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（DL）已成功实现放射治疗计划中剂量分布的自动化预测，提升了效率与质量。然而，现有方法因常采用后验平均计算的L1或L2损失，存在过度平滑问题。为缓解这一局限，我们提出一种基于扩散模型的方法（DiffDose）用于预测癌症患者的放射治疗剂量分布。具体而言，DiffDose模型包含前向过程与反向过程。在前向过程中，DiffDose通过逐步添加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一步添加的噪声。在反向过程中，利用训练后的噪声预测器通过多步去除纯高斯噪声中的噪声，最终输出预测的剂量分布图。