagnetic sensors at the best predictive CT marker locations preoperatively, which are then used for real-time prediction of the internal tumor locations. The method is validated on an artificial respiratory phantom model. The model represents the candidate external locations (fiducials) and internal targets (tumors) with CT markers. A 4D-CT image sequence with 11 time-steps at different phases of the respiratory cycles was acquired. Within this test setup, the CT markers for both internal and external structures are automatically determined by a morphology-based algorithm in the CT images. The method’s in-sample cross validation accuracy in the training set as given by the average root mean-squared error (RMSE) is between 0.00024 and 0.072 mm.


Tumor tracking Respiratory motion Prediction Optimization 


  1. 1.
    Balter, J.M., et al.: Accuracy of a wireless localization system for radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 61(3), 933–7 (2005)CrossRefGoogle Scholar
  2. 2.
    Krilavicius, T., Zliobaite, I., Simonavicius, H., Jaruevicius, L.: Predicting respiratory motion for real-time tumour tracking in radiotherapy. In: IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), June 2016. ISSN: 2372–9198Google Scholar
  3. 3.
    Lee, S.J., Motai, Y.: Prediction and Classification of Respiratory Motion. Springer, Heidelberg (2014). ISBN: 978-3-642-41508-1CrossRefGoogle Scholar
  4. 4.
    AAPM Task Group 76: The Management of Respiratory Motion in Radiation Oncology, American Association of Physicists in Medicine One Physics Ellipse, College Park, MD (2006). ISBN: 1-888340-61-4Google Scholar
  5. 5.
    Yan, H., et al.: Investigation of the location effect of external markers in respiratory-gated radiotherapy. J. Appl. Clin. Med. Phys. 9(2), 57–68 (2008)CrossRefGoogle Scholar
  6. 6.
    Spinczyk, D., Karwan, A., Copik, M.: Methods for abdominal respiratory motion tracking. Comput. Aided Surg. 19(1–3), 34–47 (2014)CrossRefGoogle Scholar
  7. 7.
    Buzurovic, I., Huang, K., Yu, Y., Podder, T.K.: A robotic approach to 4D real-time tumor tracking for radiotherapy. Phys. Med. Biol. 56(5), 1299–1318 (2011)CrossRefGoogle Scholar
  8. 8.
    Wong, J.R., et al.: Image-guided radiotherapy for prostate cancer by CT-linear accelerator combination: prostate movements and dosimetric considerations. Int. J. Radiat. Oncol. Biol. Phys. 61(2), 561–569 (2005)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Sawant, A., et al.: Toward submillimeter accuracy in the management of intrafraction motion: the integration of real-time internal position monitoring and multileaf collimator target tracking. Int. J. Radiat. Oncol. Biol. Phys. 74(2), 575–582 (2009)CrossRefGoogle Scholar
  10. 10.
    D’Souza, W.D., Naqvi, S.A., Yu, C.X.: Real-time intra-fraction-motion tracking using the treatment couch: a feasibility study. Phys. Med. Biol. 50(17), 4021–4033 (2005)CrossRefGoogle Scholar
  11. 11.
    Buzurovic, I., Podder, T.K., Huang, K., Yu, Y.: Tumor motion prediction and tracking in adaptive radiotherapy. In: IEEE International Conference on Bioinformatics and Bioengineering, pp. 273–278 (2010)Google Scholar
  12. 12.
    Bardosi, Z.: OpenCL accelerated GPU binary morphology image filters for ITK. Insight Journal (2015)Google Scholar
  13. 13.
    Spolaôr, N., Lorena, A.C., Lee, H.D.: Multi-objective genetic algorithm evaluation in feature selection. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds.) EMO 2011. LNCS, vol. 6576, pp. 462–476. Springer, Heidelberg (2011). Scholar
  14. 14.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, 1st edn. Addison-Wesley Professional (1989). ISBN: 978-0201157673Google Scholar
  15. 15.
    Rasmussen, C., Williams, C.: Gaussian Processes for Machine Learning. MIT University Press Group Ltd. (2005). ISBN: 026218253XGoogle Scholar
  16. 16.
    Weiss, E., Wijesooriya, K., Dill, S.V., Keall, P.J.: Tumor and normal tissue motion in the thorax during respiration analysis of volumetric and positional variations using 4D CT. Int. J. Radiation Oncol. Biol. Phys. 67(1), 296–307 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yusuf Özbek
    • 1
    Email author
  • Zoltan Bardosi
    • 1
  • Srdjan Milosavljevic
    • 1
  • Wolfgang Freysinger
    • 1
  1. 1.4D Visualization Research Group, Univ. ENT ClinicMedical University of InnsbruckInnsbruckAustria

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