Parameterization of photon beam dosimetry for a linear accelerator. Issue 2 (14th January 2016)

• Record Type: Journal Article
• Authors: Lebron, Sharon; Lu, Bo; Yan, Guanghua; Kahler, Darren; Li, Jonathan G.; Barraclough, Brendan; Liu, Chihray
• Is Part Of: Medical physics. Volume 43:Issue 2(2016)
• Page Start: 748
• Page End: 760
• Publication Date: 2016-01-14
• Subjects: dosimetry -- Gaussian processes -- linear accelerators -- quality assurance -- radiation therapy; Dose‐volume analysis -- Dosimetry/exposure assessment -- Therapeutic applications, including brachytherapy; Radiation therapy -- Linear accelerators -- Scintigraphy; Linac -- photon -- parameterization -- analytical model; Field size -- Computer modeling -- Dosimetry -- Linear accelerators -- Data analysis -- Photons -- Optimization -- Quality assurance -- C parity; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4939261
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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 modeled data, (3) the quality assurance process of a linear accelerator's (Linac) beam characteristics, (4) the establishment of a standard data set for comparison with other data, etcetera . Parameterization of the photon beam dosimetry creates a data set that is portable and easy to implement for different applications such as those previously mentioned. The aim of this study is to develop methods to parameterize photon beam dosimetric quantities, including percentage depth doses (PDDs), profiles, and total scatter output factors ( Scp ). Methods: Scp, PDDs, and profiles for different field sizes, depths, and energies were measured for a Linac using a cylindrical 3D water scanning system. All data were smoothed for the analysis and profile data were also centered, symmetrized, and geometrically scaled. The Scp data were analyzed using an exponential function. The inverse square factor was removed from the PDD data before modeling and the data were subsequently analyzed using exponential functions. For profile modeling, one halfside of the profile was divided into three regions described by exponential, sigmoid, and Gaussian equations. All of the analytical functions are field size, energy, depth, and, in the case of profiles, scan direction specific. The model's parameters were determined using the minimal amount of measured data necessary. The model's accuracy was evaluated via the calculation of absolute differences between the measured (processed) and calculated data in low gradient regions and distance‐to‐agreement analysis in high gradient regions. Finally, the results of dosimetric quantities obtained by the fitted models for a different machine were also assessed. Results: All of the differences in the PDDs' buildup and the profiles' penumbra regions were less than 2 and 0.5 mm, respectively. The differences in the low gradient regions were 0.20% ± 0.20% (<1% for all) and 0.50% ± 0.35% (<1% for all) for PDDs and profiles, respectively. For Scp data, all of the absolute differences were less than 0.5%. Conclusions: This novel analytical model with minimum measurement requirements was proved to accurately calculate PDDs, profiles, and Scp for different field sizes, depths, and energies.