An Efficiency Studying of an Ion Chamber Simulation Using Vriance Reduction Techniques with EGSnrc

An Efficiency Studying of an Ion Chamber Simulation Using Vriance Reduction Techniques with EGSnrc

T., Campos L.;A., Magalhães L.;V., de Almeida C. E.;
journal of biomedical physics and engineering 2019 Vol. 9 pp. 259-266
305
t2019anjournal

Abstract

Background: Radiotherapy is an important technique of cancer treatment using ionizing radiation. The determination of total dose in reference conditions is an important contribution to uncertainty that could achieve 2%. The source of this uncertainty comes from cavity theory that relates the in-air cavity dose and the dose to water. These correction factors are determined from Monte Carlo calculations of ionization chambers. The main problem of this type of calculation is the extremely long computation time to achieve reasonable statistics. Objective: The main purpose of this work is to present a combination with variance reduction techniques for the case of an ionization chamber in water. Methods: The egs_chamber code allows for very efficient computation of ionization chamber doses and dose ratios by using various variance reduction techniques, and also permits realistic simulations of the experimental setup due to the use of EGSnrc C++ library. Russian roulette and Photon Cross Section Enhancement were used with egs_chamber code. Tests were performed to obtain the parameters of variance reduction techniques resulting in a maximum efficiency. Results: It can be seen that the parameters which result in improved Monte Carlo calculation of the efficiency values are XCSE 64 and Russian Roulette (RR) 128. Conclusion: This study determines the parameters of variance reduction techniques that result in an optimal computational efficiency. Citation: Campos L. T, Magalhães L. A, de Almeida C. E. V. An Efficiency Studying of an Ion Chamber Simulation Using Vriance Reduction Techniques with EGSnrc. J Biomed Phys Eng. 2019;9(3):259-266. https://doi.org/10.22086/jbpe.v0i0.682.

Citation

ID: 57338
Ref Key: t2019anjournal
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
57338
Unique Identifier:
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet