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Design of a Monte Carlo model based on dual-source computed tomography (DSCT) scanners for dose and image quality assessment using the Monte Carlo N-Particle (MCNP5) code

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The purpose of this work was to develop and validate a Monte Carlo model for a Dual Source Computed Tomography (DSCT) scanner based on the Monte Carlo N-particle radiation transport computer code (MCNP5). The geometry of the Siemens Somatom Definition CT scanner was modeled, taking into consideration the x-ray spectrum, bowtie filter, collimator, and detector system. The accuracy of the simulation from the dosimetry point of view was tested by calculating the Computed Tomography Dose Index (CTDI) values. Furthermore, typical quality assurance phantoms were modeled in order to assess the imaging aspects of the simulation. Simulated projection data were processed, using the MATLAB software, in order to reconstruct slices, using a Filtered Back Projection algorithm. CTDI, image noise, CT-number linearity, spatial and low contrast resolution were calculated using the simulated test phantoms. The results were compared using several published values including IMPACT, NIST and actual measurements. Bowtie filter shapes are in agreement with those theoretically expected. Results show that low contrast and spatial resolution are comparable with expected ones, taking into consideration the relatively limited number of events used for the simulation. The differences between simulated and nominal CT-number values were small. The present attempt to simulate a DSCT scanner could provide a powerful tool for dose assessment and support the training of clinical scientists in the imaging performance characteristics of Computed Tomography scanners.
Rocznik
Strony
11--20
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
  • Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
  • Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
  • Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
  • Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
  • Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
  • Medical Physics Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece
Bibliografia
  • [1] Kyriakou Y, Kachelrieβ M, Knaup M, et al. Impact of the z-flying focal spot on resolution and artifact behavior for a 64-slice spiral CT scanner. Eur Radiol. 2006;16(6):1206-1215.
  • [2] Flohr TG, McCollough CH, Bruder H, et al. First performance evaluation of a dual source CT (DSCT) system. Eur Radiol. 2006;16(2):256-268.
  • [3] Deak P, Straten M, Shrimpton P, Zankl M. Validation of a Monte Carlo tool for patient-specific dose simulations in multi-slice computed tomography. Eur Radiol. 2008;18(4):759-772.
  • [4] Long D, Lee C, Tien C, et al. Monte Carlo simulations of adult and pediatric computed tomography exams: Validation studies of organ doses with physical phantoms. Med Phys. 2013;40(1):013901.
  • [5] Jarry G, DeMarco J, Beifuss U, et al. A Monte Carlo-based method to estimate radiation dose from spiral CT: from phantom testing to patient-specific models. Phys Med Biol. 2003;48(16):2645-2663.
  • [6] Ay MR, Zaidi H. Development and validation of MCNP4C-based Monte Carlo simulator for fan- and cone-beam x-ray CT. Phys Med Biol. 2005;50(20):4863-4885.
  • [7] Kyriakou Y, Kalender WA. Intensity distribution and impact of scatter for dual-source CT. Phys Med Biol. 2007;52(23):6969-6989.
  • [8] Wysocka-Rabin A, Qamhiyeh S, Jäkel O. Simulation of computed tomography (CT) images using a Monte Carlo approach. Nukleonika. 2011;56(4):299-304.
  • [9] Qamhiyeh S, Wysocka-Rabin A, Jäkel O. Monte Carlo calculated CT numbers for improved heavy ion treatment planning. Nukleonika. 2014;59(1):15-23.
  • [10] Abadi E, Harrawood B, Sharma S, et al. DukeSim: A Realistic, Rapid, and Scanner- Specific Simulation Framework in Computed Tomography. IEEE Trans Med Imaging. 2019;38(6):1457-1465.
  • [11] X-5 Monte Carlo Team. MCNP-a general Monte Carlo N-particle transport code. Version 5. Los Alamos: NM: Los Alamos National Laboratory; 2003.
  • [12] ImPACT CT scanner evaluation group. Centre for Evidence-based Purchasing (CEP). Comparative Specifications - 64slice CT scanners, guide CEP08027, NHS PASA; March 2009.
  • [13] Krauss B, Schmidt B, Flohr TG. Dual Source CT. In: Johnson TR, Fink C, Schoenberg SO, Reiser MF (eds). Dual Energy CT in Clinical Practice, Springer-Verlag Berlin Heidelberg; 2011, pp 11-20.
  • [14] www.siemens-healthineers.com [Internet]. Deutschland: Siemens Healthcare; c2019 [accessed October 1, 2019]. Available from: https://www.siemens-healthineers.com.
  • [15] Siemens Healthcare [Internet]. Deutschland: OEM & electronics-Siemens Helathcare; c2019 [cited 2019 Oct 12]. Available from: https://health.siemens.com/booneweb/
  • [16] Primak AN, Ramirez Giraldo JC, Liu X, et al. Improved dual-energy material discrimination for dual-source CT by means of additional spectral filtration. Med Phys. 2009;36(4):1359-1369.
  • [17] Johnson TRC. Dual-Energy CT: General Principles. AJR Am J Roentgenol. 2012;199:S3-S8.
  • [18] Schardt P, Deuringer J, Freudenberger J, et al. New X-ray tube performance in computed tomography by introducing the rotating envelope tube. Med Phys 2004;32(9):2699-2706.
  • [19] DeMarco JJ, Cagnon CH, Cody DD, et al. A Monte Carlo based method to estimate radiation dose from multidetector CT (MDCT): cylindrical and anthropomorphic phantoms. Phys Med Biol. 2005;50(17):3989-4004.
  • [20] Kramer R, Cassola VF, Andrade ME, et al. Mathematical modeling of scanner-specific bowtie filters for Monte Carlo CT dosimetry. Phys Med Biol. 2017;62(3):781-809.
  • [21] Gu J, Bednarz B, Caracappa PF, Xu XG. The development, validation and application of a multi-detector CT (MDCT) scanner model for assessing organ doses to the pregnant patient and the fetus using Monte Carlo simulations. Phys Med Biol. 2009;54(9):2699-2717.
  • [22] Lee C, Kim KP, Long D, et al. Organ doses for reference adult male and female undergoing computed tomography estimated by Monte Carlo simulations. Med Phys. 2011;38(3):1196-1206.
  • [23] Herman G T. Image Reconstruction from Projections. The Fundamentals of Computerized Tomography. New York, Academic Press 1980.
  • [24] Hubbell JH, Seltzer SM. 2004 Tables of X-Ray Mass Attenuation Coefficients and Mass Energy-Absorption Coefficients [Internet]. NIST: Gaithersburg, MD; c2019 [cited 2019 October 12]. Available from: https://www.nist.gov/pml/x-ray-mass-attenuationcoefficients
  • [25] Gulliksrud K, Stokke C, Martinsen AC. How to measure CT image quality: Variations in CT-numbers, uniformity and low contrast resolution for a CT quality assurance phantom. Physica Medica. 2014;30(4):521-526.
  • [26] Sharma DS, Sharma SD, Sanu KK, et al. Performance evaluation of a dedicated computed tomography scanner used for virtual simulation using in-house fabricated CT phantoms. J Med Phys 2006;31(1):28-35.
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
Identyfikator YADDA
bwmeta1.element.baztech-e7b4ebc1-9af0-459d-a676-92e5e885f4bf
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