A Prior-information-guided Residual Diffusion Model for Multi-modal PET Synthesis from MRI

Alzheimer's disease (AD) leads to abnormalities in various biomarkers (i.e., amyloid-β and tau proteins), which makes PET imaging (which can detect these biomarkers) essential in AD diagnosis. However, the high radiation risk of PET imaging limits its scanning number within a short period, presenting challenges to the joint multi-biomarker diagnosis of AD. In this paper, we propose a novel unified model to simultaneously synthesize multi-modal PET images from MRI, to achieve low-cost and time-efficient joint multi-biomarker diagnosis of AD. Specifically, we incorporate residual learning into the diffusion model to emphasize inter-domain differences between PET and MRI, thereby forcing each modality to maximally reconstruct its modality-specific details. Furthermore, we leverage prior information, such as age and gender, to guide the diffusion model in synthesizing PET images with semantic consistency, enhancing their diagnostic value. Additionally, we develop an intra-domain difference loss to ensure that the intra-domain differences among synthesized PET images closely match those among real PET images, promoting more accurate synthesis, especially at the modality-specific information. Extensive experiments conducted on the ADNI dataset demonstrate that our method achieves superior performance both quantitatively and qualitatively compared to the state-of-the-art methods. All codes for this study have been uploaded to GitHub (https://github.com/Ouzaixin/ResDM).