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ORIGINAL ARTICLE
Year : 2012  |  Volume : 8  |  Issue : 3  |  Page : 367-372

Influence of smoothing algorithms in Monte Carlo dose calculations of cyberknife treatment plans: A lung phantom study


1 Department of Radiotherapy, Apollo Speciality Hospital, Chennai, India
2 Department of Medical Physics, Anna University, Chennai, India

Correspondence Address:
H Sudahar
Department of Radiotherapy, Apollo Speciality Hospital, 320, Padma Complex, Mount Road, Chennai Pin: 600035
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0973-1482.103514

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Aim: The Monte Carlo dose calculation algorithm yields accurate dose distributions in heterogeneous media and interfaces. The Monte Carlo calculation algorithm provided in the Multiplan Cyberknife treatment planning system (Accuray, Sunnyvale, CA, USA) has five different dose-smoothing algorithms in it. As the principle of smoothing of these algorithms is different, they can produce a disparity in the final dose distribution. The aim of the present study is to analyze the influence of these Monte Carlo smoothing algorithms in the final dose distribution of cyberknife treatment plans. Materials and Methods: An anthropomorphic lung phantom with a tumor mimicking ball target was taken for this study. The basic optimization was performed with the Ray tracing algorithm. The Monte Carlo calculations were introduced with each smoothing algorithm on the basic plan and the plans were compared. Results: The Monte Carlo doses were found to be lesser than the Ray tracing doses. The dose conformity index was above 4 for all the smoothing algorithms, while it was only 1.19 for Ray tracing. The least coverage of 6.34 was obtained for a weighted average algorithm. The deviation between the V100% values of different smoothing algorithms was higher than the deviation in V80%. Conclusion: The deviations between the smoothing algorithms are higher in the high-dose regions, including the prescribing isodose, than the low-dose regions of the target, as well as in the organs at risk (OAR).


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