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Recent studies suggest that combined analysis of Magnetic resonanceimaging~(MRI) that measures brain atrophy and positron emissiontomography~(PET) that quantifies hypo-metabolism provides improved accuracy indiagnosing Alzheimer's disease. However, such techniques are limited by theavailability of corresponding scans of each modality. Current work focuses on across-modal approach to estimate FDG-PET scans for the given MR scans using a3D U-Net architecture. The use of the complete MR image instead of a localpatch based approach helps in capturing non-local and non-linear correlationsbetween MRI and PET modalities. The quality of the estimated PET scans ismeasured using quantitative metrics such as MAE, PSNR and SSIM. The efficacy ofthe proposed method is evaluated in the context of Alzheimer's diseaseclassification. The accuracy using only MRI is 70.18% while jointclassification using synthesized PET and MRI is 74.43% with a p-value of$0.06$. The significant improvement in diagnosis demonstrates the utility ofthe synthesized PET scans for multi-modal analysis.

MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net For Multi-Modal Alzheimer's Classification