Maximum Likelihood Estimation Yields Accurate Line-of-Response Assignment for Positron + Prompt Gamma Ray Events in Multiplexed PET (mPET)
Sarah J. Zou, Garry Chinn, Muhammad Nasir Ullah, Craig S. Levin

TL;DR
This paper introduces a maximum likelihood estimation method to accurately assign lines-of-response in multiplexed PET imaging involving positron+prompt-gamma emitters, improving multi-tracer imaging accuracy.
Contribution
The study develops and validates a novel MLE framework that enhances line-of-response assignment accuracy for complex PET events involving multiple radiotracers.
Findings
Achieved over 96% accuracy in LOR assignment for $^{22}$Na sources.
Achieved over 94% accuracy in LOR assignment for $^{124}$I sources.
Demonstrated comparable image quality to benchmark methods in simulated phantom studies.
Abstract
For accurate disease characterization using positron emission tomography (PET), it is desirable to image multiple radiotracers in a single scan. Conventional PET methods cannot do this due to the indistinguishable annihilation photons produced by different radiotracers. One approach is to label one radiotracer with a positron+prompt-gamma () isotope producing triple coincidences, and another with a pure positron-emitting () isotope producing double coincidences. However, emitters present challenges in correctly identifying the two annihilation photons, or equivalently, assigning the correct line-of-response (LOR) to triple-photon coincidence events. Here, we propose a maximum likelihood estimation (MLE) framework leveraging spatial, timing, and energy information to determine the most probable LOR. Simulation studies validated…
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