Monte Carlo radiation transport in external beam radiotherapy

Year 2013, Volume: 3 Issue: 1, 1 - 5, 23.06.2013

Yiğit Çeçen

https://doi.org/10.17678/beuscitech.47131

Cited By: 2

Abstract

The use of Monte Carlo in radiation transport is an effective way to predict absorbed dose distributions. Monte Carlo modeling has contributed to a better understanding of photon and electron transport by radiotherapy physicists. The aim of this review is to introduce Monte Carlo as a powerful radiation transport tool. In this review, photon and electron transport algorithms for Monte Carlo techniques are investigated and a clinical linear accelerator model is studied for external beam radiotherapy. The statistical uncertainties and variance reduction techniques for Monte Carlo simulation are also discussed.

Keywords

Electron transport , Monte Carlo , photon transport , radiotherapy

References

• Ayyangar KM, Jiang SB (1998). Do we Need Monte Carlo Treatment Planning for LINAC Based Radiosurgery? A case study. Med Dosim 23, 161-168.
• Bielajew AF (2001). Fundamentals of the Monte Carlo method for neutral and charged particle transport, Ann Arbor, Michigan, The University of Michigan.
• Cecen Y (2008). Monte Carlo Simulation in Radiotherapy. The Institute for Graduate Studies in Science and Engineering, PhD. Thesis, Hacettepe University, 62 pp.
• Chaves A, Alves C, Fragoso M, et al. (2001). EGS4 and MCNP4b MC Simulation of a Siemens KD2 Accelerator in 6MV Photon Mode. Laboratorio De Instrumenta Cao E Fisica Experimental De Particulas.
• ICRU-50 (1993). International Commission on Radiation Units and Measurements. Prescribing, Recording, and Reporting Photon Beam Therapy ICRU Report 50, Washington, DC.
• Leal A, Doblado SF, Arrans R, et al. (2003). Routine IMRT Verification by Means of an Automated Monte Carlo Simulation System. Int J Radiat Oncol Biol Phys 56, 58- 68.
• Malataras G, Kappas C, Lovelock DMJ, et al. (1996). Simulation with EGS4 Code of External Beam of Radiotherapy Apparatus with Workstation and PC Gives Similar Results? Comput Meth Prog Bio 52, 45- 51.
• Moraleda M, Gomez-Ros JM, Lopez MA, et al (2004). A MNCP Based Calibration Method and a Voxel Phantom for in-vivo Monitoring Am-241 in Skull. Nucl Instrum & Meth Phys Res 526, 551-559.
• Perez (2008). Principles and Practice of Radiation Oncology (5th Edit.), Halperin EC, Perez CA, Brady LW, Wilkins LW, ISBN-13: 978-0-7817-6369-1, ISBN-10: 0- 7817-6369-X
• Reynaert N, De Smedt B, Coghe M, et al. (2004). MCDE: A New Monte Carlo Dose Engine for IMRT. Physics in Medicine and Biology.
• Rodrigues P, Trindade A, Peralta L, et al. (2004). Application of GEANT4 radiation transport toolkit to dose calculations in anthropomorphic phantoms. Appl Radiat Isotop 61, 1451-1461.
• Scholz C, Schulze C, Oelfke U, et al. (2003). Development and Clinical Application of a Fast Superposition Algorithm in Radiation Therapy. Radiother Oncol 69, 79-90.
• Schwarz M, Bos LJ, Mijnheer BJ, et al. (2003). Importance of Accurate Dose Calculations Outside Segment Edges in IMRT Planning. Radiother Oncol 69, 305-314.
• Trindade A, Rodrigues P, Peralta L, et al. (2003). Fast Electron Beam Simulation and Dose Calculation in Radiotherapy. Nucl Instrum Meth Phys Res 522, 568- 578.
• Van Dyk J (1999). The modern technology of radiation oncology: a compendium for medical physicists and radiation oncologists, Volume 1, Medical Physics Publications, 1072 pp. ISBN: 0-9448-3838-3, 978-0- 9448-3838-9.
• Zaidi H, Sgouros G (2003). Therapeutic Applications of Monte Carlo Calculations in Nuclear Medicine. Institute of Physics Publishing, 364 pp. ISBN: 0-7503- 816-8.