ArticleBio-algorithms and med-systems2023
The properties of the positronium lifetime image reconstruction based on maximum likelihood estimation.
Article in Bio-algorithms and med-systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- First positronium lifetime imaging usingScientific reports · 2026Article
- Phantom imaging demonstration of positronium lifetime with a long axial field-of-view PET/CT andEJNMMI physics · 2025Article
- A statistical reconstruction algorithm for positronium lifetime imaging using time-of-flight positron emission tomography.IEEE transactions on radiation and plasma medical sciences · 2025Article
- First positronium lifetime imaging with scandium-44 on a long axial field-of-view PET/CT.Frontiers in nuclear medicine · 2025Article
- Enhanced positronium lifetime imaging through two-component reconstruction in time-of-flight positron emission tomography.Frontiers in physics · 2024Article
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4 authors.
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Abstract
The positronium lifetime imaging (PLI) reconstruction is a technique used in time-of-flight (TOF) positron emission tomography (PET) imaging that involves measuring the lifespan of positronium, which is a metastable electron-positron pair that arises when a PET molecule releases a positron, prior to its annihilation. We have previously developed a maximum likelihood (ML) algorithm for PLI reconstruction and demonstrated that it can generate quantitatively accurate lifetime images for a 570 ps (pico-seconds) TOF PET system. In this study, we conducted further investigations into the statistical properties of the algorithm, including the variability of the reconstruction results, the sensitivity of the algorithm to the number of acquired PLI events and its robustness to hyperparameter choices. Our findings indicate that the proposed ML method produces sufficiently stable lifetime images to enable reliable distinction of regions of interest. Moreover, the number of PLI events required to produce quantitatively accurate lifetime images is computationally plausible. These results demonstrate the potential of our ML algorithm for advancing the capabilities of TOF PET imaging.
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