Advertisement

Joint ToF Image Denoising and Registration with a CT Surface in Radiation Therapy

  • Sebastian Bauer
  • Benjamin Berkels
  • Joachim Hornegger
  • Martin Rumpf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6667)

Abstract

The management of intra-fractional respiratory motion is becoming increasingly important in radiation therapy. Based on in advance acquired accurate 3D CT data and intra-fractionally recorded noisy time-of-flight (ToF) range data an improved treatment can be achieved. In this paper, a variational approach for the joint registration of the thorax surface extracted from a CT and a ToF image and the denoising of the ToF image is proposed. This enables a robust intra-fractional full torso surface acquisition and deformation tracking to cope with variations in patient pose and respiratory motion. Thereby, the aim is to improve radiotherapy for patients with thoracic, abdominal and pelvic tumors. The approach combines a Huber norm type regularization of the ToF data and a geometrically consistent treatment of the shape mismatch. The algorithm is tested and validated on synthetic and real ToF/CT data and then evaluated on real ToF data and 4D CT phantom experiments.

Keywords

Respiratory Motion Compute Tomography Data Range Function Distance Dist Joint Approach 
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.
    Keall, P.J., Mageras, G.S., Balter, J.M., Emery, R.S., Forster, K.M., Jiang, S.B., Kapatoes, J.M., Low, D.A., Murphy, M.J., Murray, B.R., Ramsey, C.R., Herk, M.B.V., Vedam, S.S., Wong, J.W., Yorke, E.: The management of respiratory motion in radiation oncology, report of AAPM task group 76. Med. Phys. 33(10), 3874–3900 (2006)CrossRefGoogle Scholar
  2. 2.
    Essapen, S., Knowles, C., Norman, A., Tait, D.: Accuracy of set-up of thoracic radiotherapy: prospective analysis of 24 patients treated with radiotherapy for lung cancer. Br. J. Radiol. 75(890), 162–169 (2002)CrossRefGoogle Scholar
  3. 3.
    Johnson, U., Landau, D., Lindgren-Turner, J., Smith, N., Meir, I., Howe, R., Rodgers, H., Davit, S., Deehan, C.: Real time 3D surface imaging for the analysis of respiratory motion during radiotherapy. International Journal of Radiation Oncology Biology Physics 60(supplement 1), 603–604 (2004)CrossRefGoogle Scholar
  4. 4.
    Schaller, C., Penne, J., Hornegger, J.: Time-of-Flight Sensor for Respiratory Motion Gating. Medical Physics 35(7), 3090–3093 (2008)CrossRefGoogle Scholar
  5. 5.
    Fayad, H., Pan, T., Roux, C., Le Rest, C., Pradier, O., Clement, J., Visvikis, D.: A patient specific respiratory model based on 4D CT data and a time of flight camera (TOF). In: Proceedings of IEEE NSS/MIC, pp. 2594–2598 (2009)Google Scholar
  6. 6.
    Fayad, H., Pan, T., Roux, C., Le Rest, C., Pradier, O., Visvikis, D.: A 2D-spline patient specific model for use in radiation therapy. In: Proceedings of IEEE ISBI, pp. 590–593 (2009)Google Scholar
  7. 7.
    McClelland, J., Blackall, J., Tarte, S., Chandler, A., Hughes, S., Ahmad, S., Landau, D., Hawkes, D.: A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy. Medical Physics 33(9), 3348–3358 (2006)CrossRefGoogle Scholar
  8. 8.
    Kolb, A., Barth, E., Koch, R., Larsen, R.: Time-of-flight sensors in computer graphics. In: Proceedings of Eurographics, pp. 119–134 (2009)Google Scholar
  9. 9.
    Schaller, C., Adelt, A., Penne, J., Hornegger, J.: Time-of-flight sensor for patient positioning. In: Samei, E., Hsieh, J. (eds.) Proceedings of SPIE Medical Imaging, vol. 7258, p. 726110 (2009)Google Scholar
  10. 10.
    Lindner, M., Schiller, I., Kolb, A., Koch, R.: Time-of-flight sensor calibration for accurate range sensing. Computer Vision and Image Understanding 114(12), 1318–1328 (2010); Special issue on Time-of-Flight Camera Based Computer VisionCrossRefGoogle Scholar
  11. 11.
    Kapur, T., Yezzi, L., Zöllei, L.: A variational framework for joint segmentation and registration. In: Proceedings of IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 44–51 (2001)Google Scholar
  12. 12.
    Unal, G., Slabaugh, G., Yezzi, A., Tyan, J.: Joint segmentation and non-rigid registration without shape priors. Technical Report SCR-04-TR-7495, Siemens Corporate Research (2004)Google Scholar
  13. 13.
    Féron, O., Mohammad-Djafari, A.: Image fusion and unsupervised joint segmentation using a HMM and MCMC algorithms. J. of Electronic Imaging 15(02), 023014 (2004)Google Scholar
  14. 14.
    Droske, M., Rumpf, M.: Multi scale joint segmentation and registration of image morphology. IEEE Transaction on Pattern Recognition and Machine Intelligence 29(12), 2181–2194 (2007)CrossRefGoogle Scholar
  15. 15.
    Buades, T., Lou, Y., Morel, J., Tang, Z.: A note on multi-image denoising. In: Proceedings of International Workshop on Local and Non-Local Approximation in Image Processing, pp. 1–15 (2009)Google Scholar
  16. 16.
    Russo, G., Smereka, P.: A remark on computing distance functions. Journal of Computational Physics 163, 51–67 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Álvarez, L., Weickert, J., Sánchez, J.: A scale-space approach to nonlocal optical flow calculations. In: Nielsen, M., Johansen, P., Fogh Olsen, O., Weickert, J. (eds.) Scale-Space 1999. LNCS, vol. 1682, pp. 235–246. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  18. 18.
    Armijo, L.: Minimization of functions having Lipschitz continuous first partial derivatives. Pacific Journal of Mathematics 16(1), 1–3 (1966)CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Segars, W., Mori, S., Chen, G., Tsui, B.: Modeling respiratory motion variations in the 4D NCAT phantom. In: Proceedings of IEEE NSS/MIC, vol. 4, pp. 2677–2679 (2007)Google Scholar
  20. 20.
    Keller, M., Orthmann, J., Kolb, A., Peters, V.: A simulation framework for time-of-flight sensors. In: Proceedings of ISSCS, pp. 1–4 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sebastian Bauer
    • 1
  • Benjamin Berkels
    • 3
  • Joachim Hornegger
    • 1
    • 2
  • Martin Rumpf
    • 4
  1. 1.Pattern Recognition Lab, Dept. of Computer ScienceFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany
  2. 2.Erlangen Graduate School in Advanced Optical Technologies (SAOT)Friedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany
  3. 3.Interdisciplinary Mathematics Inst.University of South CarolinaColumbiaUSA
  4. 4.Inst. for Numerical SimulationRheinische Friedrich-Wilhelms-Universität BonnBonnGermany

Personalised recommendations