Estimation of Delivered Dose in Radiotherapy: The Influence of Registration Uncertainty

  • Petter Risholm
  • James Balter
  • William M. Wells
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6891)


We present a probabilistic framework to estimate the accumulated radiation dose and the corresponding dose uncertainty that is delivered to important anatomical structures, e.g. the primary tumor and healthy surrounding organs, during radiotherapy. The dose uncertainty we report is a direct result of uncertainties in the estimates of the deformation which aligns the daily cone-beam CT images with the planning CT. The accumulated radiation dose is an important measure to monitor during treatment, in particular to see if it significantly deviates from the planned dose which might indicate that either the patient was not properly positioned before treatment or that the anatomy has changed due to the treatment. In the case of the latter, the treatment plan should be adaptively changed to align with the current patient anatomy. We estimate the accumulated dose distribution, and its uncertainty, retrospectively on a dataset acquired during treatment of cancer in the neck and show the dose distributions in the form of dose volume histograms.


Parotid Gland Dose Distribution Submandibular Gland Intensity Modulate Radiation Therapy CBCT Image 
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.


  1. 1.
    Drzymala, R., Mohan, R., Brewster, L., Chu, J., Goitein, M., Harms, W., Urie, M.: Dose-volume histograms. Int. J. Radiat. Oncol. Biol. Phys. 21(1), 71–78 (1991)CrossRefGoogle Scholar
  2. 2.
    Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis, 2nd edn. Chapman & Hall/CRC (July 2003)Google Scholar
  3. 3.
    Hong, T.S., Tome, W.A., Chappell, R.J., Chinnaiyan, P., Mehta, M.P., Harari, P.M.: The impact of daily setup variations on head-and-neck intensity-modulated radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 61(3), 779–788 (2005)CrossRefGoogle Scholar
  4. 4.
    Lu, W., Olivera, G.H., Chen, Q., Ruchala, K.J., Haimerl, J., Meeks, S.L., Langen, K.M., Kupelian, P.A.: Deformable registration of the planning image (kvct) and the daily images (mvct) for adaptive radiation therapy. Phys. Med. Biol. 51(17), 4357 (2006)CrossRefGoogle Scholar
  5. 5.
    Risholm, P., Pieper, S., Samset, E., Wells III, W.M.: Summarizing and visualizing uncertainty in non-rigid registration. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 554–561. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Risholm, P., Samset, E., Wells III, W.: Bayesian estimation of deformation and elastic parameters in non-rigid registration. In: Fischer, B., Dawant, B.M., Lorenz, C. (eds.) WBIR 2010. LNCS, vol. 6204, pp. 104–115. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Schwartz, D.L., Dong, L.: Adaptive radiation therapy for head and neck cancer-can an old goal evolve into a new standard? J. Oncol. (2011)Google Scholar
  8. 8.
    Sykes, J.R., Brettle, D.S., Magee, D.R., Thwaites, D.I.: Investigation of uncertainties in image registration of cone beam ct to ct on an image-guided radiotherapy system. Phys. Med. and Biol. 54(24), 7263 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Petter Risholm
    • 1
  • James Balter
    • 2
  • William M. Wells
    • 1
  1. 1.Brigham and Women’s HospitalHarvard Medical SchoolUSA
  2. 2.Department of Radiation OncologyUniversity of MichiganUSA

Personalised recommendations