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1 2024

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Investigation of Quantum Entanglement Information for β+ γ Coincidences

Romanchek Gregory R., Shoop Greyson, Kupinski Matthew A., Kuo Phillip H., King Michael, Furenlid Lars R., Abbaszadeh Shiva

Bio-Algorithms and Med-Systems

2024

Vol. 20, Special issue

27--34

Objective: We assess the viability of using quantum entanglement (QE) information for improving event classification in a combined PET-Compton Camera (PET-CC) system, particularly in the potential for distinguishing true positron annihilation events from Random events due to prompt gamma contamination for β + and γ emitting isotopes. Methods: Monte Carlo GATE simulations were performed to evaluate the sensitivity and accuracy of event classification in various scenarios using ground truth data, including standard PET events and Compton Camera interactions. QE-sensitive data subsets were identified and filtered based on either polar scattering angles (θ) or the energy of the initial Compton scatter (EC ). The enhancement ratio - ratio of the difference of azimuthal scattering at Δφ = 90° and 0° - and fraction of post-filter Trues were used as metrics. Results: The simulations showed that QE information could assist in resolving energy ambiguities, particularly in cases where prompt gamma emissions complicate event pairing. Filtering based on EC provided a higher enhancement ratio (R ≈ 1.8) compared to θ-based filtering (R ≈ 1.4), indicating better discrimination between True and Random events. The ratio of Trues to Total events passing the EC filter (0.837) greatly improved upon that of the θ-based filter (0.541). Conclusions: Our results suggest that energy-based filtering is more effective in leveraging QE information, but further refinement of filtering algorithms is needed to fully realize its benefits. While QE has the potential to improve event classification in PET-CC systems for a few coincidence cases, further studies are needed to utilize this paradigm in image formation.