Zero-Dose PET Synthesis for Patients with Glioblastoma by Mixture-of-Experts-Based TransUNet

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.