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Influence of Electron Transport Parameters On Monte Carlo Based Treatment Planning

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G Mora

G Mora1*, A Eldib2 , J Li2 , C Ma2 , (1) IBEB, Faculty of Sciences, University of Lisbon, Portugal (2) Fox Chase Cancer Center, Philadelphia, PA


SU-I-GPD-T-349 (Sunday, July 30, 2017) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: To investigate the effect of the global electron cutoff energy (ECUT) on the calculation accuracy of Monte Carlo based Treatment Planning. Systematic studies on heterogeneous phantoms were done to determine how ECUT affects the dose uncertainty and consequently the dose volume histogram (DVH) and dose mass histogram (DMH).

Methods: Four square phantoms were built using the MCSHOW code. Each phantom (10x10x10 cm³) contained one target of 8x8x8 cm³ subdivided into two components (T1 and T2) with equal dimensions of 8x8x4 cm³. T2 is made of the same tissue-like material with density of 1 g/cm³ in the four different phantoms. T1 is made of air-like material with different densities (0.001 g/cm³, 0.1 g/cm³, 0.2 g/cm³ and 0.3 g/cm³). Dose calculations (6MV-IMRT with two beams) were performed using the MCSIM code with the same number of histories and two different ECUT settings (521 keV and 700 keV).

Results: The uncertainty on the average dose calculated for T1 (0.001 g/cm³) was about 2% with ECUT=521 keV and 9% with ECUT=700 keV, respectively. Increasing the density of T1 to 0.3 g/cm³ decreased the uncertainty to about 1% in both 521 keV and 700 keV calculations. We observed differences up to 20% in the Total Target DVH between 521 keV and 700 keV while the DMH values differed by less than 2% between the two ECUT settings. The effect of ECUT on the uncertainty of dose, DVH and DMH calculated for T2 was not significant.

Conclusion: DVHs for targets including large (about 50%) low-density regions may be significantly affected by the ECUT value used with the Monte Carlo algorithm. In contrast, DMH was not affected by high ECUT in the treatment plan evaluation, and thus we can use high ECUT and DMH to improve the efficiency and accuracy of Monte Carlo based Treatment Planning.

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