Determination of a medical linac wedge factor dependency on the field size, depth and separation using Monte Carlo method to introduce an algorithm for treatment planning

The study was performed to investigate the dependency of the wedge factors (WF) of a linac internal wedge with the depth and field size for 6MV photon beams. In addition, the current study was performed in order to accurate determination of the WF to introduce an algorithm to be incorporated it in treatment planning to decrease the level of systematic error due to this factor in clinical applications. Materials An Elekta linac head structure with and without the internal wedge was precisely simulated based on the data provided by the manufacturer using MCNP 4C Monte Carlo code. The simulated linac was then benchmarked against the experimental measurement. Thereafter, a mini - phantom was simulated using the same Monte Carlo code. Then, the relevant linac wedge factors due to the internal wedge were determined for different symmetric field sizes of 4, 5, 10, 15, and 20 cm2 at various depths ranged from 0.5 to 34cm, while the mini phantom was present in the fields. The wedge factors were determined based on the values, which are measured at 100cm SAD and 10 cm depth for each field size. Linear trend lines were fitted on the set of depth dependent wedge factors for each of the field sizes. Then the trend lines’ constants were analyzed for investigating their field size dependency. Results indicated that the wedge factor is dependent on the depth for each field size. In addition, it was noted that the level of the wedge factor dependency on the depth varies for different field sizes. For example the wedge factor calculated at a depth of 10 cm had a variation of %7.4 when the field size varied from 4 to 20 cm2 symmetric square fields. ‎In this study, a more accurate algorithm was determined that it can be used instead of the ‎usual wedge factor look-up tables used for MU calculation in radiotherapy planning. The variation of the ‎photon beam due to these circumstances can be determined accurately using Monte Carlo method and the ‎proposed algorithm. This algorithm enables us to reduce the level of possible systematic errors ‎encountered in clinical practices