Optimization of a commercial portal dose image prediction algorithm for pre-treatment verifications of plans using unflattened photon beams.

Abstract

The aim was to improve the portal dosimetry-based quality assurance results of conventional treatment plans by adjusting the multileaf collimator (MLC) dosimetric leaf gap (DLG) and transmission (T) values of the anisotropic analytic algorithm (AAA) used for portal dose image prediction (PDIP). The AAA-based PDIP v. 16.1 algorithm (PDIP-AAA) of the Eclipse TPS was configured for 6 MV FFF energy. Optimal DLG and T values were achieved for this algorithm by comparing predicted versus measured portal images of the Chair pattern. Twenty clinical plans using 6 MV FFF beams were verified using the optimal PDIP-AAA algorithm and the standard PDIP v. 16 algorithm (PDIP-vE), configured using the van Esch package. The 3% global/2 mm gamma passing rates (GPRs) and average gamma indexes (AGIs) were computed for each acquired image. For each plan, the mean GPR (GPRmean) and mean GAI (GAImean) were compared for both algorithms. A 2-tailed Student t-test (α = 0.05) was used to evaluate whether there was a statistically significant difference. Optimal values of DLG = 0.1 mm and T = 0.01 were found for the PDIP-AAA algorithm, providing significantly better values of GPRmean and AGImean than PDIP-vE (p < 0.001). All plans verified with PIDP-AAA showed GPRmean ≥ 95%. In contrast, only 45% of the plans reported GPRmean ≥ 95% with the PDIP-vE algorithm. The MLC parameters available in the PDIP-AAA model must be tuned to improve the accuracy of the predicted dose image. This work-around is not possible using the standard PDIP algorithm. The adjusted PDIP-AAA resulted in significantly better results than PDIP-vE.