ArticleIEEE transactions on medical imaging2024
SPLIT: Statistical Positronium Lifetime Image Reconstruction via Time-Thresholding.
Article in IEEE transactions on medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06242119 (Clinical Use of a J-PET Scanner Prototype Made of Plastic Scintilators), which is not on this map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Clinical Use of a J-PET Scanner Prototype Made of Plastic Scintilators
Who cites it
12 citing papers in PubMed.
- First ex-vivo positronium imaging of tissues with modular J-PET scanner usingZeitschrift fur medizinische Physik · 2026Article
- Feasibility simulation study of positronium lifetime imaging with the Biograph Vision Quadra and J-PET tomographs.EJNMMI physics · 2026Article
- First positronium lifetime imaging usingScientific reports · 2026Article
- Improved random coincidence estimation including triple coincidence detection in PET.Frontiers in nuclear medicine · 2026Article
- Integrating CT image reconstruction, segmentation, and large language models for enhanced diagnostic insight.Medical & biological engineering & computing · 2026Article
- High-Resolution Positronium Lifetime Tomography at Clinical Activity Levels on the PennPET Explorer.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Phantom imaging demonstration of positronium lifetime with a long axial field-of-view PET/CT andEJNMMI physics · 2025Article
- Fast high-resolution lifetime image reconstruction for positron lifetime tomography.Communications physics · 2025Article
- First positronium lifetime imaging with scandium-44 on a long axial field-of-view PET/CT.Frontiers in nuclear medicine · 2025Article
- High-resolution positronium lifetime tomography by the method of moments.Physics in medicine and biology · 2024Article
- Positronium image of the human brain in vivo.Science advances · 2024Article
- Positronium lifetime validation measurements using a long-axial field-of-view positron emission tomography scanner.EJNMMI physics · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Positron emission tomography (PET) is a widely utilized medical imaging modality that uses positron-emitting radiotracers to visualize biochemical processes in a living body. The spatiotemporal distribution of a radiotracer is estimated by detecting the coincidence photon pairs generated through positron annihilations. In human tissue, about 40% of the positrons form positroniums prior to the annihilation. The lifetime of these positroniums is influenced by the microenvironment in the tissue and could provide valuable information for better understanding of disease progression and treatment response. Currently, there are few methods available for reconstructing high-resolution lifetime images in practical applications. This paper presents an efficient statistical image reconstruction method for positronium lifetime imaging (PLI). We also analyze the random triple-coincidence events in PLI and propose a correction method for random events, which is essential for real applications. Both simulation and experimental studies demonstrate that the proposed method can produce lifetime images with high numerical accuracy, low variance, and resolution comparable to that of the activity images generated by a PET scanner with currently available time-of-flight resolution.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.