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This study aims to develop a novel Cycle-guided Denoising DiffusionProbability Model (CG-DDPM) for cross-modality MRI synthesis. The CG-DDPMdeploys two DDPMs that condition each other to generate synthetic images fromtwo different MRI pulse sequences. The two DDPMs exchange random latent noisein the reverse processes, which helps to regularize both DDPMs and generatematching images in two modalities. This improves image-to-image translationac-curacy. We evaluated the CG-DDPM quantitatively using mean absolute error(MAE), multi-scale structural similarity index measure (MSSIM), and peaksig-nal-to-noise ratio (PSNR), as well as the network synthesis consistency, onthe BraTS2020 dataset. Our proposed method showed high accuracy and reliableconsistency for MRI synthesis. In addition, we compared the CG-DDPM withseveral other state-of-the-art networks and demonstrated statisticallysignificant improvements in the image quality of synthetic MRIs. The proposedmethod enhances the capability of current multimodal MRI synthesis approaches,which could contribute to more accurate diagnosis and better treatment planningfor patients by synthesizing additional MRI modalities.

Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis