Abstract
Purpose: Advanced treatment delivery techniques in radiosurgery require improved treatment planning system (TPS). The aim of this study was to compare the traditional tissue maximum ratio calculation method (TMR) with recently developed by Elekta collapsed cone convolution (CCC) algorithm for dose calculation in Gamma Plan TPS. Methods: The possibility of this study was a consequence of the untraditional approach to the Gamma Knife treatments at Cleveland Clinic using both MR and CT studies for the absolute majority of the treatments. The basis for the plan data were approximately 1000 clinical cases treated on Perfexion Gamma Knife using TMR classic and complete MR/CT studies. Dose characteristics like D95 (dose received by 95% of target), D50 (dose received by 50% of target), minimum dose, maximum dose, average dose, integral dose, average target density, shortest distance to skull surface, and average axial skull radii were calculated for both algorithms and analyzed. The shot time and configuration were fixated according to the original plan made with TMR Classic. Convolution dose calculations were enabled by retrospectively assigning HU/ED conversion table to the available CT study. The comparison was done for different clinical indications. Results: As expected by the inherent approximation of all tissue to water, the TMR algorithm systematically overestimates the actual physical dose delivered compared with CCC method. It was shown that D95 was basically independent, when error bars considered, from the type of the lesion. The CCC/TMR ratios were in the range of 0.92/0.94. It was shown that there were statistically significant correlations for the most of the analyzed dose characteristics. Conclusion: This work indicates the potential for adjusting dose prescriptions with a mathematical factor which could imply a simple transition to use of more advanced and accurate CCC algorithm for calculation of the dose distributions for the Gamma Knife radiosurgery treatments. Elekta Instrument AB have provided means to calculate the data and have been involved in the data analysis.
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