A Practical Method to Optimize Quality Assurance Results of Arc Therapy Plans in Beam Modeling

Dosimetric accuracy of a volumetric modulated arc therapy (VMAT) plan is directly related to the beam model, particularly with multileaf collimator characterization. Inappropriate dosimetric leaf gap (DLG) value can lead to a suboptimal beam model, with significant failure in patient-specific quality assurance (PSQA) of VMAT plans. This study addressed the systematic issue of beam modeling and developed a practical method to determine the optimal DLG value for a beam model. Several complex VMAT plans were selected for the quality assurance analysis using the variable DLG values. The results of three-dimensional (3D) Gamma analysis as a function of the DLG at 3%/3 mm, 2%/2 mm, and 1%/1 mm criteria were fitted by a polynomial curve. The DLG value corresponding to the maximum Gamma passing rate for each polynomial fitting function was derived, and the average was calculated to be the optimal DLG value for each model. The 3D Gamma analysis was repeated with the optimal DLG value to verify the dosimetric accuracy of each VMAT case by PSQA. Gamma passing rates are seen to vary considerably with the DLG values and different analysis criteria (3%/3 mm, 2%/2 mm, and 1%/1 mm) for each case. The optimal DLG derived for each model was 1.16 mm and 1.10 mm, much larger than the measured value (about 0.3 mm). The beam models with the optimal DLG was able to produce an average Gamma passing rate of 97.1% (range, 94.6%– 99.1%) at 3%/3 mm and 93.5% (range, 89.0%– 96.5%) at 2%/2 mm for one beam model, and 97.1% (range, 94.8%– 99.1%) at 3%/3 mm, and 93.3% (range, 88.8%– 96.7%) at 2%/2 mm for another. The overall accuracy of dose calculation for VMAT plans should be optimized with a compromise of varied modulation complexities in a beam model. We have developed a practical method to derive the optimal DLG value for each beam model based on the Gamma passing criterion. This technique should be applicable in general for all beam energies and patient cases.