  Treatment planning, which is a critical component of the radiotherapy
workflow, is typically carried out by a medical physicist in a time-consuming
trial-and-error manner. Previous studies have proposed knowledge-based or
deep-learning-based methods for predicting dose distribution maps to assist
medical physicists in improving the efficiency of treatment planning. However,
these dose prediction methods usually fail to effectively utilize distance
information between surrounding tissues and targets or organs-at-risk (OARs).
Moreover, they are poor at maintaining the distribution characteristics of ray
paths in the predicted dose distribution maps, resulting in a loss of valuable
information. In this paper, we propose a distance-aware diffusion model
(DoseDiff) for precise prediction of dose distribution. We define dose
prediction as a sequence of denoising steps, wherein the predicted dose
distribution map is generated with the conditions of the computed tomography
(CT) image and signed distance maps (SDMs). The SDMs are obtained by distance
transformation from the masks of targets or OARs, which provide the distance
from each pixel in the image to the outline of the targets or OARs. We further
propose a multi-encoder and multi-scale fusion network (MMFNet) that
incorporates multi-scale and transformer-based fusion modules to enhance
information fusion between the CT image and SDMs at the feature level. We
evaluate our model on two in-house datasets and a public dataset, respectively.
The results demonstrate that our DoseDiff method outperforms state-of-the-art
dose prediction methods in terms of both quantitative performance and visual
quality.
