Evidence map›Paper›PMID 41585607›Full record

ArticleFrontiers in physics2024

Enhanced positronium lifetime imaging through two-component reconstruction in time-of-flight positron emission tomography.

Zhuo Chen, Chien-Min Kao, Hsiun-Hsiung Huang, Lingling An

Abstract read
In one paragraph

Article in Frontiers in physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. High-Resolution Positronium Lifetime Tomography at Clinical Activity Levels on the PennPET Explorer.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025
    Article
  4. Article
  5. Article
  6. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zhuo ChenGraduate Interdisciplinary Program in Statistics and Data Science, University of Arizona, Tucson, AZ, United States.
Chien-Min KaoDepartment of Radiology, University of Chicago, Chicago, IL, United States.
Hsiun-Hsiung HuangDepartment of Statistics and Data Science, University of Central Florida, Orlando, FL, United States.
Lingling AnGraduate Interdisciplinary Program in Statistics and Data Science, University of Arizona, Tucson, AZ, United States.

Funding

A preclinical integrated PET/EPR imaging system-Rev3R01EB029948 · NIBIB · UNIVERSITY OF CHICAGO · PI EPEL, BORIS, KAO, CHIEN-MIN · 2020 to 2023
$1.7M
NIBIB NIH HHS R01 EB029948
6 · The paper itself

Abstract

Positronium lifetime imaging (PLI) is a newly demonstrated technique possible with time-of-flight (TOF) positron emission tomography (PET), capable of producing an image reflecting the lifetime of the positron, more precisely ortho-positronium (o-Ps), before annihilation, in addition to the traditional uptake image of the PET tracer. Due to the limited time resolution of TOF-PET systems and the added complexities in physics and statistics, lifetime image reconstruction presents a challenge. Recently, we described a maximum-likelihood approach for PLI by considering only o-Ps. In real-world scenarios, other populations of positrons that exhibit different lifetimes also exist. This paper introduces a novel two-component model aimed at enhancing the accuracy of o-Ps lifetime images. Through simulation studies, we compare this new model with the existing single-component model and demonstrate its superior performance in accurately capturing complex lifetime distributions.

Indexed as

image reconstructionmaximum likelihood estimationpositronium lifetime imagingtime-of-flight positron emission tomographytwo-component model

Identifiers

PMID41585607
PMCPMC12829466

What OpenQuestion holds

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Registered trials

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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.