Investigation of Quantum Entanglement Information for β⁺γ Coincidences

<br><b>Objective:</b> 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. <br><b>Methods:</b> 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 (E<sub>C</sub>). 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. <br><b>Results:</b> 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 E<sub>C</sub> 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 E<sub>C</sub> filter (0.837) greatly improved upon that of the θ-based filter (0.541). <br><b>Conclusions:</b> 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.

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Publication Details

Journal
Bio-Algorithms and Med-Systems
Published
2024-12-17
DOI
https://doi.org/10.5604/01.3001.0054.9079
Citations
4
Primary Topic
Quantum Mechanics and Applications
Type
article
Field-Weighted Citation Impact
1.35
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article

Investigation of Quantum Entanglement Information for β⁺γ Coincidences

Phillip H. Kuo, Lars R. Furenlid, Gregory R. Romanchek, Matthew A. Kupinski et al.
4 citations
Bio-Algorithms and Med-Systems
Quantum Mechanics and Applications
1.35
article

Investigation of Quantum Entanglement Information for β⁺γ Coincidences

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

Abstract

<br><b>Objective:</b> 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. <br><b>Methods:</b> 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 (E<sub>C</sub>). 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. <br><b>Results:</b> 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 E<sub>C</sub> 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 E<sub>C</sub> filter (0.837) greatly improved upon that of the θ-based filter (0.541). <br><b>Conclusions:</b> 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.

Bio-Algorithms and Med-SystemsVol. 20(Special Issue)
University of Arizona (US), University of Illinois Urbana-Champaign (US), University of Massachusetts Chan Medical School (US), University of California, Santa Cruz (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 19%
Quantum Mechanics and Applications
1.35
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