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An Optimization-Based Approach to the Multiple Static Delivery Technique in Radiation Therapy

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  • J. Tervo
  • P. Kolmonen
  • T. Lyyra-Laitinen
  • J.D. Pintér
  • T. Lahtinen

Abstract

The paper considers the intensity modulated radiation therapy (inverse) treatment planning. An approach to determine the trajectories of the leaves of the multileaf collimator (MLC) in order to produce the prescribed intensity distribution is developed. The paper concentrates on the multiple static delivery technique. A mathematical model for calculating the intensity distribution with the help of locations of the leafheads of subsequent subfields is constructed. Furthermore, an optimization model in which the decision variables are the locations of leafheads is developed. The relevant constraints are considered as well. The optimization problem is a large dimensional constrained nonlinear global extremum problem. It is solved by the LGO (Lipschitz (Continuous) Global Optimizer) program system. Comparisons with other optimization method (Hooke–Jeeves iteration) are included. Numerical experiments are presented to confirm the functionality of the method. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • J. Tervo & P. Kolmonen & T. Lyyra-Laitinen & J.D. Pintér & T. Lahtinen, 2003. "An Optimization-Based Approach to the Multiple Static Delivery Technique in Radiation Therapy," Annals of Operations Research, Springer, vol. 119(1), pages 205-227, March.
    • Handle: RePEc:spr:annopr:v:119:y:2003:i:1:p:205-227:10.1023/a:1022942825680
    DOI: 10.1023/A:1022942825680
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    Cited by:

    1. Fredrik Carlsson & Anders Forsgren, 2014. "On column generation approaches for approximate solutions of quadratic programs in intensity-modulated radiation therapy," Annals of Operations Research, Springer, vol. 223(1), pages 471-481, December.
    2. Mustafa Çağlayan & János Pintér, 2013. "Development and calibration of a currency trading strategy using global optimization," Journal of Global Optimization, Springer, vol. 56(2), pages 353-371, June.
    3. János D. Pintér, 2018. "How difficult is nonlinear optimization? A practical solver tuning approach, with illustrative results," Annals of Operations Research, Springer, vol. 265(1), pages 119-141, June.
    4. Luciano Ferreira Cruz & Flavia Bernardo Pinto & Lucas Camilotti & Angelo Marcio Oliveira Santanna & Roberto Zanetti Freire & Leandro Santos Coelho, 2022. "Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process," Annals of Operations Research, Springer, vol. 319(2), pages 1565-1587, December.
    5. János Pintér & Zoltán Horváth, 2013. "Integrated experimental design and nonlinear optimization to handle computationally expensive models under resource constraints," Journal of Global Optimization, Springer, vol. 57(1), pages 191-215, September.

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