Small field detector correction factors kQclin, Qmsrfclin, fmsr for silicon‐diode and diamond detectors with circular 6 MV fields derived using both empirical and numerical methods. Issue 1 (30th December 2015) - O'Brien, D. J.; León‐Vintró, L.; McClean, B.
Abstract : Purpose: The use of radiotherapy fields smaller than 3 cm in diameter has resulted in the need for accurate detector correction factors for small field dosimetry. However, published factors do not always agree and errors introduced by biased reference detectors, inaccurate Monte Carlo models, or experimental errors can be difficult to distinguish. The aim of this study was to provide a robust set of detector‐correction factors for a range of detectors using numerical, empirical, and semiempirical techniques under the same conditions and to examine the consistency of these factors between techniques. Methods: Empirical detector correction factors were derived based on small field output factor measurements for circular field sizes from 3.1 to 0.3 cm in diameter performed with a 6 MV beam. A PTW 60019 microDiamond detector was used as the reference dosimeter. Numerical detector correction factors for the same fields were derived based on calculations from a geant4 Monte Carlo model of the detectors and the Linac treatment head. Semiempirical detector correction factors were derived from the empirical output factors and the numerical dose‐to‐water calculations. Results: The PTW 60019 microDiamond was found to over‐respond at small field sizes resulting in a bias in the empirical detector correction factors. The over‐response was similar in magnitude to that of the unshielded diode. Good agreement was generally found between semiempirical and numerical detector correction factors except for the PTW 60016 Diode P, where the numerical values showed a greater over‐response than the semiempirical values by a factor of 3.7% for a 1.1 cm diameter field and higher for smaller fields. Conclusions: Detector correction factors based solely on empirical measurement or numerical calculation are subject to potential bias. A semiempirical approach, combining both empirical and numerical data, provided the most reliable results.