High-Particle Simulation of Monte-Carlo Dose Distribution with 3D ConvLSTMs

Monte-Carlo simulation of radiotherapy dose remains an extremely time-consuming task, despite being still the most precise tool for radiation transport calculation. To circumvent this issue, deep learning offers promising avenues. In this paper, we extend ConvLSTM to handle 3D data and introduce a 3D recurrent and fully convolutional neural network architecture. Our model's purpose is to infer a computationally expensive Monte Carlo dose calculation result for VMAT plans with a high number of particles from a sequence of simulations with a low number of particles. We benchmark our framework against other learning methods commonly used for denoising and other medical tasks. Our model outperforms the other methods with regards to several evaluation metrics used to assess the clinical viability of the predictions.