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TopasOpt: An open-source library for optimization with Topas Monte Carlo.

Brendan Whelan1,2, Billy W Loo2,3, Jinghui Wang2,4

  • 1Image X Institute, School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.

Medical Physics
|December 9, 2022
PubMed
Summary
This summary is machine-generated.

TopasOpt, a free library for optimizing Monte Carlo simulations in Topas, was developed and tested. It improves accuracy and efficiency for unknown parameter values in simulations, aiding component design and model refinement.

Keywords:
monte-carloopen-sourceoptimization

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Area of Science:

  • Medical Physics
  • Computational Physics
  • Scientific Software Development

Background:

  • Monte Carlo simulations are crucial for modeling complex physical processes.
  • Optimizing simulation parameters is essential for accurate and efficient results.
  • Topas is a widely used Monte Carlo simulation software in various scientific fields.

Purpose of the Study:

  • To introduce and evaluate TopasOpt, a novel, free, and open-source library for mathematical optimization of Topas Monte Carlo simulations.
  • To demonstrate the library's capability to transform any Topas model into an optimization problem, allowing parameters to be treated as variables.

Main Methods:

  • TopasOpt was tested using three case studies involving electron beam, bremsstrahlung X-ray spectrum, and collimator geometry.
  • Bayesian Optimization and Nelder-Mead methods were employed to solve optimization problems.
  • The impact of noise on optimization convergence was investigated by varying the number of primary particles.

Main Results:

  • Both Bayesian Optimization and Nelder-Mead successfully minimized objective functions for phase space and geometry optimization, achieving low mean differences in dose profiles and depth-dose.
  • Bayesian Optimization demonstrated robustness to noise, maintaining accuracy even with a 16% standard deviation in the objective function.
  • Parameter recovery accuracy varied, with some electron beam parameters showing high relative errors due to inherent dose insensitivity.

Conclusions:

  • TopasOpt, an open-source library for optimizing Topas Monte Carlo simulations, has been successfully developed, tested, and released.
  • This tool enhances accuracy and efficiency in determining optimal parameters for Monte Carlo simulations.
  • Potential applications include designing new components, reverse engineering models from data, and tuning simulation hyperparameters.