Research article Special Issues

Monte Carlo dose index estimation in computed tomography

  • Received: 27 April 2020 Accepted: 27 June 2020 Published: 15 July 2020
  • We numerically study the computed tomography dose index (CTDI) quantity based on the Monte Carlo method using GATE software. In this work, it was demonstrated that the CTDI values decreased following an exponential form as a function of phantom diameter. As expected, the absorbed dose is shown to have a good relationship which increases linearly with X-ray tube current (mAs) values. The simulation presented in particularly that the (CTDI) dose increases not-linearly dependence with photon deposited energy (kVp). It seems that the average percent of the absorbed dose in the abdominal phantom was lower than the heat phantom object's absorbed dose, which was equal to 80%. In conclusion, the use of Monte Carlo simulation represents a dosimetry tool for radiation protection in the field of radiology imaging.

    Citation: Moncef ATI, Rachid BOUAMRANE, Djamel ADDI, Fatima Zohra MAROC, Fatima Zohra Mecheret. Monte Carlo dose index estimation in computed tomography[J]. AIMS Bioengineering, 2020, 7(4): 224-235. doi: 10.3934/bioeng.2020019

    Related Papers:

  • We numerically study the computed tomography dose index (CTDI) quantity based on the Monte Carlo method using GATE software. In this work, it was demonstrated that the CTDI values decreased following an exponential form as a function of phantom diameter. As expected, the absorbed dose is shown to have a good relationship which increases linearly with X-ray tube current (mAs) values. The simulation presented in particularly that the (CTDI) dose increases not-linearly dependence with photon deposited energy (kVp). It seems that the average percent of the absorbed dose in the abdominal phantom was lower than the heat phantom object's absorbed dose, which was equal to 80%. In conclusion, the use of Monte Carlo simulation represents a dosimetry tool for radiation protection in the field of radiology imaging.


    加载中

    Acknowledgments



    The study was performed at the University of Sciences and technology-MB of Oran, Algeria. The authors acknowledge with gratitude the support from the Faculty of Physics. Our sincere thanks go out to all IBN BAJA Supercomputer team.

    Conflict of interest



    The authors declare no conflict of interest.

    Author contributions



    M.A. and R.B. conceived of the presented idea and performed the numerical simulations, to the analysis of the results and wrote the manuscript with input from all authors.

    [1] Ghavami SM, Mesbahi A, Pesianian I (2012) Patient doses from X-ray computed tomography examinations by a single array detector unit: Axial versus spiral mode. Int J Radiat Res 10: 89-94.
    [2] Ngaile JE, Msaki PK (2006) Estimation of patient organ doses from CT examinations in Tanzania. J Appl Clin Med Phys 7: 80-94. doi: 10.1120/jacmp.v7i3.2200
    [3] Saravanakumar A, Vaideki K, Govindarajan KN, et al. (2016) Establishment of CT diagnostic reference levels in select procedures in South India. Int J Radiat Res 14: 341-347.
    [4] Kramer R, Cassola VF, Andrade MEA, et al. (2017) Mathematical modelling of scanner-specific bowtie filters for Monte Carlo CT dosimetry. Phys Med Biol 62: 781-809. doi: 10.1088/1361-6560/aa5343
    [5] (2006) National Research CouncilHealth Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII Phase 2. Washington DC: The National Academies Press.
    [6] ICRP, ICRP Publication 103, The 2007 Recommendations of the International Commission on Radiological Protection, 2007.Available from: https://journals.sagepub.com/doi/pdf/10.1177/ANIB_37_2-4.
    [7] Liu FL, Wang G, Cong WX, et al. (2013) Dynamic bowtie for fan-beam CT. J X-Ray Sci Technol 21: 579-590. doi: 10.3233/XST-130386
    [8] Williams LE (2003) Therapeutic applications of Monte Carlo calculations in nuclear medicine. J Nucl Med 44: 991.
    [9] Fallah Mohammadi GR, Riyahi Alam N, Geraily G, et al. (2016) Thorax organ dose estimation in computed tomography based on patient CT data using Monte Carlo simulation. Int J Radiat Res 14: 313-321.
    [10] Guidez J, Saturnin A (2017) Evolution of the collective radiation dose of nuclear reactors from the 2nd through to the 3rd generation and 4th generation sodium-cooled fast reactors. EPJ Nuclear Sci Technol 3: 32. doi: 10.1051/epjn/2017024
    [11] Vafapour H, Salehi Z (2018) Comparison of incident air kerma (ki) of common digital and analog radiology procedures in kohgiluyeh and boyer-Ahmad province. Pol J Med Phys Eng 24: 37-41. doi: 10.2478/pjmpe-2018-0006
    [12] Cranley K, Gilmore BJ, Fogarty GWA (1991)  Data for Estimating X-ray Tube Total Filtration UK: Institute of Physical Sciences in Medicine.
    [13] Sahbaee P, Segars W P, Samei E (2014) Patient-based estimation of organ dose for a population of 58 adult patients across 13 protocol categories. Med Phys 41: 072104. doi: 10.1118/1.4883778
    [14] Birch R, Marshall M (1979) Computation of bremsstrahlung X-ray spectra and comparison with spectra measured with a Ge (Li) detector. Phys Med Biol 24: 505-517. doi: 10.1088/0031-9155/24/3/002
    [15] Lee CL, Kim HJ, Chung YH, et al. (2009) GATE simulations of CTDI for CT dose. J Korean Phys Soc 54: 1702-1708. doi: 10.3938/jkps.54.1702
    [16] Poiata A, Focea R, Creanga D (2012) Pathogen germs response to low-dose radiation—medical approach. EDP Sci 24: 06005.
    [17] (2012)  Comparison of different methods for measuring CT dose profiles with a new dosimetry phantom. Available form: http://www.irpa.net/members/P02.156.pdf.
    [18] Judy PF, Balter S, Bassano D, et al. (1977)  Phantoms for performance evaluation and quality assurance of CT scanners USA: American Association of Physicists in Medicine. doi: 10.37206/1
    [19] Panzer W, Shrimpton PC, Jessen K, et al.European guidelines on quality criteria for computed tomography, EU publications, 2000.Available form: https://op.europa.eu/en/publication-detail/-/publication/d229c9e1-a967-49de-b169-59ee68605f1a.
    [20] Siewerdsen JH, Waese AM, Moseley DJ, et al. (2004) Spektr: A computational tool for X-ray spectral analysis and imaging system optimization. Med Phys 31: 3057-3067. doi: 10.1118/1.1758350
    [21] Nagel HD (2000)  Radiation Exposure in Computed Tomography: Fundamentals, Influencing Parameters, Dose Assessment, Optimisation, Scanner Data, Terminology Hamburg: Offizin Paul Hartung Druck GmbH & Co. KGD-20020.
    [22] Hernandez AM, Boone JM (2014) Tungsten anode spectral model using interpolating cubic splines: unfiltered X-ray spectra from 20 kV to 640 kV. Med Phys 41: 042101. doi: 10.1118/1.4866216
    [23] Khoramian D, Sistani S, Hejazi P (2019) Establishment of diagnostic reference levels arising from common CT examinations in Semnan County, Iran. Pol J Med Phys Eng 25: 51-55. doi: 10.2478/pjmpe-2019-0008
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4365) PDF downloads(269) Cited by(0)

Article outline

Figures and Tables

Figures(10)  /  Tables(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog