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Sci-Fri AM: YIS-03: Simulated annealing optimization of the pre-target electron beam in Monte Carlo virtual linac models.

作者信息

Bush K, Zavgorodni S, Beckham W

机构信息

Department of Physics and Astronomy, University of Victoria, Victoria, BC.

Department of Medical Physics, British Columbia Cancer Agency, Victoria, BC.

出版信息

Med Phys. 2008 Jul;35(7Part3):3410. doi: 10.1118/1.2965965.

DOI:10.1118/1.2965965
PMID:28512888
Abstract

The purpose of this study is to develop a method for determining the initial parameters of the pre-target electron beam within a Monte Carlo (MC) accelerator model able to produce accurate 18 MV 40×40 cm photon field profiles. We have developed a novel method by which the electron beam intensity distribution can be reverse engineered to reproduce measured dose distributions. The method begins from a cylindrically symmetric pre-target electron beam (radius 0.5 cm) of uniform intensity. This beam is subdivided into annular regions of fluence for which each region is individually transported through the accelerator head and into a water phantom. A simulated annealing search is then performed to determine the optimal combination of weights of the annular fluences that provide a best match between measured dose distributions and the weighted sum of annular dose distributions. Remarkably, the intensity distribution converges to a solution that is predominantly Gaussian, with a FWHM=1.1mm. In addition, the solution contains an important secondary "extra focal halo" on the order of 10% of the maximum Gaussian intensity. Agreement of the 40×40 cm photon field profiles with measurement was within 0.5%. The method greatly reduces the effort required to commission a MC accelerator model for clinical use and has achieved better agreement with measurement than other methods described in the literature. Our derived value of the electron beam FWHM agrees with that measured by Jaffray et al, 1993, and the "extra focal halo" is in qualitative agreement with their measurements of extra focal radiation.

摘要

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