Simulation of surface flux received through breast tumor radiation therapy with MCNPX code
محورهای موضوعی : Photon Therapy, Diagnosis and Equipment
Parsa Afshin
1
(Department of Biomedical Engineering, Islamic Azad University of Isfahan (Khorasgan) branch, Isfahan, Iran.)
Sharifeh Shahi
2
(Laser and Biophotonics in Biotechnologies Research Center, Islamic Azad University of Isfahan (Khorasgan) branch, Isfahan, Iran.)
Farhad Azimi Far
3
(Department of Biomedical Engineering, Islamic Azad University of Isfahan (Khorasgan) branch, Isfahan, Iran.)
کلید واژه: breast tumor, Radiotherapy, Absorbed dose distribution, Surface flux, Code MCNPX,
چکیده مقاله :
In radiation therapy, investigating of the effects of the surface flux reaching on the tissue is important in planning the treatment and this requires a precise evaluation of the absorbed dose distribution throughout the irradiated tissue. Therefore, by Monte Carlo simulation with MCNP code, a point source with the size of E with a spectrum width of 0.6 µm with a single energy transfer of 6 MeV to the breast tumor tissue with a size of 2 x 4 x 4 cm and also a density (Kg/m^3) of 11.34 at a fixed depth of 3 cm. It is radiated from a standard phantom (VIP MAN) made of tissue. The results show the highest surface flux that received on the tumor is around 9.97 × 〖10〗^(-6) and is located almost in the center of the tumor in dimensions (-0.75 cm - 1.3 cm) and the less surface flux around the tumor is caused by the rate of the dose which is distributed. Also, the template phenomenon in the creation of electrons is based on the Compton effect, while in the creation of photons, the Compton effect did not occur.
[1] R. Khabaz, “Phantom dosimetry and cancer risks estimation undergoing 6 MV photon beam by an Elekta SL-25 linac,” Appl. Radiat. Isot. vol. 163, pp. 109232, 2020.
[2] Z. Xu, X. Chen, Z. Sun, C. Li, and B. Jiang, “Recent progress on mitochondrial targeted cancer therapy based on inorganic nanomaterials,” Mater. Today Chem. vol. 12, pp. 240–260, 2019.
[3] S. Siddique and J. C. L. Chow, “Artificial intelligence in radiotherapy,” Reports Pract. Oncol. Radiother. vol. 25, no. 4, pp. 656–666, 2020.
[4] J. C. L. Chow, “Artificial intelligence in radiotherapy and patient care,” in Artificial Intelligence in Medicine, Springer, vol. 95, pp. 1–13, 2021.
[5] J. F. Briesmeister, “MCNPTM-A general Monte Carlo N-particle transport code,” Version 4C, LA-13709-M, Los Alamos Natl. Lab. 2000.
[6] I. G Evseev, H. R Schelin, S. A Paschuk, E. Milhoretto, J. A P Setti, O. Yevseyeva, J. T de Assis, J. M Hormaza, K. S Díaz, and R. T Lopes, “Comparison of SRIM, MCNPX and GEANT simulations with experimental data for thick Al absorbers,” Appl. Radiat. Isot. vol. 68, no. 4–5, pp. 948–950, 2010.
[7] M. Mirzaie, A. A. Mowlavi, S. Mohammadi, and H. Mirshekarpour, “Absorbed dose calculation from beta and gamma rays of 131I in ellipsoidal thyroid and other organs of neck with MCNPX code,” ISMJ, vol. 15, pp. 201–208, 2012.
[8] N. Azadegan, M. Hassanpour, M. U. Khandaker, M. R. I. Faruque, K. S. Al-mugren, and D. A. Bradley, “Calculation of secondary radiation absorbed doses due to the proton therapy on breast cancer using MCNPX code,” Radiat. Phys. Chem. vol. 183, pp. 109427, 2021.
[9] X. G. Xu and K. F. Eckerman, Handbook of anatomical models for radiation dosimetry, CRC Press, 2009.
[10] U. Javaid, K. Souris, S. Huang, and J. A. Lee, “Denoising proton therapy Monte Carlo dose distributions in multiple tumor sites: A comparative neural networks architecture study,” Phys. Medica, vol. 89, pp. 93–103, 2021.
[11] N. Reynaert, S.C. van der Marck, D.R. Schaart, W. Van der Zee, C. Van Vliet-Vroegindeweij, M. Tomsej, J. Jansen, B. Heijmen, M. Coghe, and C. De Wagter, “Monte Carlo treatment planning for photon and electron beams,” Radiat. Phys. Chem. vol. 76, pp. 643–686, 2007.
[12] D. Sarrut, M. Bardiès, N. Boussion, N. Freud, S. Jan, Jean Michel Létang, G. Loudos, L. Maigne, S. Marcatili, T. Mauxion, P. Papadimitroulas, Y. Perrot, U. Pietrzyk, C. Robert Robert, D. R. Schaart, D. Visvikis, and I. Buvat , “A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications,” Med. Phys. vol. 41, pp. 64301(1-14), 2014.
