  Cancer is a primary cause of morbidity and mortality worldwide. The
radiotherapy plays a more and more important role in cancer treatment. In the
radiotherapy, the dose distribution maps in patient need to be calculated and
evaluated for the purpose of killing tumor and protecting healthy tissue. Monte
Carlo (MC) radiation transport calculation is able to account for all aspects
of radiological physics within 3D heterogeneous media such as the human body
and generate the dose distribution maps accurately. However, an MC calculation
for doses in radiotherapy usually takes a great mass of time to achieve
acceptable statistical uncertainty, impeding the MC methods from wider clinic
applications. Here we introduce a convolutional neural network (CNN), termed as
Monte Carlo Denoising Net (MCDNet), to achieve the acceleration of the MC dose
calculations in radiotherapy, which is trained to directly predict the
high-photon (noise-free) dose maps from the low-photon (noise-much) dose maps.
Thirty patients with postoperative rectal cancer who accepted
intensity-modulated radiation therapy (IMRT) were enrolled in this study. 3D
Gamma Index Passing Rate (GIPR) is used to evaluate the performance of
predicted dose maps. The experimental results demonstrate that the MCDNet can
improve the GIPR of dose maps of 1x107 photons over that of 1x108 photons,
yielding over 10x speed-up in terms of photon numbers used in the MC
simulations of IMRT. It is of great potential to investigate the performance of
this method on the other tumor sites and treatment modalities.
