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Radiation Treatment Planning: Mixed Integer Programming Formulations and Approaches

In: Handbook on Modelling for Discrete Optimization

Author

Listed:
  • Michael C. Ferris

    (University of Wisconsin)

  • Robert R. Meyer

    (University of Wisconsin)

  • Warren D’Souza

    (University of Maryland School of Medicine)

Abstract

Radiation therapy is extensively used to treat a wide range of cancers. Due to the increasing complexities of delivery mechanisms, and the improved imaging devices that allow more accurate determination of cancer location, determination of high quality treatment plans via trial-and-error methods is impractical and computer optimization approaches to planning are becoming more critical and more difficult. We outline three examples of the types of treatment planning problem that can arise in practice and strive to understand the commonalities and differences in these problems. We highlight optimization approaches to the problems, and particularly consider approaches based on mixed integer programming. Details of the mathematical formulations and algorithmic approaches are developed and pointers are given to supporting literature that shows the efficacy of the approaches in practical situations.

Suggested Citation

  • Michael C. Ferris & Robert R. Meyer & Warren D’Souza, 2006. "Radiation Treatment Planning: Mixed Integer Programming Formulations and Approaches," International Series in Operations Research & Management Science, in: Gautam Appa & Leonidas Pitsoulis & H. Paul Williams (ed.), Handbook on Modelling for Discrete Optimization, chapter 0, pages 317-340, Springer.
    • Handle: RePEc:spr:isochp:978-0-387-32942-0_11
    DOI: 10.1007/0-387-32942-0_11
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    Cited by:

    1. Ali Tuncel & Felisa Preciado & Ronald Rardin & Mark Langer & Jean-Philippe Richard, 2012. "Strong valid inequalities for fluence map optimization problem under dose-volume restrictions," Annals of Operations Research, Springer, vol. 196(1), pages 819-840, July.

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