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This work introduces a novel surface-based diffusion bridge framework DB-SUiT for MRI-to-PET translation that operates natively on the cortical manifold and demonstrates high-fidelity synthesis that substantially outperforms other baselines.
Refined annotations can boost PE segmentation performance more than changes in model training, challenging assumptions about model superiority.
Synthesized PET scans from MRI can nearly match the diagnostic accuracy of real PET for Alzheimer's, potentially unlocking wider access to crucial functional insights.