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Particle swarm optimizer for weighting factor selection in intensity-modulated radiation therapy optimization

Jie Yang1, Pengcheng Zhang2, Liyuan Zhang2

  • 1National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan, Shanxi 030051, China; School of Medicine Management, Shanxi University of TCM, Taiyuan, Shanxi 030619, China.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|January 17, 2017
PubMed
Summary

A new particle swarm optimization (PSO) method automates intensity-modulated radiation therapy (IMRT) planning by adjusting objective function weights, reducing planner workload and improving treatment plan quality.

Keywords:
Automated planningIntensity-modulated radiation therapyOptimizationParticle swarm optimizationTreatment planning

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Intensity-modulated radiation therapy (IMRT) planning relies on manual adjustment of objective function weights, a time-consuming trial-and-error process.
  • This manual intervention in IMRT planning increases workload and can limit the optimization of treatment plans.

Purpose of the Study:

  • To investigate a novel particle swarm optimization (PSO) method for automated adjustment of objective function weights in IMRT inverse planning.
  • To reduce human planner workload and advance the development of fully automated IMRT treatment planning.

Main Methods:

  • A PSO algorithm was developed to automatically optimize weighting factors for the objective function.
  • The method involves initializing a swarm of weights, optimizing plans for each particle, and updating weights iteratively.
  • Evaluation functions based on dose-volume histograms and a hybrid crossover-mutation strategy were employed.

Main Results:

  • The proposed PSO method successfully generated high-quality IMRT plans for prostate cancer cases without human intervention.
  • Automated plans were found to be superior to those developed with manual weight adjustment.
  • The algorithm produced admissible solutions within reasonable computational times.

Conclusions:

  • The automated PSO method effectively optimizes IMRT treatment plans, outperforming manual planning approaches.
  • This approach significantly reduces the workload for human planners in the iterative IMRT planning process.
  • The developed algorithm is a viable step towards fully automated IMRT treatment planning.