  The next great leap toward improving treatment of cancer with radiation will
require the combined use of online adaptive and magnetic resonance guided
radiation therapy techniques with automatic X-ray beam orientation selection.
Unfortunately, by uniting these advancements, we are met with a substantial
expansion in the required dose information and consequential increase to the
overall computational time imposed during radiation treatment planning, which
cannot be handled by existing techniques for accelerating Monte Carlo dose
calculation. We propose a deep convolutional neural network approach that
unlocks new levels of acceleration and accuracy with regards to post-processed
Monte Carlo dose results by relying on data-driven learned representations of
low-level beamlet dose distributions instead of more limited filter-based
denoising techniques that only utilize the information in a single dose input.
Our method uses parallel UNET branches acting on three input channels before
mixing latent understanding to produce noise-free dose predictions. Our model
achieves a normalized mean absolute error of only 0.106% compared with the
ground truth dose contrasting the 25.7% error of the under sampled MC dose fed
into the network at prediction time. Our model's per-beamlet prediction time is
~220ms, including Monte Carlo simulation and network prediction, with
substantial additional acceleration expected from batched processing and
combination with existing Monte Carlo acceleration techniques. Our method shows
promise toward enabling clinical practice of advanced treatment technologies.
