Radiation therapy is crucial in cancer treatment. Experienced experts typically iteratively generate high-quality dose distribution maps, forming the basis for excellent radiation therapy plans. Therefore, automated prediction of dose distribution maps is significant in expediting the treatment process and providing a better starting point for developing radiation therapy plans. With the remarkable results of diffusion models in predicting high-frequency regions of dose distribution maps, dose prediction methods based on diffusion models have been extensively studied. However, existing methods mainly utilize CNNs or Transformers as denoising networks. CNNs lack the capture of global receptive fields, resulting in suboptimal prediction performance. Transformers excel in global modeling but face quadratic complexity with image size, resulting in significant computational overhead. To tackle these challenges, we introduce a novel diffusion model, MD-Dose, based on the Mamba architecture for predicting radiation therapy dose distribution in thoracic cancer patients. In the forward process, MD-Dose adds Gaussian noise to dose distribution maps to obtain pure noise images. In the backward process, MD-Dose utilizes a noise predictor based on the Mamba to predict the noise, ultimately outputting the dose distribution maps. Furthermore, We develop a Mamba encoder to extract structural information and integrate it into the noise predictor for localizing dose regions in the planning target volume (PTV) and organs at risk (OARs). Through extensive experiments on a dataset of 300 thoracic tumor patients, we showcase the superiority of MD-Dose in various metrics and time consumption.

翻译：放射治疗在癌症治疗中至关重要。经验丰富的专家通常通过迭代生成高质量剂量分布图，这构成了制定优秀放疗计划的基础。因此，自动化预测剂量分布图对于加快治疗流程并为放疗计划开发提供更优起点具有重要意义。鉴于扩散模型在预测剂量分布图高频区域方面取得的显著成果，基于扩散模型的剂量预测方法已被广泛研究。然而，现有方法主要使用CNN或Transformer作为去噪网络。CNN缺乏对全局感受野的捕获能力，导致预测性能欠佳。Transformer在全局建模方面表现出色，但其复杂度随图像尺寸呈二次增长，导致计算开销显著。为解决这些挑战，我们提出了一种基于Mamba架构的新型扩散模型MD-Dose，用于预测胸部癌症患者的放疗剂量分布。在前向过程中，MD-Dose向剂量分布图添加高斯噪声以获得纯噪声图像。在反向过程中，MD-Dose利用基于Mamba的噪声预测器预测噪声，最终输出剂量分布图。此外，我们开发了一个Mamba编码器来提取结构信息，并将其集成到噪声预测器中，用于定位计划靶区（PTV）和危及器官（OAR）的剂量区域。通过在300名胸部肿瘤患者数据集上进行的大量实验，我们展示了MD-Dose在各项指标和时间消耗方面的优越性。