SU-E-T-220: Computational Accuracy of Adaptive Convolution (AC) and Collapsed Cone Convolution (CCC) Algorithms in the Presence of Air Gaps




To compare the percentage depth dose (PDD) computational accuracy of Adaptive Convolution (AC) and Collapsed Cone Convolution (CCC) algorithms in the presence of air gaps.


A 30×30×30 cm3 solid water phantom with two 5cm air gaps was scanned with a CT simulator unit and exported into the Phillips Pinnacle™ treatment planning system. PDDs were computed using the AC and CCC algorithms. Photon energy of 6 MV was used with field sizes of 3×3 cm2, 5×5 cm2, 10×10 cm2, 15×15 cm2, and 20×20 cm2. Ionization chamber readings were taken at different depths in water for all the field sizes. The percentage differences in the PDDs were computed with normalization to the depth of maximum dose (dmax). The calculated PDDs were then compared with measured PDDs.


In the first buildup region, both algorithms overpredicted the dose for all field sizes and under-predicted for all other subsequent buildup regions. After dmax in the three water media, AC under-predicted the dose for field sizes 3×3 and 5×5 cm2 and overpredicted for larger field sizes, whereas CCC under-predicted for all field sizes. Upon traversing the first air gap, AC showed maximum differences of –3.9%, −1.4%, 2.0%, 2.5%, 2.9% and CCC had maximum differences of −3.9%, −3.0%,–3.1%, −2.7%, −1.8% for field sizes 3×3, 5×5, 10×10, 15×15, and 20×20 cm2 respectively.


The effect of air gaps causes a significant difference in the PDDs computed by both the AC and CCC algorithms in secondary build-up regions. AC computed larger values for the PDDs except at smaller field sizes. For CCC, the size of the errors in prediction of the PDDs has an inverse relationship with respect to field size. These effects should be considered in treatment planning where significant air gaps are encountered.