  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.
