Local Radiation Dosimetry Method using Optically Stimulated Pulsed Luminescence and Monte Carlo Simulation

Mohammed Talbi, M'hamed El Mansouri, Mounir Ben Messaoud, Rajaa Sebihi, Morad Erraoudi, Yassine Azakhmam, Mohammed Khalis

Abstract


Background: This is the first study that has been done in Morocco with the aim of optimizing protection and protocols in diagnostic radiology, by using Monte Carlo simulation and Optically Stimulated Luminescence (OSL). 

Methods: Measurements have been performed with solid (AGMS-D+) and OSL detectors to determine the Air Kerma and the backscattering factor on a diagnostic radiology unit.

Results: The spectra simulated by GATE were in a good adequacy with spectra generated by IPEM  report 78, with slight differences in the X-rays intensity characteristic, and there was no statistically significant difference between Air Kerma simulated with GATE and those measured using the AGMS-D+ and OSL (P < 0.01).

Conclusion: Monte Carlo simulation responses were suitable and could provide an accurate alternative for Air Kerma and the entrance surface dose determination with non‐uniform primary x‐ray beams.    

Keywords: Radiation Dosimetry; Pulsed Luminescence; Monte Carlo Simulation 


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References


Ay MR, Sarkar S, Shahriari M, Sardari D, Zaidi H. Assessment of different computational models for generation of x-ray spectra in diagnostic radiology and mammography 2005. pp. 1660-1675.

Fewell TR, Shuping RE. Photon energy distribution of some typical diagnostic x-ray beams. Medical Physics, (1977); 4(3): 187-197.

Boone JM, McNitt-Gray MF, Hernandez AM. Monte Carlo Basics for Radiation Dose Assessment in Diagnostic Radiology. Journal of the

American College of Radiology, (2017); 14(6): 793-794.

Evans S. Catalogue of Diagnostic X-Ray Spectra and Other Data. Journal of Radiological Protection, (1998); 18(1).

Boone JM, Seibert JA. Monte Carlo simulation of the scattered radiation distribution in diagnostic radiology. Medical Physics, (1988).

Kramer R, Khoury HJ, Vieira JW. CALDose_X – A software tool for the assessment of organ and tissue absorbed doses, effective dose and cancer risks in diagnostic radiology. Physics in Medicine and Biology, (2008); 53(22): 6437-6459.

Baek C-h, Lee S-j, Kim D. Diagnostic X-ray Spectra Detection by Monte Carlo Simulation. (2018); 12(3): 289-295.

Cao J, Jiang CY, Zhao YF, Yang QW, Yin ZJ. A novel X-ray tube spectra reconstruction method based on transmission measurements. Nuclear Science and Techniques, (2016); 27(2).

Salehi Z, Ya Ali NK, Yusoff AL. X-ray spectra and quality parameters from Monte Carlo simulation and analytical filters. Applied Radiation and Isotopes, (2012); 70(11): 2586-2589.

Sarrut D, Bardiès M, Boussion N, Freud N, Jan S, et al. (2014) A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications. John Wiley and Sons Ltd.

Benmakhlouf H, Bouchard H, Fransson A, Andreo P. Backscatter factors and mass energy-absorption coefficient ratios for diagnostic radiology dosimetry. Physics in Medicine and Biology, (2011); 56(22): 7179-7204.

International Commission on Radiation Units and Measurements (ICRU).

Iaea TRS457; https://www-pub.iaea.org/MTCD/publications/PDF/TRS457_web.pdf

Musa Y, Hashim S, Karim MKA, Bakar KA, Ang WC, et al. Response of optically stimulated luminescence dosimeters subjected to X-rays in diagnostic energy range. Journal of Physics: Conference Series, (2017); 851(1): 7-13.

Musa Y, Hashim S, Ghoshal SK, Ahmad NE, Bradley DA, et al. Effectiveness of Al2O3:C OSL dosimeter towards entrance surface dose measurement in common X-ray diagnostics. Radiation Physics and Chemistry, (2019); 165.

Musa Y, Hashim S, Khalis M, Karim A. Direct and indirect entrance surface dose measurement in X-ray diagnostics using nanoDot OSL dosimeters: Yahaya Musa et al. J Phys: Conf Ser, (2019); 124812014-12014.

Takegami K, Hayashi H, Nakagawa K, Okino H, Okazaki T, et al. Measurement method of an exposed dose using the nanoDot dosimeter. European Congress of radiology (EPOS), (2015); (April): 1-16.

Petoussi-Henss N, Zankl M, Drexler G, Panzer W, Regulla D. Calculation of backscatter factors for diagnostic radiology using Monte Carlo methods. Physics in Medicine and Biology, (1998); 43(8): 2237-2250.

Adeli R, Pezhman Shirmardi S, Amiri J, Singh VP, Medhat M. Simulation and comparison of radiology X-ray spectra. Journal of Paramedical Sciences, (2015); 6(4): 8-14.

Tran KA, Truong LTH, Mai NV, Dang PN, Vo DTT. Study on the characteristics of X-ray spectra in imaging diagnosis using Monte Carlo simulations. Journal of the Korean Physical Society, (2016); 69(7): 1168-1174.

Ay MR, Shahriari M, Sarkar S, Adib M, Zaidi H. Monte Carlo simulation of x-ray spectra in diagnostic radiology and mammography using MCNP4C. Physics in Medicine and Biology, (2004); 49(21): 4897-4917.

Adeli R, Shirmardi SP, Amiri J, Singh VP, Medhat ME. Simulation and comparison of radiology X-ray spectra by MCNP and GEANT4 codes. Journal of Paramedical Sciences, (2015); 6(4): 8-14.

Shimizu K, Koshida K, Miyati T. Monte Carlo Simulation Analysis of Backscatter Factor for Low-Energy X-Ray. EGS4 Users' Meeting, KEK Proceedings, (2001); 115-118.




DOI: http://dx.doi.org/10.62940/als.v8i2.1063

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