Optimization in Medicine

Volume 12 of the series Springer Optimization and Its Applications pp 47-70

Neuro-dynamic programming for fractionated radiotherapy planning

  • Geng DengAffiliated withDepartment of Mathematics, University of Wisconsin at Madison
  • , Michael C. FerrisAffiliated withComputer Sciences Department, University of Wisconsin at Madison

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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