International Journal of Contents

The Korea Contents Association (한국콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

Geant4-DICOM Interface-based Monte Carlo Simulation to Assess Dose Distributions inside the Human Body during X-Ray Irradiation

  • Received : 2012.04.01
  • Accepted : 2012.06.07
  • Published : 2012.06.28
Download PDF
⟨ Previous Next ⟩

Abstract

This study uses digital imaging and communications in medicine (DICOM) files acquired after CT scan to obtain the absorbed dose distribution inside the body by using the patient's actual anatomical data; uses geometry and tracking (Geant)4 as a way to obtain the accurate absorbed dose distribution inside the body. This method is easier to establish the radioprotection plan through estimating the absorbed dose distribution inside the body compared to the evaluation of absorbed dose using thermo-luminescence dosimeter (TLD) with inferior reliability and accuracy because many variables act on result values with respect to the evaluation of the patient's absorbed dose distribution in diagnostic imaging and the evaluation of absorbed dose using phantom; can contribute to improving reliability accuracy and reproducibility; it makes significance in that it can implement the actual patient's absorbed dose distribution, not just mere estimation using mathematical phantom or humanoid phantom. When comparing the absorbed dose in polymethly methacrylate (PMMA) phantom measured in metal oxide semiconductor field effect transistor (MOSFET) dosimeter for verification of Geant4 and the result of Geant4 simulation, there was $0.46{\pm}4.69%$ ($15{\times}15cm^2$), and $-0.75{\pm}5.19%$ ($20{\times}20cm^2$) difference according to the depth. This study, through the simulation by means of Geant4, suggests a new way to calculate the actual dose of radiation exposure of patients through DICOM interface.

Keywords

References

  1. Weiss S. (2009), "Privacy threat model for data portability in social network applications," International journal of information management, 29, pp. 249-254. https://doi.org/10.1016/j.ijinfomgt.2009.03.007
  2. Cristy and K. F. Eckerman, Specific Absorbed Fractions of Energy at Various Ages from Internal Photon Sources, 1. Methods, Appendix A, Description of the Mathematical Phantoms, Health and Safety Research Division, ORNL, 1987.
  3. F. Briesmeister Judith, MCNP-A General Monte carlo Code for Neutron and Photon Transport , LA7396-M, 1986.
  4. Korean Standards Association, X and gamma reference radiation for calibrating dosemeters and doserate meters and for determining their response as a function of photon energy. Part 1. Radiation characteristics and production methods, KSA ISO 4037-1, 2003.
  5. Philips Healthcare Global Information Center Document. http://incenter.medical.philips.com/doclib/enc/fetch/2000/4504/577242/577261/577263/670329/670330/5162255/EasyDiagnost_Eleva_with_digital_Radiography_Fluoroscopy_room_solutions_to_combine_both_applications_in_one_room_-_DRF_applications.pdf%3fnodeid%3d5162718%26vernum%3d2. 2009.
  6. Catalogue of Diagnostic X-Ray Spectra and other Data, IPEM, 1997.
  7. K. Amako, S. Guatelli, L. Urban et al., "Comparison of Geant4 Electromagntic Physics Models Against the NIST Reference Data," IEEE, 42(4), 2005, pp.910-918.
  8. E. Poon, F. Verhaegen, "Accuracy of the photon and electron physics in Geant4 for radiotherapy applications," Med. Phys, 32(6), 2005, pp.1696-1711. https://doi.org/10.1118/1.1895796
  9. A. Kimura, T. Aso, H. Yoshida et al., "DICOM Data Handling for Geant4-Based Medical Physics Application," IEEE, 4, 2004, pp.2124-2127.
  10. A.-M. Kim, S.-W. Kim, J.-W. Song, et al., "Radiation dose plan system based on particle simulation and volume rendering," Korea Computer Graphics Society, 12(3), 2006, pp.21-26.
  11. B. Cheng, P. Saw, L. Alphonse et al., "Determination of CT-To_Density conversion relationship for image-based Treatment Planning Systems," Medical Dosimetry, 30(3), 2005, pp.145-148. https://doi.org/10.1016/j.meddos.2005.05.001
  12. Beardmore et al., "Evaluation of MVCT images with skin collimation for electron beam treatment planning," J Appl Clin Med Phys, 9(3), 2008, pp.43-57. https://doi.org/10.1120/jacmp.v9i3.2773
  13. W. Schneider, T. Bortfeld, W. Schlegel, "Correlation : between CT numbers and tissue parameters needed for Monte carlo simulations of clinical dose distributions," Phys Med Biol, 45, 2000, pp.459-478. https://doi.org/10.1088/0031-9155/45/2/314
  14. G. Jarry, J. J. DeMarco, U. Beifuss, C. H. Cagnon and M. F. McNitt-Gray, "A Monte carlo-based method to estimate radiation dose from spiral CT :from phantom testing to patient-specific models," Phys Med Biol, 48, 2003, pp.2645-2663. https://doi.org/10.1088/0031-9155/48/16/306
  15. J. R. Greig, R. W Miller, P. Okunieff, "An approach to dose measurement for total body irradiation," Int J Radiat Oncol Biol Phys, 36, 1996, pp.463-468. https://doi.org/10.1016/S0360-3016(96)00268-4
  16. P. C.Lee, J. M. Sawicka, G. P Glasgow, "Patient dosimetry quality assurance program with a commercial diode system," Int J Radiat Oncol Biol Phys, 29, 1994, pp.1175-1182. https://doi.org/10.1016/0360-3016(94)90415-4
  17. E. B. Podgorsak, C. Pla, M. Evans, M. Pla, "The influence of phantom size on output factor, peak scatter factor, and percentage depth dose in large-field photon irradiation," Med Phys, 12, 1985, pp.639-45. https://doi.org/10.1118/1.595686
  18. T. Aso, A. Kiura, T. Yamashita, T. Sasaki, Optimization of Patient Geometry Based on CT data in Geant4 for Medical Application. Nuclear Science Symposium Conference Record, pp.2576-2580, 2007.

Cited by

  1. A Comparative Evaluation of Organ Doses in Infants and toddlers between Axial and Spiral CT Scanning vol.7, pp.2, 2013, https://doi.org/10.7742/jksr.2013.7.2.137