Photon scatter in portal images: accuracy of a fluence based pencil beam superposition algorithm

The accuracy of a pencil beam algorithm to predict scattered photon fluence into portal imaging systems was studied. A data base of pencil beam kernels describing scattered photon fluence behind homogeneous water slabs (1-50 cm thick) at various air gap distances (0-100 cm) was generated using the EGS Monte Carlo code. Scatter kernels were partitioned according to particle history: singly-scattered, multiply-scattered, and bremsstrahlung and positron annihilation photons. Mean energy and mean angle with respect to the incident photon pencil beam were also scored. This data allows fluence, mean energy, and mean angular data for each history type to be predicted using the pencil beam algorithm. Pencil beam algorithm predictions for 6 and 24 MV incident photon beams were compared against full Monte Carlo simulations for several inhomogeneous phantoms, including approximations to a lateral neck, and a mediastinum treatment. The accuracy of predicted scattered photon fluence, mean energy, and mean angle was investigated as a function of air gap, field size, photon history, incident beam resolution, and phantom geometry. Maximum errors in mean energies were 0.65 and 0.25 MeV for the higher and lower energy spectra, respectively, and 15 degrees for mean angles. The ability of the pencil beam algorithm to predict scatter fluence decreases with decreasing air gap, with the largest error for each phantom occurring at the exit surface. The maximum predictive error was found to be 6.9% with respect to the total fluence on the central axis. By maintaining even a small air gap (approximately 10 cm), the error in predicted scatter fluence may be kept under 3% for the phantoms and beam energies studied here. It is concluded that this pencil beam algorithm is sufficiently accurate (using International Commission on Radiation Units and Measurements Report No. 24 guidelines for absorbed dose) over the majority of clinically relevant air gaps, for further investigation in a portal dose prediction algorithm.