  Monte Carlo (MC) simulation is considered the gold standard method for
radiotherapy dose calculation. However, achieving high precision requires a
large number of simulation histories, which is time consuming. The use of
computer graphics processing units (GPUs) has greatly accelerated MC simulation
and allows dose calculation within a few minutes for a typical radiotherapy
treatment plan. However, some clinical applications demand real time efficiency
for MC dose calculation. To tackle this problem, we have developed a real time,
deep learning based dose denoiser that can be plugged into a current GPU based
MC dose engine to enable real time MC dose calculation. We used two different
acceleration strategies to achieve this goal: 1) we applied voxel unshuffle and
voxel shuffle operators to decrease the input and output sizes without any
information loss, and 2) we decoupled the 3D volumetric convolution into a 2D
axial convolution and a 1D slice convolution. In addition, we used a weakly
supervised learning framework to train the network, which greatly reduces the
size of the required training dataset and thus enables fast fine tuning based
adaptation of the trained model to different radiation beams. Experimental
results show that the proposed denoiser can run in as little as 39 ms, which is
around 11.6 times faster than the baseline model. As a result, the whole MC
dose calculation pipeline can be finished within 0.15 seconds, including both
GPU MC dose calculation and deep learning based denoising, achieving the real
time efficiency needed for some radiotherapy applications, such as online
adaptive radiotherapy.
