This study aims to develop a novel Cycle-guided Denoising Diffusion
Probability Model (CG-DDPM) for cross-modality MRI synthesis. The CG-DDPM
deploys two DDPMs that condition each other to generate synthetic images from
two different MRI pulse sequences. The two DDPMs exchange random latent noise
in the reverse processes, which helps to regularize both DDPMs and generate
matching images in two modalities. This improves image-to-image translation
ac-curacy. We evaluated the CG-DDPM quantitatively using mean absolute error
(MAE), multi-scale structural similarity index measure (MSSIM), and peak
sig-nal-to-noise ratio (PSNR), as well as the network synthesis consistency, on
the BraTS2020 dataset. Our proposed method showed high accuracy and reliable
consistency for MRI synthesis. In addition, we compared the CG-DDPM with
several other state-of-the-art networks and demonstrated statistically
significant improvements in the image quality of synthetic MRIs. The proposed
method enhances the capability of current multimodal MRI synthesis approaches,
which could contribute to more accurate diagnosis and better treatment planning
for patients by synthesizing additional MRI modalities.

该研究旨在发展一种新型的循环引导去噪扩散概率模型（Cycle-guided Denoising Diffusion Probability Model，CG-DDPM），用于跨模态 MRI 合成，并使用 BraTS2020 数据集定量评估。该方法在 MRI 合成中具有很高的准确性，并与几种现有最先进的网络进行了比较，证明了合成 MRIs 的图像质量的显著改进。该方法增强了当前多模态 MRI 合成方法的能力，为患者的更精确诊断和更好的治疗规划做出贡献。