An analysis of the changes of dose calculation algorithm in gamma knife

WONSEOP SEO1, H.J. Choi2, S.K. Choi2, Y.J. Lim2

1 2Gamma Knife Center, Department of Neurosurgery, Kyung Hee University Hospital

Keywords: physics, Dose Prescription, dose planning, technique, gamma knife

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Abstract

     In recent year, a Gamma plan 10.1, treatment planning software for Gamma Knife, has been provided by Elekta. It has three kinds of dose calculation algorithms in the program, equivalent TMR classic, TMR 10 and Convolution. 
     The purpose of this study is to analyze the difference between old and new algorithm and study the change of parameters affecting on a dose calculation of Gamma Knife.
     
     TMR 10 is an algorithm improved from TMR classic with new Monte Carlo calculation and other parameters for dose calculation. Convolution is an algorithm considering heterogeneous electron density in the brain. We made a CT calibration of our center with a special phantom for electron density and set the Gamma plan to use convolution dose calculation algorithm. On the CT image, 5 targets according to location were made. We made two treatments planning by using convolution and TMR 10, but equivalent TMR classic was calculated automatically. Then, we compared the maximum dose among Convolution, TMR 10 and equivalent TMR classic. The equivalent TMR classic represents the TMR classic which has been used in previous version of Gamma Plan.
     Compared to three algorithms, convolution showed the most large maximum dose and TMR classic followed. The TMR 10 showed 0-3% lower than the equivalent TMR classic. However, the equivalent TMR classic algorithms cannot be used for a while, because some error was found there.
     A limited data set was evaluated and compared. The clinical relevance is not known.
     The difference of maximum dose in three algorithms depended on the location. 
     As the prescription dose of gamma knife is usually determined from past experience of treatment, we should be careful in applying for the new algorithm without consideration of past treatment data.


Acknowledgements

Project Roles:

W. SEO (), H. Choi (), S. Choi (), Y. Lim ()