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Determination of dosimetric dependence for effective atomic number of LDR brachytherapy seed capsule by Monte Carlo simulation

  • Berkay Camgoz (Ege University, Institute of Nuclear Sciences) ;
  • Dilara Tarim (Ege University, Institute of Nuclear Sciences)
  • Received : 2023.01.06
  • Accepted : 2023.04.10
  • Published : 2023.08.25

Abstract

Brachytherapy is a special case of radiotherapy. It should be arranged according to some principles in medical radiation applications and radiation physics. The primary principle is to use as low as reasonably achievable dose in all ionizing radiation applications for diagnostic and therapeutic treatments. Dosimetric distributions are dependent on radioactive source properties and radiation-matter interactions in an absorber medium such as phantom or tissue. In this consideration, the geometrical structure and material of the seed capsule, which surrounds a radioactive material, are directly responsible for isodose profiles and dosimetric functions. In this study, the radiometric properties of capsule material were investigated on dose distribution in a water phantom by changing its nuclear properties using the EGSnrc Monte Carlo (MC) simulation code. Effective atomic numbers of hypothetic mixtures were calculated by using different elements with several fractions for capsule material. Model 6711 brachytherapy seed was modeled by EGSnrc/Dosrcnrc Code and dosimetric functions were calculated. As a result, dosimetric parameters of hypothetic sources have been acquired in large-scale atomic number. Dosimetric deviations between the data of hypothetic seeds and the original one were analyzed. Unit dose (Gy/Particle) distributions belonging to different types of material in seed capsule have remarkably differed from the original capsule's data. Capsule type is major variable to manage the expected dose profile and isodose distribution around a seed. This study shows us systematically varied scale of material type (cross section or effective atomic number dependent) offers selective material usage in production of seed capsules for the expected isodose profile of a specific source.

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References

  1. M.J. Rivard, J.L.M. Venselaar, L. Beaulieu, The evolution of brachytherapy treatment planning, Jun, Med. Phys. 36 (6) (2009) 2136-2153. https://doi.org/10.1118/1.3125136
  2. J.M. Hannoun-Levi, Brachytherapy for prostate cancer: Present and future Cancer/Radiotherapie, 21, 2017, pp. 469-472. https://doi.org/10.1016/j.canrad.2017.06.009
  3. C. Boukarama, J.M. Hannoun-Levi, Management of prostate cancer recurrence after definitive radiation therapy, Cancer Treat Rev. 36 (2010) 91-100. https://doi.org/10.1016/j.ctrv.2009.06.006
  4. J.M. Rivard, B.M. Coursey, L.A. DeWerd, W.F. Hanson, M.S. Huq, G.S. Ibbott, M.G. Mitch, R. Nath, J.F. Williamson, American Association of Physicists in Medicine (AAPM) Task Group No.43 Report, 2004, p. 42p.
  5. B. Camgoz, M.N. Kumru, A Monte Carlo evaluation for effects of probable dimensional uncertainties of low dose rate brachytherapy seeds on dose, RPOR (1 9) (2014) 301-309.
  6. S Radiation Gad, Dosimetry Department of Nuclear and Biomedical Engineering Ben Gurion University Beer Sheva. Israel Instrumentation and Methods Second Edition, Boca Raton, London New York Washington. D.C, 2001.
  7. N.D. Mukhopadhyay, A.J. Sampson, D. Deniz, G.A. Carlsson, J. Williamson, A. Malusek, Estimating statistical uncertainty of Monte Carlo efficiency-gain in the context of a correlated sampling Monte Carlo code for brachytherapy treatment planning with non-normal dose distribution, Appl. Radiat. Isot. 70 (2012) 315-323. https://doi.org/10.1016/j.apradiso.2011.09.015
  8. J.A. Davies, C.M. Hockensmith, V.Y. Kukushkin, Y.N. Kukushkin, Synthetic Coordination Chemistry: Principles and Practice, World Scientific, 1996, p. 423p.
  9. D.J. Bernstein, Understanding Brute Force, Department of Mathe-matics, Statistics, and Computer Science, 2005, 2010, http://cr.yp.to/snue/bruteforce20050425.pdf.
  10. C.P.J. Pelzl, Understanding Cryptography, A Textbook for Students and Practitioners, Springer Heidelberg, Dordrecht London New York, 2010.
  11. R.E.P. Taylor, G. Yegin, D.W.O. Rogers, Benchmarking BrachyDose: voxel based EGSnrc Monte Carlo calculations of TG-43 dosimetry parameters, Med. Phys. 34 (2007) 445p.
  12. L.P. Reis, A. Facure, S.C. Cardoso, A.X. Silva, Characterization of some dosimetric parameters of 125I seeds used for prostate implants using Monte Carlo simulations, in: International Nuclear Atlantic Conference (INAC), Brazil, 2009, p. 10p.
  13. L. Lin, R.R. Patel, B.R. Thomadsen, D.L. Henderson, The use of directional interstitial sources to improve dosimetry in breast brachytherapy, Med. Phys. 35 (2008) 240-247. https://doi.org/10.1118/1.2815623
  14. V. Chaswal, B.R. Thomadsen, D.L. Henderson, Development of an adjoint sensitivity field-based treatment-planning technique for the use of newly designed directional LDR sources in brachytherapy, Phys. Med. Biol. 57 (2012) 963-982. https://doi.org/10.1088/0031-9155/57/4/963
  15. Y.S. William, K. Tanderup, B. Pieters, Emerging Technologies in Brachytherapy, CRC Press, 2017.
  16. F. Julio, R.G. Almansaa, M.O. Feras, et al., Dose distribution in water for monoenergetic photon point sources in the energy range of interest in brachytherapy: Monte Carlo simulations with PENELOPE and GEANT4, Radiat. Phys. Chem. 76 (2007) 766-773. https://doi.org/10.1016/j.radphyschem.2006.12.001
  17. G. Luxton, G. Jozsef, Radial dose distribution, dose to water and dose rate constant for monoenergetic photon point sources from 10 keV to 2MeV: EGS4 Monte Carlo model calculation, Med. Phys. 26 (1999) 2531-2538. https://doi.org/10.1118/1.598790