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Abstract #1523

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

Ashwin Kumar1, Donghoon Kim1, Brandon Ho1, Mackenzie Carlson2, Elizabeth Mormino2, Akshay Chaudhari1, Christina Young2, Kevin Chen3, Mehdi Khalighi1, and Greg Zaharchuk1
1Radiology, Stanford University, Stanford, CA, United States, 2Neurology, Stanford University, Stanford, CA, United States, 3Biomedical Engineering, National Taiwan University, Taipei, Taiwan

Synopsis

Keywords: PET/MR, Multimodal, PET/MR; Image Synthesis; Alzheimer's Disease; Disambiguation

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.

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Keywords