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SU‐E‐T‐597: Parameterization of the Photon Beam Dosimetry for a Commercial Linear Accelerator
Author(s) -
Lebron S,
Lu B,
Yan G,
Kahler D,
Li J,
Barraclough B,
Liu C
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4924960
Subject(s) - dosimetry , linear particle accelerator , photon , physics , beam (structure) , quality assurance , data set , wedge (geometry) , computational physics , gaussian , exponential function , optics , mathematics , nuclear medicine , mathematical analysis , statistics , medicine , economy , service (business) , quantum mechanics , economics
Purpose: In radiation therapy, accurate data acquisition of photon beam dosimetric quantities is important for (1) beam modeling data input into a treatment planning system (TPS), (2) comparing measured and TPS modelled data, (3) a linear accelerator's (linac) beam characteristics quality assurance process, and (4) establishing a standard data set for data comparison, etcetera. Parameterization of the photon beam dosimetry creates a portable data set that is easy to implement for different applications such as those previously mentioned. The aim of this study is to develop methods to parameterize photon percentage depth doses(PDD), profiles, and total scatter output factors(Scp). Methods: Scp, PDDs and profiles for different field sizes (from 2×2 to 40×40cm 2 ), depths and energies were measured in a linac using a three‐dimensional water tank. All data were smoothed and profile data were also centered, symmetrized and geometrically scaled. The Scp and PDD data were analyzed using exponential functions. For modelling of open and wedge field profiles, each side was divided into three regions described by exponential, sigmoid and Gaussian equations. The model's equations were chosen based on the physical principles described by these dosimetric quantities. The equations’ parameters were determined using a least square optimization method with the minimal amount of measured data necessary. The model's accuracy was then evaluated via the calculation of absolute differences and distance–to–agreement analysis in low gradient and high gradient regions, respectively. Results: All differences in the PDDs’ buildup and the profiles’ penumbra regions were less than 2 mm and 0.5 mm, respectively. Differences in the low gradient regions were 0.20 ± 0.20% and 0.50 ± 0.35% for PDDs and profiles, respectively. For Scp data, all differences were less than 0.5%. Conclusion: This novel analytical model with minimum measurement requirements proved to accurately calculate PDDs, profiles, and Scp for different field sizes, depths and energies.