Diffusion models have demonstrated significant potential in producing
high-quality images for medical image translation to aid disease diagnosis,
localization, and treatment. Nevertheless, current diffusion models have
limited success in achieving faithful image translations that can accurately
preserve the anatomical structures of medical images, especially for unpaired
datasets. The preservation of structural and anatomical details is essential to
reliable medical diagnosis and treatment planning, as structural mismatches can
lead to disease misidentification and treatment errors. In this study, we
introduced a frequency-decoupled diffusion model (FDDM), a novel framework that
decouples the frequency components of medical images in the Fourier domain
during the translation process, to allow structure-preserved high-quality image
conversion. FDDM applies an unsupervised frequency conversion module to
translate the source medical images into frequency-specific outputs and then
uses the frequency-specific information to guide a following diffusion model
for final source-to-target image translation. We conducted extensive
evaluations of FDDM using a public brain MR-to-CT translation dataset, showing
its superior performance against other GAN-, VAE-, and diffusion-based models.
Metrics including the Frechet inception distance (FID), the peak
signal-to-noise ratio (PSNR), and the structural similarity index measure
(SSIM) were assessed. FDDM achieves an FID of 29.88, less than half of the
second best. These results demonstrated FDDM's prowess in generating
highly-realistic target-domain images while maintaining the faithfulness of
translated anatomical structures.

本研究介绍了一种频率解耦的扩散模型（FDDM），通过在转换过程中将医学图像的频率分量在傅里叶域中分离，实现保留结构并高质量转换图像。通过公开的脑部 MR-to-CT 转换数据集进行了广泛评估，结果表明 FDDM 在产生高度逼真的目标领域图像的同时保持了翻译的解剖结构的准确性。