MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net For Multi-Modal Alzheimer's Classification
This work addresses the challenge of multi-modal data scarcity for Alzheimer's disease diagnosis, but it is incremental as it builds on existing cross-modal synthesis methods.
The paper tackled the problem of limited availability of corresponding MRI and PET scans for Alzheimer's disease diagnosis by using a 3D U-Net to synthesize FDG-PET scans from MRI, resulting in a classification accuracy improvement from 70.18% with MRI alone to 74.43% with synthesized PET and MRI.
Recent studies suggest that combined analysis of Magnetic resonance imaging~(MRI) that measures brain atrophy and positron emission tomography~(PET) that quantifies hypo-metabolism provides improved accuracy in diagnosing Alzheimer's disease. However, such techniques are limited by the availability of corresponding scans of each modality. Current work focuses on a cross-modal approach to estimate FDG-PET scans for the given MR scans using a 3D U-Net architecture. The use of the complete MR image instead of a local patch based approach helps in capturing non-local and non-linear correlations between MRI and PET modalities. The quality of the estimated PET scans is measured using quantitative metrics such as MAE, PSNR and SSIM. The efficacy of the proposed method is evaluated in the context of Alzheimer's disease classification. The accuracy using only MRI is 70.18% while joint classification using synthesized PET and MRI is 74.43% with a p-value of $0.06$. The significant improvement in diagnosis demonstrates the utility of the synthesized PET scans for multi-modal analysis.