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Monte Carlo simulation-based dose calculation for organs under accidental radiation exposure in flaw detection / 中国辐射卫生
Chinese Journal of Radiological Health ; (6): 365-371, 2023.
Artigo em Chinês | WPRIM | ID: wpr-988136
ABSTRACT
Objective To calculate the doses and their dose conversion coefficients for the personnel whose organs were under accidental exposure to three types of X-ray machines and two γ radiation sources, and to provide a simple method for rapid estimation of accidental doses. Methods The radiation source models of X-ray machines and two γ sources were established with the FLUKA simulation software and a Chinese reference voxel phantom was imported. The organ absorbed dose, dose conversion coefficient between organ absorbed dose and air Kerma, and conversion coefficient between organ absorbed dose and radiation source were calculated for a simulated scenario where the personnel were under antero-posterior exposure to radiation sources 1 meter away. Results For the lungs, heart, muscles, soft tissue, liver, skin, and brain, the conversion coefficient between organ absorbed dose and air Kerma was 0.30-1.19 (Gy/Gy). For X-ray machines, the conversion coefficient between organ absorbed dose and output for the six organs ranged from 6.12 × 10−3 to 2.90 × 10−2 Gy·m2/(mA·min). For γ radiation sources, the conversion coefficient between organ absorbed dose and activity for the six organs ranged from 1.12 × 10−8 to 7.01 × 10−8 Gy·m2/(GBq·s). Conclusion The conversion coefficient between organ absorbed dose and air Kerma and the conversion coefficient between organ absorbed dose and output or activity of a flaw detector can provide important dosimetric parameters for rapid assessment of similar radiation accidents.

Texto completo: DisponíveL Índice: WPRIM (Pacífico Ocidental) Idioma: Chinês Revista: Chinese Journal of Radiological Health Ano de publicação: 2023 Tipo de documento: Artigo

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Texto completo: DisponíveL Índice: WPRIM (Pacífico Ocidental) Idioma: Chinês Revista: Chinese Journal of Radiological Health Ano de publicação: 2023 Tipo de documento: Artigo