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2019 | Vol. 25, Iss. 3 | 189--192
Tytuł artykułu

SIMU-RAD programme: a learning tool for radiation (photons and charged particles) interaction

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Radiation education is necessary for a wide variety of people, such as radiation workers particularly for students of secondary school and higher education institution who learn radiation sciences. The fact that we could not see or feel radiation makes it difficult to understand it. The use of radiation trajectories shown on a personal computer should be useful to overcome this difficulty. In order to understand radiation behaviour inside the material, we have developed a Simu-Rad (Copyright: LY2018002738) by using Monte Carlo simulation programme. One who has no programming knowledge is able to simulate photons in a material through the developed programme. The program could become a computer aided learning tool for radiation related courses. We aim to facilitate lecturer from ‘The Traditional Classroom’ to ‘The Flipped Classroom’ for radiation education concerning in the era of IR 4.0. To validate our radiation simulator, we calculate photon linear attenuation coefficient (µ) of an aluminium material which commonly used as a filter in diagnostic radiology. µ is one of the main characteristics to understand how the radiation attenuated inside the materials. We calculate at energy photon of 662 keV (Cs-137 radiation source) to compare our results of µ with the XCOM database. Consequently, the results from the developed simulator comparable with the database verified our programme to be used for radiation study.
Wydawca

Rocznik
Strony
189--192
Opis fizyczny
Bibliogr. 21 poz., rys.
Twórcy
  • School of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Malaysia , suffian@unisza.edu.my
  • School of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Malaysia
Bibliografia
  • [1] Harrison RL. Introduction to Monte Carlo Simulation. AIP Conf Proc. 2010;1204:17-21.
  • [2] Metropolis N, Ulam S. The Monte Carlo Method. J Am Stat Assoc. 1949;44(247)335-341.
  • [3] Hirayama H, Namito Y. Lecture Notes of Radiation Transport Calculation by Monte Carlo Method. KEK Internal.2001;2000-20.
  • [4] Dimov IT, Tonev OI. Monte Carlo Algorithms: Performance Analysis for Some Computer Architectures. J Comput Appl Math. 1993;48(3):253-277.
  • [5] Peter Ziegenhein, Pirner S, Ph Kamerling C, Oelfke U. Fast CPU-based Monte Carlo Simulation for Radiotherapy Dose Calculation. Phys Med Biol. 2015;60(15):6097-6111.
  • [6] Kara U, Tekin HO. Estımatıon of Absorbed Dose Dıstrıbutıon in Dıfferent Organs durıng the CT Scan: Monte Carlo Study. Austin J Radiol. 2017;4(1):1063.
  • [7] Larsson E, Ljungberg M, Strand SE, Jonsson BA. Monte Carlo Calculations of Absorbed Doses In Tumours using A Modified Moby Mouse Phantom for Pre-Clinical Dosimetry Studies. Acta Oncol. 2011;50:6,973-980.
  • [8] Sukara S, Rimjeam S. Simulation of Gamma Rays Attenuation Through Matters using the Monte Carlo Program. J Phys: Conf Ser. 2017;901:012141.
  • [9] Hirayama H, Namito Y, Bielajew AF, et al. The EGS5 Code System. 2005; SLAC-Report-730.
  • [10] Nelson WR, Field C. Comparison of EGS5 Simulations with Experiment. Nuclear Instrument and Methods in Physics Research. 2007;572(3):1083-1093.
  • [11] Tajudin SM, Namito Y, Sanami T, Hirayama H. Quasi-monoenergetic 200 keV Photon Field using a Radioactive Source with Backscatter Layout. Japan J Appl Phys. 2014;53(11):116401.
  • [12] Malik AH, Shimazoe K, Takahashi H. EGS5 Simulations for the Development of Non-Contacting System to Online Measure the Radiotracer Concentration in Blood. Prog Nucl Sci Technol. 2014;4:290-293.
  • [13] Hubbell JH, Seltzer SM. X-Ray Mass Attenuation Coefficients. NISTIR 5632. https://www.nist.gov/pml/x-ray-mass-attenuationcoefficients.
  • [14] Berger MJ, Hubbell JH, Seltzer SM, et al. XCOM: Photon Cross Sections Database. NBSIR 87-3597. http://www.nist.gov/pml/data/xcom/index.cfm.
  • [15] Pawar PP. Measurement of Mass and Linear Attenuation Coefficients of Gamma-Rays of AL for 514, 662 and 1280 keV Photons. J Chem Pharm Res. 2011;3(4): 899-903.
  • [16] Tekin HO, Manici T. Simulations of Mass Attenuation Coefficients for Shielding Materials using The MCNP-X code. Nucl Sci Techniques. 2017;28:95.
  • [17] Zhang L, Jia MC, Gong JJ, Xia WM. Simulation of Photon Attenuation Coefficients for High Effective Shielding Material Lead-Boron Polyethyene. IOP Conf Ser: Earth Environ Sci. 2017; 100:012137.
  • [18] Ferreira CC, Ximenes RE, Garcia CAB, et al. Total Mass Attenuation Coefficient Evaluation of Ten Materials Commonly used to Simulate Human Tissue. J Phys: Conf Ser. 2010;249:012029.
  • [19] Knoll GF. Radiation Detection and Measurement. New York: John Wiley & Sons, Inc. 2010.
  • [20] Suffian MT, Adila Hanim AS. Simu-Rad. Poster presented at Putra InnoCreative Carnival in Teaching and Learning (PicTL’18); 2018; Universiti Putra Malaysia, Malaysia.
  • [21] CGVIEW Particle Trajectory and Geometry Display Program. http://rcwww.kek.jp/research/egs/kek/cgview/
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-ff43c5ac-7150-41fa-bd30-55c0ee6dd1a1
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