2018Journal of Fundamental and Applied SciencesOpen access

Calculation of dose distribution on Rhizophora spp soy protein phantom at 6 MV photon beam energy using Monte Carlo method

Nor Ain Rabaiee, Mohd Zahri Abdul Aziz, Rokiah Hashim, Reduan Abdullah, Ahmad Lutfi Yusoff, Muhammad Fadhirul Izwan Abdul Malik, A.A. Tajuddin

Open full text 2 citations

Abstract

Some of the commercial solid phantoms were unable to provide a good simulation to water atlow and high energy ranges. A potential phantom from Malaysian mangrove wood family,Rhizophoraspp was fabricated with addition of Soy Protein. An Electron Gamma Sho(EGSnrc) code was used to evaluate the dose distribution on Rhizophoraspp Soy Proteinphantom at 6 MV photon beam energy. The result of the Rhizophoraspp Soy Protein phantom was then compared with the water phantom and the solid water phantom. This study showed that the uncertainty between Rhizophoraspp Soy Protein phantom and the water phantom and the solid water phantom is less than 8 % at certain depth. These comparable results have demonstrated the potential of the Rhizophoraspp Soy Protein phantom as another option for solid phantom in dosimetry purposes.Keywords: mangrove wood; solid water phantom; water equivalent phantom; EGSnrc; depth dose.

Open-access reader

About this research paper

What this paper is about

Some of the commercial solid phantoms were unable to provide a good simulation to water atlow and high energy ranges. A potential phantom from Malaysian mangrove wood family,Rhizophoraspp was fabricated with addition of Soy Protein. An Electron Gamma Sho(EGSnrc) code was used to evaluate the dose distribution on Rhizophoraspp Soy Proteinphantom at 6 MV photon beam energy. The result of the Rhizophoraspp Soy Protein phantom was then compared with the water phantom and the solid water phantom. This study showed that the uncertainty between Rhizophoraspp Soy Protein phantom and the water phantom and the solid water phantom is less than 8 % at certain depth. These comparable results have demonstrated the potential of the Rhizophoraspp Soy Protein phantom as another option for solid phantom in dosimetry purposes.Keywords: mangrove wood; solid water phantom; water equivalent phantom; EGSnrc; depth dose.

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Some of the commercial solid phantoms were unable to provide a good simulation to water atlow and high energy ranges. A potential phantom from Malaysian mangrove wood family,Rhizophoraspp was fabricated with addition of Soy Protein. An Electron Gamma Sho(EGSnrc) code was used to evaluate the dose distribution on Rhizophoraspp Soy Proteinphantom at 6 MV photon beam energy. The result of the Rhizophoraspp Soy Protein phantom was then compared with the water phantom and the solid water phantom. This study showed that the uncertainty between Rhizophoraspp Soy Protein phantom and the water phantom and the solid water phantom is less than 8 % at certain depth. These comparable results have demonstrated the potential of the Rhizophoraspp Soy Protein phantom as another option for solid phantom in dosimetry purposes.Keywords: mangrove wood; solid water phantom; water equivalent phantom; EGSnrc; depth dose.

Key concepts: Imaging phantom, Monte Carlo method, Materials science, Dosimetry, Beam (structure), Photon, Nuclear medicine, Biomedical engineering

Related papers

Back to paper searchBrowse research topicsOriginal source
Calculation of dose distribution on Rhizophora spp soy protein phantom at 6 MV photon beam energy using Monte Carlo method — Research Paper | ScholarLens