Abstract
In High Dose Rate (HDR) brachytherapy the conventional dose optimization algorithms consider the multiple objectives in form of an aggregate function which combines individual objectives into a single utility value. As a result, the optimization problem becomes single objective, prior to optimization. Up to 300 parameters must be optimized satisfying objectives which are often competing. We use multiobjective dose optimization methods where the objectives are expressed in terms of quantities derived from dose-volume histograms or in terms of statistical parameters of dose distributions from a small number of sampling points. For the last approach we compare the optimization results of evolutionary multiobjective algorithms with deterministic optimization methods. The deterministic algorithms are very efficient and produce the best results. The performance of the multiobjective evolutionary algorithms is improved if a small part of the population is initialized by deterministic algorithms.
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References
Yu, Y., Schell, M. C.: A genetic algorithm for the optimization of prostate implants. Med. Phys. 23 (1996) 2085–2091
Yang, G., Reinstein, L. E., Pai, S., Xu, Z.: A new genetic algorithm technique in optimization of permanent 125I prostate implants. Med. Phys. 25 (1998) 2308–2315
Lahanas, M., Baltas, D., Giannouli, S., Milickovic, N., Zamboglou, N.: Generation of uniformly distributed dose points for anatomy-based three-dimensional dose optimization methods in brachytherapy. Med. Phys. 27 (2000) 1034–1046
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation. 8 (2000) 173–195
Press, W. H., Teukolsky, S. A., Vetterling, W.T., Flannery, B. P.: Numerical Recipes in C. 2nd ed. Cambridge University Press, Cambridge, England. 1992
Nath, R., Anderson, L. L., Luxton, G., Weaver, K. A., Williamson, J. F., Meigooni, A. S.: Dosimetry of interstitial brachytherapy sources: Recommendations of the AAPM Radiation Therapy Committee Task Group No. 43. Med. Phys. 22 (1995) 209–234
Baltas D., Kolotas, C., Geramani, K., Mould, R. F., Ioannidis, G., Kekchidi, M., Zamboglou, N.: A Conformal Index (COIN) to evaluate implant quality and dose specifications in brachytherapy. Int. J. Radiat. Oncol. Biol. Phys., 40 (1998) 512–524
Horn, J., Nafpliotis, N.: Multiobjective optimization using the niched Pareto genetic Algorithm. IlliGAL Report No.93005. Illinois Genetic Algorithms Laboratory. University of Illinois at Urbana-Champaign, 1993
Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation. 37 (1999) 257–271
Fonseca, M., Fleming, P. J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms I: A unified formulation. Research report 564, Dept. Automatic Control and Systems Eng. University of Sheffield, Sheffield, U.K., Jan. 1995
Fonseca, M., Fleming, P. J.: An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3 (1995) 1–16
Milickovic, N., Lahanas, M., Baltas, D., Zamboglou, N.: Comparison of evolutionary and deterministic multiobjective algorithms for dose optimization in brachytherapy. These proceedings
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer Verlag. 1996
Deb, K.: Non-linear goal programming using Multi-objective Genetic Algorithms. Technical Report CI-60/98, Department of Computer Science /LS11. University of Dortmund, Germany. (1999)
Goldberg, D. E., Richardson, J.: Genetic Algorithms with Sharing for Multimodal Function Optimization. J.J. Grefenstette (Editor), Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms. Lawrence Erlbaum Associated. (1987) 41–49
Lahanas, M., Baltas, D., Zamboglou, N.: Anatomy-based three-dimensional dose optimization in brachytherapy using multiobjective genetic algorithms. Med. Phys. 26 (1999) 1904–1918
Edmundson, K.: Geometry based optimization for stepping source implants, in: Brachytherapy HDR and LDR, A. A. Martinez, C. G. Orton and R. F. Mould eds., Nucleotron: Columbia. (1990) 184–192
Van der Laarse, T. P. E. Prins.: Introduction to HDR brachytherapy optimisation, In: R. F. Mould, J. J. Battermann, A. A. Martinez and B. L. Speiser eds. Brachytherapy from Radium to Optimization. Veenendaal, The Netherlands: Nucleotron International. (1994) 331–351
Das, I. Dennis, J.: A Closer Look at Drawbacks of Minimizing Weighted Sums of Objectives for Pareto Set Generation in Multicriteria Optimization Problems. Structural Optimization 14 (1997) 63–69
Deasy, J. O.: Multiple local minima in radiotherapy optimization problems with dose-volume constraints. Med. Phys. 24 (1997) 1157–1161
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Lahanas, M., Milickovic, N., Baltas, D., Zamboglou, N. (2001). Application of Multiobjective Evolutionary Algorithms for Dose Optimization Problems in Brachytherapy. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_40
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DOI: https://doi.org/10.1007/3-540-44719-9_40
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