Recently, the diffusion model has emerged as a superior generative model that
can produce high-quality images with excellent realism. There is a growing
interest in applying diffusion models to image translation tasks. However, for
medical image translation, the existing diffusion models are deficient in
accurately retaining structural information since the structure details of
source domain images are lost during the forward diffusion process and cannot
be fully recovered through learned reverse diffusion, while the integrity of
anatomical structures is extremely important in medical images. Training and
conditioning diffusion models using paired source and target images with
matching anatomy can help. However, such paired data are very difficult and
costly to obtain, and may also reduce the robustness of the developed model to
out-of-distribution testing data. We propose a frequency-guided diffusion model
(FGDM) that employs frequency-domain filters to guide the diffusion model for
structure-preserving image translation. Based on its design, FGDM allows
zero-shot learning, as it can be trained solely on the data from the target
domain, and used directly for source-to-target domain translation without any
exposure to the source-domain data during training. We trained FGDM solely on
the head-and-neck CT data, and evaluated it on both head-and-neck and lung
cone-beam CT (CBCT)-to-CT translation tasks. FGDM outperformed the
state-of-the-art methods (GAN-based, VAE-based, and diffusion-based) in all
metrics, showing its significant advantages in zero-shot medical image
translation.

使用频域滤波器来指导扩散模型，以实现结构保留图像翻译的频率引导扩散模型（FGDM），同时在医学图像翻译中具有零样本学习的能力。 该模型在结构保留医学图像翻译任务中具有良好的性能。