Abstract
Diffusion models have demonstrated significant potential in producing high-quality images in medical image translation to aid disease diagnosis, localization, and treatment. Nevertheless, current diffusion models often fall short when it comes to faithfully translating medical images. They struggle to accurately preserve anatomical structures, especially when working with unpaired datasets. In this study, we introduce the Frequency Decoupled Diffusion Model (FDDM) for MR-to-CT conversion. The differences between MR and CT images lie in both anatomical structures (e.g., the outlines of organs or bones) and the data distribution (e.g., intensity values and contrast within). Therefore, FDDM first converts anatomical information using an initial conversion module. Then, the converted anatomical information guides a subsequent diffusion model to generate high-quality CT images. Our diffusion model uses a dual-path reverse diffusion process for low-frequency and high-frequency information, achieving a better balance between image quality and anatomical accuracy. We extensively evaluated FDDM using public datasets for brain MR-to-CT and pelvis MR-to-CT translations. The results show that FDDM outperforms other generative adversarial network (GAN)-based, variational autoencoder (VAE)-based, and diffusion-based models. The evaluation metrics included Fréchet Inception Distance (FID), mean absolute error (MAE), mean squared error (MSE), Structural Similarity Index Measure (SSIM), and Dice similarity coefficient (DICE). FDDM achieved the best scores on all metrics for both datasets, particularly excelling in FID, with scores of 25.9 for brain data and 29.2 for pelvis data, significantly outperforming other methods. These results demonstrate that FDDM can generate high-quality target domain images while maintaining the accuracy of translated anatomical structures, thereby facilitating more precise/accurate downstream tasks including anatomy segmentation and radiotherapy planning.