Comparing List-mode and Count-Mixing Techniques for Deep Learning-Based Disambiguation of AD Radiotracers in PET/MRI

Motivation: AD patients require multiple visits for amyloid and tau imaging because PET cannot acquire multiple radiotracers in a single session. Goal(s): DL-based separation of amyloid and tau radiotracers from mixed-dose images could reduce AD patient visits, but current DL applications are hindered by computational costs and other challenges of list-mode dose-mixing. Approach: We propose count-mixing as a compute-efficient alternative for simulating dose-mixing, which can then be used for deep learning (DL)-based radiotracer separation. Results: PET/MR count-mixing can serve as an alternative to list-mode dose-mixing. The approach agrees with list-mode dose-mixing, exhibits enhanced quantitative performance, and equivalent anatomical preservation. Impact: Count-mixing provides a faster, compute-efficient way to generate realistic mixed-dose PET images, enhancing model training and scaling DL applications for radiotracer separation. This approach could enable simultaneous injection of multiple radiotracers in a single acquisition for AD patients.