Spline modelling electron insert factors using routine measurements

Biggs, Simon; Sobolewski, Matthew; Murry, R.; Kenny, John

doi:10.1016/j.ejmp.2015.11.002

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…The spline model requires a minimum of eight empirical measurements, and the expected uncertainty of the technique to predict insert factors for a 12 MeV beam and 10 cm cone with this number of measurements was estimated as 0.5%. 17 The results from the current study demonstrated a slightly larger overall uncertainty of 0.6%. This uncertainty may be more representative of the prediction accuracy of this technique over the wide range of energy, cone, and SSD combinations possible in electron treatments.…”

Section: Discussionmentioning

confidence: 53%

“…16 One such analytical technique presented by Biggs et al allows for the calculation of the insert factor for any irregular shape using a bivariate spline model fit to empirically measured output factors. 17 This technique relies on parameterizing insert shapes into equivalent ellipses, characterized by the width and perimeter-toarea ratio of the insert. Parameterizing insert shapes into ellipses was found to be correlated with the primary factors that affect change in electron beam output by the insert, including changes in lateral scatter, bremsstrahlung produced in the insert material, and scatter from the edge of the insert.…”

Section: Introductionmentioning

confidence: 99%

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Validation of spline modeling for calculation of electron insert factors for varian linear accelerators

Baltz

Kirsner

2021

J Applied Clin Med Phys

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There are several methods available in the literature for predicting the insert factor for clinical electron beams. The purpose of this work was to build on a previously published technique that uses a bivariate spline model generated from elliptically parameterized empirical measurements. The technique has been previously validated for Elekta linear accelerators for limited clinical electron setups. The same model is applied to Varian machines to test its efficacy for use with these linear accelerators. Insert factors for specifically designed elliptical cutouts were measured to create spline models for 6, 9, 12, 16, and 20 MeV electron energies for four different cone sizes at source-to-surface distances (SSD) of 100, 105, and 110 cm. Insert factor validation measurements of patient cutouts and clinical standard cutouts were acquired to compare to model predictions. Agreement between predicted insert factors and validation measurements averaged 0.8% over all energies, cones, and clinical SSDs, with an uncertainty of 0.6% (1SD), and maximum deviation of 2.1%. The model demonstrated accurate predictions of insert factors using the minimum required amount of input data for small cones, with more input measurements required for larger cones. The results of this study provide expanded validation of this technique to predict insert factors for all energies, cones, and SSDs that would be used in most clinical situations. This level of accuracy and the ease of creating the model necessary for the insert factor predictions demonstrate its acceptability to use clinically for Varian machines.

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Section: Discussionmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Validation of spline modeling for calculation of electron insert factors for varian linear accelerators

Baltz

Kirsner

2021

J Applied Clin Med Phys

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“…Splines are an intensively developing field of applied mathematics and econometrics (Wahba, 1990;Pagan & Ullah, 1999;Ruppert, Wand, & Carrol, 2003;Hardle, Muller, Sperlich, & Werwatz, 2004;Keele, 2008). The most widely practical application of splines is found in different branches of physics and related fields (Biggs, Sobolewski, Murry, & Kenny, 2016;Xiao, Liu, Li, Wang, & Zhao, 2016;Shen, Li, Wu, & Zhu, 2017;Pérez-Arribas & Pérez-Fernández, 2018).…”

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confidence: 99%