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The Effect of Multi Leaf Collimator Characteristics in Treatment Planning Systems Calculation Errors as Detected in Phantom and in Patient QA

M Bakhtiari*, J Schmitt, A Sedaghat, V Aroumougame, M Sarfaraz, J Rodgers, RadAmerica, LLC--MedStar Health, Baltimore, MD 21237

SU-E-T-182 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: We performed patient specific VMAT quality assurance (QA) with a 3D array dosimetry device and a software system that estimates 3D dose distribution in patient anatomy from 3D measurements in phantom. The correlation between the dose errors from phantom measurements and the corresponding volumetric (3D) estimated dose in patients were studied. We used the gamma pass-rates to determine if either method depends on MLC characteristics.

Methods: The plan and dose were calculated by the Eclipse TPS (VARIAN) and the VMAT treatments were delivered to an ArcCHECK diode phantom (Sun Nuclear). The gamma pass-rates were calculated using the data measured using the ArcCHECK and the verification plan calculated in Eclipse. The 3DVH software (Sun Nuclear) was used to estimate the 3D dose in patient using Plan Dose Perturbation algorithm, based on the ArcCHECK measurements. In-house software was developed to calculate the percentage of the leaf pairs with a gap less than 1 cm for each treatment along with the percentage of the beams areas that was blocked by MLCs for the entire treatment. For each patient, any dependency on pass-rates and the MLC characteristics was investigated.

Results: There was no correlation between the phantom and the 3D patient QA gamma pass-rates. The Phantom gamma pass-rates did not correlate with the percentage of the leaf pairs with 1 cm gaps or smaller. With 3D QA in patient anatomy, it was found that when the percentage of the leaf pairs with smaller gaps increases, the gamma pass-rate drops. There was no correlation between phantom gamma-pass rate and the field area blocked by MLCs. Increasing the MLC-blocked area corresponds to decrease in 3D gamma pass-rate in patient QA.

Conclusion: It was found that 3D QA in patient anatomy more efficiently reflects the errors caused by MLC characterization in the treatment planning software.

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