Neuro-dynamic programming for fractionated radiotherapy planning

  • Geng Deng
  • Michael C. Ferris
Part of the Springer Optimization and Its Applications book series (SOIA, volume 12)


We investigate an on-line planning strategy for the fractionated radiotherapy planning problem, which incorporates the effects of day-to-day patient motion. On-line planning demonstrates significant improvement over off-line strategies in terms of reducing registration error, but it requires extra work in the replanning procedures, such as in the CT scans and the re-computation of a deliverable dose profile. We formulate the problem in a dynamic programming framework and solve it based on the approximate policy iteration techniques of neuro-dynamic programming. In initial limited testing, the solutions we obtain outperform existing solutions and offer an improved dose profile for each fraction of the treatment.


Fractionation adaptive radiation therapy neuro-dynamic programming reinforcement learning 


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Geng Deng
    • 1
  • Michael C. Ferris
    • 2
  1. 1.Department of MathematicsUniversity of Wisconsin at MadisonMadisonUSA
  2. 2.Computer Sciences DepartmentUniversity of Wisconsin at MadisonMadisonUSA

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