Keywords: AI/ML Image Reconstruction, PET/MR
Motivation: Through the utilization of MRI images: T1, T1c, ASL, and T2-FLAIR, the PET image can be synthesized without requiring the patients to face radiation exposure.
Goal(s): To synthesize high-quality FDG-PET images by multi-contrast MRI, using TransUNet with mixture-of-experts (MoE) in order to ensemble both local and global feature maps.
Approach: TransUNet was utilized as the backbone to synthesize PET from multi-contrast MRI. A mixture-of-experts (MoE) was designed to assign a weight map to each layer's feature.
Results: Multi-contrast MRIs can be used to synthesize FDG-PET images for GBM cases by the proposed MoE-based TransUNet model, and incorporating MoE in TransUNet yields solid results.
Impact: To synthesize high-quality FDG-PET images by multi-contrast MR via deep learning, this zero-dose project can be transformative in today’s modern field of medicine, It would not require the patients to face radiation exposure while improving the accessibility of PET information.
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