A fast GPU-based Monte Carlo simulation of proton transport with detailed modeling of nonelastic interactions.
2015
Purpose:
Very fast Monte
Carlo
(MC)
simulations of proton transport have been implemented recently on graphics
processing units (GPUs). However, these MCs usually use simplified models for
nonelastic proton–nucleus interactions. Our primary goal is to build a GPU-based
proton
transport MC with detailed modeling of elastic and nonelastic
proton–nucleus collisions.
Methods:
Using the cuda framework, the authors implemented GPU kernels for the
following tasks: (1) simulation of beam spots from our possible scanning nozzle
configurations, (2) proton propagation through CT geometry, taking into account
nuclear elastic
scattering,
multiple
scattering, and energy loss straggling, (3) modeling of the
intranuclear cascade stage of nonelastic interactions when they
occur, (4) simulation of nuclear evaporation, and (5) statistical error estimates
on the dose. To validate our MC, the authors
performed (1) secondary particle yield calculations in proton
collisions with therapeutically relevant nuclei, (2)
dose
calculations in homogeneous phantoms, (3) recalculations of complex head and neck
treatment plans from a commercially available treatment planning system, and
compared with geant 4.9.6p2/TOPAS.
Results:
Yields, energy, and angular distributions of secondaries from nonelastic
collisions on various nuclei are in good agreement with the
geant 4.9.6p2 Bertini and Binary cascade models. The 3D-gamma pass
rate at 2%-2 mm for treatment plan simulations is typically 98%. The net
computational time on a NVIDIA GTX680 card, including all CPU–GPU data transfers,
is ∼20 s for 1 × 107
proton
histories.
Conclusions:
Our GPU-based MC is the first of its kind to include a detailed nuclear
model to handle nonelastic interactions of protons with any nucleus. Dosimetric
calculations are in very good agreement with geant 4.9.6p2/TOPAS. Our
MC is
being integrated into a framework to perform fast routine clinical QA of
pencil-beam based treatment plans, and is being used as the dose calculation
engine in a clinically applicable MC-based IMPT treatment planning system. The
detailed nuclear modeling will allow us to perform very fast linear energy
transfer and neutron
dose
estimates on the GPU.
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