Prospective Monte Carlo Simulation for Choosing High Efficient Detectors for Small-Field Dosimetry

  • Donya H
  • Seniwal B
  • Darwesh R
  • et al.
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Abstract

In this chapter, a detailed study on physics and methodology of small field dosimetry are reported. It introduces talking about how small radiation fields came into existence and the importance of accurate small-field dosimetry. In addition, it discusses small and long cavity theories for evaluating accurate dose response. It sheds the spot on pencil beam algorithms for evaluating dose response and uses Monte Carlo (MC) simulation in categorizing primary and scattering components of the radiotherapeutic photon beam. Moreover, it summarizes all commercial dosimeters used in small-field dosimetry. It gives good knowledge about detectors and equipment like ionization chambers for reference dosimetry in small and non- reference fields and different types of solid-state detector. The importance and applications of Monte Carlo techniques in small-field dosimetry and radio- therapeutic treatment methods based on small field are reported. For this purpose, different commonly used Monte Carlo codes are handled like Electron Gamma Shower (EGSnrc), Geant4, PENELOPE, and Monte Carlo N-Particle (MCNP). A review on the recent studies of using Monte Carlo simulation particularly on the small-field dosimetric studies is also reported. This chapter also discusses the rec- ommendations of the code of practices (COPs) for dosimetry of small radiation fields. It mentions all recommendations provided by TRS-483 for accurate beam data collection and accurate dosimetric measurements. It gives good knowledge to the user for selecting a suitable dosimeter in small-field dosimetry through investigation of different practical methods and Monte Carlo simulations.

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APA

Donya, H., Seniwal, B., Darwesh, R., & C.F. Fonseca, T. (2019). Prospective Monte Carlo Simulation for Choosing High Efficient Detectors for Small-Field Dosimetry. In Theory, Application, and Implementation of Monte Carlo Method in Science and Technology. IntechOpen. https://doi.org/10.5772/intechopen.89150

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