High density dental materials and radiotherapy planning: Comparison of the dose predictions using superposition algorithm and fluence map Monte Carlo method with radiochromic film measurements

被引:35
作者
Spirydovich, Siarhei
Papiez, Lech
Langer, Mark
Sandison, George
Thai, Van
机构
[1] Indiana Univ, Sch Med, Dept Radiat Oncol, Indianapolis, IN 46204 USA
[2] Purdue Univ, Sch Hlth Sci, W Lafayette, IN 47907 USA
基金
美国国家科学基金会;
关键词
fluence map; convolution; superposition; Monte Carlo; dental; head and neck;
D O I
10.1016/j.radonc.2006.10.010
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background and purpose: During radiotherapy planning high density dental materials create a major challenge in determining correct dose distribution inside patients with head-and-neck tumors. Patients and methods: In this work we investigated the absorbed dose distribution inside a solid water((R)) stab phantom with embedded high density material irradiated by a 6 MV photon beam of field size 10 x 10 cm. We evaluated the absorbed dose distribution with three different techniques: superposition algorithm, radiochromic film, and the fluence map Monte Carlo (FMMC) method. Results: The results obtained with radiochromic film and FMMC were in good agreement (within +/- 5% of the dose) with one another. The superposition algorithm, which is often considered superior to other commercially available dose calculation algorithms, produced appreciably less accurate results than FMMC. In particular, downstream from the high density cerrobend inhomogeneity the superposition algorithm predicts a higher dose than the measurement does by at least 10-16% depending upon the size of the inhomogeneity and the distance from it. Upstream of the high density inhomogeneities the superposition algorithm predicts a lower than measured dose due to its failure to predict the dose enhancement close to the inhomogeneity interface. Conclusions: The delivered dose downstream from a high density inhomogeneity would be significantly less than the prescribed dose calculated by the superposition algorithm. The FMMC method which is based on a hybrid of the superposition algorithm input fluence data and Monte Carlo can be a useful tool in predicting dose in the presence of high density (e.g. dental) materials. (c) 2006 Elsevier Ireland Ltd. All rights reserved.
引用
收藏
页码:309 / 314
页数:6
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