  Currently, deep learning (DL) has achieved the automatic prediction of dose
distribution in radiotherapy planning, enhancing its efficiency and quality.
However, existing methods suffer from the over-smoothing problem for their
commonly used L_1 or L_2 loss with posterior average calculations. To alleviate
this limitation, we innovatively introduce a diffusion-based dose prediction
(DiffDP) model for predicting the radiotherapy dose distribution of cancer
patients. Specifically, the DiffDP model contains a forward process and a
reverse process. In the forward process, DiffDP gradually transforms dose
distribution maps into Gaussian noise by adding small noise and trains a noise
predictor to predict the noise added in each timestep. In the reverse process,
it removes the noise from the original Gaussian noise in multiple steps with
the well-trained noise predictor and finally outputs the predicted dose
distribution map. To ensure the accuracy of the prediction, we further design a
structure encoder to extract anatomical information from patient anatomy images
and enable the noise predictor to be aware of the dose constraints within
several essential organs, i.e., the planning target volume and organs at risk.
Extensive experiments on an in-house dataset with 130 rectum cancer patients
demonstrate the s
