Abstract
Purpose: This study explores novel methods to address two significant challenges affecting measurement of patient-specific quality assurance (QA) with IBA’s Matrixx Evolution™ ionization chamber array. First, dose calculation algorithms often struggle to accurately determine dose to the chamber array due to CT artifact and algorithm limitations. Second, finite chamber size and volume averaging effects cause additional deviation from the calculated dose. Methods: QA measurements were taken with the Matrixx positioned on the treatment table in a solid-water Multi-Cube™ phantom. To reduce the effect of CT artifact, the Matrixx CT image set was masked with appropriate materials and densities. Individual ionization chambers were masked as air, while the high-z electronic backplane and remaining solid-water material were masked as aluminum and water, respectively. Dose calculation was done using Varian’s Acuros XB™ (V11) algorithm, which is capable of predicting dose more accurately in non-biologic materials due to its consideration of each material’s atomic properties. Finally, the exported TPS dose was processed using an in-house algorithm (MATLAB) to assign the volume averaged TPS dose to each element of a corresponding 2-D matrix. This matrix was used for comparison with the measured dose. Square fields at regularly-spaced gantry angles, as well as selected patient plansmore » were analyzed. Results: Analyzed plans showed improved agreement, with the average gamma passing rate increasing from 94 to 98%. Correction factors necessary for chamber angular dependence were reduced by 67% compared to factors measured previously, indicating that previously measured factors corrected for dose calculation errors in addition to true chamber angular dependence. Conclusion: By comparing volume averaged dose, calculated with a capable dose engine, on a phantom masked with correct materials and densities, QA results obtained with the Matrixx Evolution™ can be significantly improved. In addition, necessary correction factors are reduced, allowing for more reliable and meaningful patient-specific QA measurements.« less
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