A preliminary study of a photon dose calculation algorithm using a convolutional neural network.

The aim of dose calculation algorithm research is to improve the calculation accuracy while maximizing the calculation efficiency. In this study, the three-dimensional distribution of total energy release per unit mass (TERMA) and the electron density (ED) distribution are considered inputs in a method for calculating the three-dimensional dose distribution based on a convolutional neural network (CNN). Attempts are made to improve the efficiency of the collapsed cone convolution/superposition (CCCS) algorithm while providing an approach to improve the efficiency of other traditional dose calculation algorithms. Twelve sets of computed tomography (CT) images were employed for training. Data sets were generated by the CCCS algorithm with a random beam configuration. For each monoenergetic photon model, 7500 samples were generated for the training set, and 1500 samples were generated for the validation set. Training occurred for 0.5 MeV, 1 MeV, 2 MeV, 3 MeV, 4 MeV, 5 MeV, and 6 MeV monoenergetic photon models. To evaluate the usability under linac conditions, a comparison between CCCS and CNN-Dose was performed for the Mohan 6-MV spectrum for 12 additional new sets of CT images with different anatomies. A total of 1512 test samples were generated. For all anatomies, the mean value, 95% lower confidence limit (LCL) and 95% upper confidence limit (UCL) were 99.56%, 99.51% and 99.61%, respectively, at the 3%/2 mm criteria. The mean value, 95% LCL and 95% UCL were 98.57%, 98.46% and 98.67%, respectively, at the 2%/2 mm criteria. The results meet the relevant clinical requirements. In the proposed methods, the dose distribution of clinical energy can be obtained by TERMA, and the electronic density can be obtained with a CNN. This method can also be used for other traditional dose algorithms and displays potential in treatment planning, adaptive radiation therapy, andin vivoverification.