SP-DiffDose: A Conditional Diffusion Model for Radiation Dose Prediction
  Based on Multi-Scale Fusion of Anatomical Structures, Guided by
  SwinTransformer and Projector
Linjie Fu Xia Li Xiuding Cai Yingkai Wang Xueyao Wang Yu Yao Yali Shen
Radiation therapy serves as an effective and standard method for cancer
treatment. Excellent radiation therapy plans always rely on high-quality dose
distribution maps obtained through repeated trial and error by experienced
experts. However, due to individual differences and complex clinical
situations, even seasoned expert teams may need help to achieve the best
treatment plan every time quickly. Many automatic dose distribution prediction
methods have been proposed recently to accelerate the radiation therapy
planning process and have achieved good results. However, these results suffer
from over-smoothing issues, with the obtained dose distribution maps needing
more high-frequency details, limiting their clinical application. To address
these limitations, we propose a dose prediction diffusion model based on
SwinTransformer and a projector, SP-DiffDose. To capture the direct correlation
between anatomical structure and dose distribution maps, SP-DiffDose uses a
structural encoder to extract features from anatomical images, then employs a
conditional diffusion process to blend noise and anatomical images at multiple
scales and gradually map them to dose distribution maps. To enhance the dose
prediction distribution for organs at risk, SP-DiffDose utilizes
SwinTransformer in the deeper layers of the network to capture features at
different scales in the image. To learn good representations from the fused
features, SP-DiffDose passes the fused features through a designed projector,
improving dose prediction accuracy. Finally, we evaluate SP-DiffDose on an
internal dataset. The results show that SP-DiffDose outperforms existing
methods on multiple evaluation metrics, demonstrating the superiority and
generalizability of our method.