MCNPX'S water equivalent thickness simulation of material with different density via proton beam irradiation

The radiological thickness of materials and beam penetration range is often referred as the water equivalent thickness (WET). In the clinical application of radiotherapy it is mandatory to obtain a WET calculation with high accuracies to ensure the beam that penetrated the human tissues is capable t...

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Bibliographic Details
Main Authors: Khattak M.A., Borhana A.A., Shafii L.F.A., Khan R.
Other Authors: 55909928700
Format: Article
Published: Science Publishing Corporation Inc 2023
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Summary:The radiological thickness of materials and beam penetration range is often referred as the water equivalent thickness (WET). In the clinical application of radiotherapy it is mandatory to obtain a WET calculation with high accuracies to ensure the beam that penetrated the human tissues is capable to deliver high dose of radiation into the deep-rooted tumors and kill the malignant cancerous cell without any major damages to the healthy tissues. Nevertheless, the present method of calculation that is available needs either intensive numerical method or approximation techniques with unknown precision. Hence, the purpose of this research is to study the depth of proton beam irradiation penetration range of materials with arbitrary density & elemental composition and modeled the water equivalent thickness (WET) calculation by using the Monte Carlo N Particle Transport Code Extension (MCNPX). There are several type of material with different density that are utilize in this project which are water phantom (? =1.0 g cm -3 ), PMMA (? =1.19 g cm -3 ) aluminum (? = 2.70 g cm -3 lead (? =11.3g cm -3 ). The water phantom represent reference material whilst PMMA, Aluminum and Lead each represent low, medium and high density respectively. Based from the result produced in output file, Bragg curves for each material were reproduced, analyzed and compared with the Bragg curve of water phantom. The WET of water phantom was successfully modelled by using MCNPX. Apart from the short computing time, modelling WET via MCNPX was more efficient compare to analytical calculation. � 2018 Authors.