Program Information
Electron Monte Carlo: An Analysis of Cutout Factors for Irregularly Shaped Electron Fields
N Mensah-Brown*, N Neba , Y Altundal , L Kile, H Saleh , Univ of Kansas Cancer Center, Lee's Summit, MO
Presentations
SU-I-GPD-T-63 (Sunday, July 30, 2017) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose: To determine if cutout factors (CFs) obtained using the Electron Monte Carlo (EMC) calculation algorithm in Aria Version 11.0 can replace traditional measurements in a solid water phantom.
Methods: The EMC calculation algorithm was made available for the following energies: 6e-, 9e-, 12e-, and 20e-. A 30x30 cm² solid water phantom (12cm depth) was built and imaged using a Siemens Somatom CT scanner. Reference fields (10x10) for all energies were planned in Aria. The CF of 7 irregular shapes, which were also planned in Aria, could then be obtained at each energy. Actual measurements were obtained with a pinpoint chamber and/or a farmer chamber and compared with the results obtained in Aria. In addition, 17 rectangular cutouts were exported to RadCalc and the associated monitor units (MUs) using the square root method were compared to those using EMC in Aria.
Results: The average absolute standard deviation between measured cutout factors and those of Monte Carlo for all irregularly shaped cutouts and energies was 1.95±0.01%. The absolute discrepancy decreased with increasing energy, namely 2.58±0.02% (6e-), 2.13±0.01% (9e-), 1.97±0.01% (12e-), and 1.13±0.01% (20e-)for all cutouts. The discrepancies for the different shapes ranged from 1.12±0.01% for the oval cutout to 2.72±0.02% for the smallest hemispherical cutout (3.64cm x 3.41cm). The discrepancy in the MU calculation between Monte Carlo derived MUs and those obtained in RadCalc for rectangular cutouts was 1.76 ±1.3%.
Conclusion: We have shown that EMC in Aria can be a useful and accurate tool to determine CFs for a wide range of energies and electron cutouts. Care must be taken however when calculating MUs directly using EMC as these may be subject to statistical inaccuracies, which are not as pronounced when measuring cutout factors.
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