  Deep learning (DL) has successfully automated dose distribution prediction in
radiotherapy planning, enhancing both efficiency and quality. However, existing
methods suffer from the over-smoothing problem for their commonly used L1 or L2
loss with posterior average calculations. To alleviate this limitation, we
propose a diffusion model-based method (DiffDose) for predicting the
radiotherapy dose distribution of cancer patients. Specifically, the DiffDose
model contains a forward process and a reverse process. In the forward process,
DiffDose transforms dose distribution maps into pure Gaussian noise by
gradually adding small noise and a noise predictor is simultaneously trained to
estimate the noise added at each timestep. In the reverse process, it removes
the noise from the pure Gaussian noise in multiple steps with the well-trained
noise predictor and finally outputs the predicted dose distribution maps...
