Neuro-dynamic programming for fractionated radiotherapy planning

Geng Deng () and Michael C. Ferris ()
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Geng Deng: University of Wisconsin at Madison
Michael C. Ferris: University of Wisconsin at Madison

A chapter in Optimization in Medicine, 2008, pp 47-70 from Springer

Abstract: Summary 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.

Keywords: Fractionation; adaptive radiation therapy; neuro-dynamic programming; reinforcement learning (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1007/978-0-387-73299-2_3

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