Deep Learning-based Disambiguation for Multiple AD Radiotracers using PET/MRI

Motivation: AD patients must undergo repeated visits for amyloid and tau radiotracer imaging, leading to high costs and dose concerns due to PET's inability to simultaneously acquire multiple radiotracers during a single session. Goal(s): Using PET/MRI scans, we used deep learning to create separate amyloid and tau PET images from a simulated combined dual-tracer image. Approach: We simulated a combined amyloid-tau image by blending co-registered list-mode data and employed a 2.5D U-Net architecture for effective separation. Results: Mixed-dose models, incorporating physics-inspired data augmentation and MR information, exhibited enhanced anatomical preservation and reduced variability in quantitative metrics. Impact: The demonstrated separation of a simulated combined amyloid and tau PET/MRI study into its individual components using DL may allow for simultaneous injection of multiple radiotracers in a single acquisition, streamlining the imaging process for AD patients.