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MRI and PET are crucial diagnostic tools for brain diseases, as they providecomplementary information on brain structure and function. However, PETscanning is costly and involves radioactive exposure, resulting in a lack ofPET. Moreover, simultaneous PET and MRI at ultra-high-field are currentlyhardly infeasible. Ultra-high-field imaging has unquestionably proven valuablein both clinical and academic settings, especially in the field of cognitiveneuroimaging. These motivate us to propose a method for synthetic PET fromhigh-filed MRI and ultra-high-field MRI. From a statistical perspective, thejoint probability distribution (JPD) is the most direct and fundamental meansof portraying the correlation between PET and MRI. This paper proposes a noveljoint diffusion attention model which has the joint probability distributionand attention strategy, named JDAM. JDAM has a diffusion process and a samplingprocess. The diffusion process involves the gradual diffusion of PET toGaussian noise by adding Gaussian noise, while MRI remains fixed. JPD of MRIand noise-added PET was learned in the diffusion process. The sampling processis a predictor-corrector. PET images were generated from MRI by JPD of MRI andnoise-added PET. The predictor is a reverse diffusion process and the correctoris Langevin dynamics. Experimental results on the public Alzheimer's DiseaseNeuroimaging Initiative (ADNI) dataset demonstrate that the proposed methodoutperforms state-of-the-art CycleGAN for high-field MRI (3T MRI). Finally,synthetic PET images from the ultra-high-field (5T MRI and 7T MRI) beattempted, providing a possibility for ultra-high-field PET-MRI imaging.

Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model