AbstractID: 9012 Title: Convolution/Superposition using random sampling of photons,
kernels, and field segments
We present a stochastic (Monte Carlo) approach to point kernel convolution/superposition for
radiotherapy dose calculations. Instead of deterministically convolving point kernels with TERMA,
the photon energies, directions and interaction points are randomly sampled. Subsequently,
monoenergetic kernel rays are sampled and propagated across the calculation phantom. Explicit
sampling of the photon energy obviates the need for spectral hardening and kernel hardening
corrections that were necessary in polyenergetic convolution. Furthermore, continuous spatial
sampling allows us to easily model sharp changes in fluence, such as those due to collimator
tongue-and-groove. Also, because the photon directional information is preserved, obliquely
directed photons, such as head scatter, are modeled more accurately.
We extend the Monte Carlo sampling method to calculate IMRT dose distributions more
efficiently. By sampling beam angles and field segments according to their fluence weights, a full
aperture-specific calculation, with effects of tongue-and-groove, leaf curvature, and head scatter,
can be calculated using a single CPU in 30 minutes with 1% statistical fluctuations, for a CT
phantom divided into 4 mm voxels. The computational efficiency is independent of the complexity
of the MLC leaf sequence, unlike that for deterministic calculations, in which the time taken for a
collimator-based calculation is proportional to the number of field segments. Thus, the Monte
Carlo superposition method serves as a simpler, more accurate, and in very complex cases like
IMRT, a faster technique than existing polyenergetic deterministic superposition methods.