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...

翻译：深度学习已成功实现放疗计划中剂量分布预测的自动化，显著提升了规划效率与质量。然而，现有方法因其普遍采用的L1或L2损失函数及后验平均计算而存在过度平滑问题。为缓解这一局限，本文提出一种基于扩散模型的方法（DiffDose）用于预测癌症患者的放疗剂量分布。具体而言，DiffDose模型包含前向过程与反向过程。在前向过程中，模型通过逐步添加微小噪声将剂量分布图转化为纯高斯噪声，同时训练噪声预测器以估计每个时间步所添加的噪声。在反向过程中，模型借助训练完备的噪声预测器从纯高斯噪声中分步去除噪声，最终输出预测的剂量分布图...