Evidence map›Paper›PMID 41689688›Full record

ArticleEJNMMI physics2026

Patlak-PBNT: a simple population-based Patlak model to generate [

Lianghua Li, Wenjian Gu, Wentong Yang, Weijun Wei, Jianjun Liu, Gang Huang, Junbo Ge, Gongning Luo, Shiming Xu, Yun Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EJNMMI physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

10 authors.

Lianghua Li *Oriental Pan-Vascular Devices Innovation College, University of Shanghai for Science and Technology, Shanghai, 200093, China.ORCID http://orcid.org/0000-0002-1062-7160
Wenjian Gu *Faculty of Computing, Harbin Institute of Technology, Harbin, China.
Wentong YangUnited Imaging Healthcare Group Co., Ltd, Shanghai, China.
Weijun WeiDepartment of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China.
Jianjun LiuDepartment of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China.
Gang HuangDepartment of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China.
Junbo GeOriental Pan-Vascular Devices Innovation College, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Gongning LuoFaculty of Computing, Harbin Institute of Technology, Harbin, China. luogongning@hit.edu.cn.
Shiming XuOriental Pan-Vascular Devices Innovation College, University of Shanghai for Science and Technology, Shanghai, 200093, China. xsm2020@usst.edu.cn.
Yun ZhouUnited Imaging Healthcare Group Co., Ltd, Shanghai, China. yun.zhou@united-imaging.com.ORCID http://orcid.org/0000-0001-9135-336X

Funding

Chongqing MuniciKey Research and Development Program of China 2020YFA0909000National Natural Science Foundation of China 62272135, 62372135, 82127807, and 62001144Shanghai Key Laboratory of Molecular Imaging 18DZ2260400
6 · The paper itself

Abstract

purposeThe objective of this study is to generate reliable K

methodsWe proposed a population-based Patlak model named Patlak-PBNT, which incorporates a population-based "normalized time" (PBNT) into the traditional Patlak plot. This model does not require long-duration PET scans to obtain an image-derived input function (IDIF) of full measured kinetics, thereby generating reliable K

resultsCompared to the traditional Patlak, Patlak-PBNT effectively reduces PET scan duration while maintaining the quality of generated K

conclusionThe proposed Patlak-PBNT model reduces the dependency on a complete input function, thereby avoiding the need for long-duration PET scans typically required to obtain a full input function. When utilizing dynamic PET images of identical scan durations (20-60 min post-injection), the K

Indexed as

[68Ga]Ga-PSMA-11Parametric imagePatlak plotTotal-body PET

Identifiers

PMID41689688
PMCPMC13009459

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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 Socratic graph.