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  4. Vol 8, Issue 6, 2023

Application and Future Prospects of Computational Human Models in Conjunction with Monte Carlo Simulations in Radiation Dosimetry

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DOI: 10.23977/jeis.2023.080612 | Downloads: 11 | Views: 301

Author(s)

Shang Sun 1, Liwu Liu 1, Shaozhang Zhao 1, Xiaoyao Ma 1

Affiliation(s)

1 College of Science, Tibet University, Lhasa, Tibet, 850000, China

Corresponding Author

Shaozhang Zhao

ABSTRACT

In the field of radiation dosimetry research, the combination of computer-based human models and Monte Carlo simulation methods has proven to be indispensable and enables a differentiated and detailed approach to studying radiation exposure and its effects. This article examines in depth the application of radiation dosimetry to computer-aided human models combined with Monte Carlo simulations in assessing ionizing radiation doses and illustrates its critical role in this field. This article systematically analyses three main techniques for assessing ionizing radiation dose: direct measurement, phantom model measurement and the advanced method using computer-aided human models with Monte Carlo simulations, highlighting the notable advantages of the latter method. In addition, this article introduces a variety of software tools and discusses the basic principles and wide-ranging applications of computational human models based on Monte Carlo simulations, highlighting their adaptability and effectiveness in this area. Finally, the paper provides a visionary outlook on the evolving landscape and future possibilities of integrating computer-based human models with Monte Carlo simulation methods for radiation dose estimation and predicts significant advances in the field.

KEYWORDS

Human Model, Monte Carlo Simulation, Ionizing Radiation

CITE THIS PAPER

Shang Sun, Liwu Liu, Shaozhang Zhao, Xiaoyao Ma, Application and Future Prospects of Computational Human Models in Conjunction with Monte Carlo Simulations in Radiation Dosimetry. Journal of Electronics and Information Science (2023) Vol. 8: 95-103. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2023.080612.

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