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Predicting voxel-level dose distributions for esophageal radiotherapy using densely connected network with dilated convolutions

This work aims to develop a voxel-level dose prediction framework by integrating distance information between PTV and OARs, as well as image information, into a densely-connected network (DCNN). Firstly, a four-channel feature map, consisting of a PTV image, an OAR image, a CT image, and a distance image, is constructed. A densely connected neural network is then built and trained for voxel-level dose prediction. Considering that the shape and size of OARs are highly inconsistent, a dilated convolution is employed to capture features from multiple scales. Finally, the proposed network is evaluated with five-fold cross-validation, based on ninety-eight clinically approved treatment plans. The voxel-level mean absolute error(MAEV) of DCNN was 2.1% for PTV, 4.6% for left lung, 4.0% for right lung, 5.1% for heart, 6.0% for spinal cord, and 3.4% for body, which outperforms conventional U-Net, Resnet-antiResnet, U-Resnet-D by 0.1-0.8%. This result shows that with the introduction of a distance image and DCNN model, the accuracy of predicted dose distribution could be significantly improved. This approach offers a new dose prediction tool to support quality assurance and the automation of treatment planning in esophageal radiotherapy.