Two‐stage diffusion model deriving FDG‐PET from T1 Weighted Magnetic Resonance Images for diagnosis of Alzheimer’s disease

Abstract Background Alzheimer’s Disease (AD) is a neurodegenerative disease which is characterized by cognitive and functional impairment, and defined by triad of Amyloid status, Tau pathology, and Neuroimaging marker of neurodegeneration or neuronal injury. An early diagnosis of AD can be challenging and it can be detected earlier in brain functional imaging than brain structural imaging. Method We develop a pipeline for synthesizing FDG‐PET/MRI from source MRI‐T1‐Weighted (MRI‐T1W) images based on denoising diffusion mechanism [1]. The pipeline includes a forward pass which encodes the source MRI‐T1WI images to gaussian noise space, and a backward pass which generates the corresponding FDG‐PET from the derived noises with repeated denoising steps. Furthermore, to validate the effectiveness of the synthesized FDG‐PET images, we also develop multiple types of classifiers and the experiments for both single input of MRI‐T1WI and joint input of MRI‐T1WI and synthesized FDG‐PET. Result The experiments are performed on 1036 of FDG‐PET/MRI pairs (286,474, 276 for Cognitive Normal, Mild Cognitive Impaiement and Alzheimer’s Disease each) are obtained from ADNI [2] datasets and display the promising results with low Mean Absolute Error (MAE) and high Structural Similarity Index Measure (SSIM) for the two stage diffusion denoising based FDG‐PET synthesis pipeline. The results for the additional classification experiments also show that in most cases the joint input enhances the classifiers' performance in accuracy and F1‐score. The results also indicate that the synthesized FDG‐PET images are not only visually similar to the ground truth images, but also improve the performance of machine learning based AD diagnosis. Conclusion This work provides support for the notion that machine learning‐derived image analysis may be a useful approach to improving the diagnosis of AD. Reference: [1] Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems , 33 , 6840‐6851. [2] Petersen, R., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., … & Toga, A. W. (2010). Alzheimer’s disease neuroimaging initiative (ADNI). Neurology , 74 (3), 201‐209.