Skip to main content

Intensity Modulated Beam Radiation Therapy Dose Optimization with Multiobjective Evolutionary Algorithms

  • Conference paper
  • First Online:
Evolutionary Multi-Criterion Optimization (EMO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2632))

Included in the following conference series:

Abstract

We apply the NSGA-II algorithm and its controlled elitist version NSGA-IIc for the intensity modulated beam radiotherapy dose optimization problem. We compare the performance of the algorithms with objectives for which deterministic optimization methods provide global optimal solutions. The number of parameters to be optimized can be up to a few thousands and the number of objectives varies from 3 to 6. We compare the results with and without supporting solutions. Optimization with constraints for the target dose variance value provides clinical acceptable solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Peres, C. A., and Brady, L. W.: Principles and practice of radiotherapy. Lippincott-Raven, Philadelphia, 3rd edition, 1998.

    Google Scholar 

  2. Xing, L., Li, J.G., Donaldson, S., Le, Q.T. and Boyer, A. L.: Optimization of importance factors in inverse planning. Phys Med. Biol., 44 2525–2536, 1999.

    Article  Google Scholar 

  3. Cotrutz, C., Lahanas, M., Kappas, C. and Baltas, D.: A multiobjective gradient based dose optimization algorithm for conformal radiotherapy. Phys. Med. Biol. 46 2161–2175, 2001.

    Article  Google Scholar 

  4. Webb, S.: Optimization of conformal radiotherapy dose distributions by simulated annealing. Phys. Med. Biol., 34 1349–1370, 1990.

    Article  Google Scholar 

  5. Lahanas, M., Baltas, D. and Zamboglou, N.: A hybrid evolutionary algorithm for multiobjective anatomy based dose optimization in HDR brachytherapy, to be published in Phys. Med. Biol. 2003.

    Google Scholar 

  6. Bortfeld, T., Ürkelbach, J., Boesecke, R. and Schlegel, W.: Methods of image reconstruction from projections applied to conformation therapy, Phys. Med. Biol. 35 1423–1434, 1990.

    Article  Google Scholar 

  7. Haas, O. C. L., Burnham K. J. and Mills J. A.: On Improving the selectivity in the treatment of cancer: a systems modelling and optimisation approach. J. Control Engineering Practice, 5 1739–45, 1997

    Article  Google Scholar 

  8. Haas, O. C. L.: Radiotherapy Treatment Planning: New System Approaches. Springer Verlag London, Advances in Industrial Control Monograph, ISBN 1-85233-063-5, 1999.

    Google Scholar 

  9. Knowles, J. D., Corne, D. and Bishop J. M.: Evolutionary Training of Artificial Neural Networks for Radiotherapy Treatment of Cancers in Proceedings of the 1998 IEEE International Conference on Evolutionary Computation IEEE Neural Networks Council, 0-7803-4871-0, pp 398–403, pp. 398–403

    Google Scholar 

  10. Knowles, J. D. and Corne, D.: Evolving Neural Networks for Cancer Radiotherapy, in Chambers, L.(ed.), Practical Handbook of Genetic Algorithms: Application 2nd Edition, Chapman Hall/CRC Press, pp. 443–448. ISBN L-58488-240-9, 2000.

    Google Scholar 

  11. Liu, D.C., and Nocedal, J.: On the limited memory BFGS method for large scale optimization. Mathematical Programming 45 503–528, 1989.

    Article  MATH  MathSciNet  Google Scholar 

  12. Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. Technical Report 20001, Indian Institute of Technology, Kanpur, Kanpur Genetic Algorithms Laboratory (KanGAL), 2000.

    Google Scholar 

  13. Deb, K. and Goel, T.: Controlled elitist non-dominated sorting genetic algorithms for better convergence, in Proceedings of the first international conference. EMO 2001, Zurich, Switzerland, edited by E. Zitzler, K. Deb, L. Thiele, C. A. Coello Coello, D. Corne, Lecture Notes in Computer Science Vol. 1993, Springer 67–81, 2001

    Google Scholar 

  14. Gandibleaux, X., Morita, H. and Katoh, N.: The Supported Solutions Used as a Genetic Information in Population Heuristic, in Proceedings of the first international conference. EMO 2001, Zurich, Switzerland, edited by E. Zitzler, K. Deb, L. Thiele, C. A. Coello Coello, D. Corne, Lecture Notes in Computer Science Vol. 1993, Springer 429–42. 2001.

    Google Scholar 

  15. Milickovic, N., Lahanas, M., Baltas, D. and Zamboglou, N.: Comparison of Evolutionary and Deterministic Multiobjective Algorithms for Dose Optimization in Brachytherapy, in Proceedings of the first international conference. EMO 2001, Zurich, Switzerland, edited by E. Zitzler, K. Deb, L. Thiele, C. A. Coello Coello, D. Corne, Lecture Notes in Computer Science Vol. 1993, Springer 167–180. 2001.

    Google Scholar 

  16. Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the Pareto Archived Evolution Strategy. Evolutionary Computation 8 149–172, 2000.

    Article  Google Scholar 

  17. Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 37 257–271, 1999.

    Article  Google Scholar 

  18. Deb, K. and Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems 9 115–148, 1995.

    MATH  MathSciNet  Google Scholar 

  19. Deb, K. and Beyer, H.G.: Self-Adaptive genetic Algorithms with Simulated Binary Crossover. Evolutionary Computation 9 197–221, 2001.

    Article  Google Scholar 

  20. Deb, K., Multi-Objective Optimization using Evolutionary Algorithms, Chichester, Wiley, UK, 2001.

    MATH  Google Scholar 

  21. Jaszkiewicz, A. Genetic local search for multiple objective combinatorial optimization, Technical Report RA-014/98, Institute of Computing Science, Poznan University of Technology, 1998.

    Google Scholar 

  22. Goel, T. and Deb, K.: Hybrid Methods for Multi-Objective Evolutionary Algorithms. KanGAL Report Number 2001004, 2001.

    Google Scholar 

  23. http://www-dss.cs.put.poznan.pl/~jaskiewicz/MOMHLIB/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lahanas, M., Schreibmann, E., Milickovic, N., Baltas, D. (2003). Intensity Modulated Beam Radiation Therapy Dose Optimization with Multiobjective Evolutionary Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_46

Download citation

  • DOI: https://doi.org/10.1007/3-540-36970-8_46

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01869-8

  • Online ISBN: 978-3-540-36970-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics