Evidence map›Paper›PMID 39621094›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

Self-supervised neural network for Patlak-based parametric imaging in dynamic [

Wenjian Gu, Zhanshi Zhu, Ze Liu, Yihan Wang, Yanxiao Li, Tianyi Xu, Weiping Liu, Gongning Luo, Kuanquan Wang, Yun Zhou

Abstract read
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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

10 authors.

Wenjian GuFaculty of Computing, Harbin Institute of Technology, Harbin, China.
Zhanshi ZhuFaculty of Computing, Harbin Institute of Technology, Harbin, China.
Ze LiuUnited Imaging Healthcare Technology Group Co., Ltd, Shanghai, China.
Yihan WangUnited Imaging Healthcare Technology Group Co., Ltd, Shanghai, China.
Yanxiao LiUnited Imaging Healthcare Technology Group Co., Ltd, Shanghai, China.
Tianyi XuUnited Imaging Healthcare Technology Group Co., Ltd, Shanghai, China.
Weiping LiuUnited Imaging Healthcare Technology Group Co., Ltd, Shanghai, China.
Gongning LuoFaculty of Computing, Harbin Institute of Technology, Harbin, China. luogongning@hit.edu.cn.
Kuanquan WangFaculty of Computing, Harbin Institute of Technology, Harbin, China. wangkq@hit.edu.cn.
Yun ZhouUnited Imaging Healthcare Technology Group Co., Ltd, Shanghai, China. yun.zhou@united-imaging.com.ORCID 0000-0001-9135-336X

Funding

National Natural Science Foundation of China 62001144National Natural Science Foundation of China 62272135National Natural Science Foundation of China 62372135
6 · The paper itself

Abstract

purposeThe objective of this study is to generate reliable K

methodsWe proposed a self-supervised neural network algorithm with Patlak graphical analysis (SN-Patlak) to generate K

resultsThe K

conclusionsThe SN-Patlak parametric imaging algorithm is robust and reliable for quantification of 10-min dynamic [

Indexed as

Fluorodeoxyglucose F18Image Processing, Computer-AssistedNeural Networks, ComputerPositron-Emission TomographyWhole Body ImagingAlgorithmsFemaleHumansMaleMiddle AgedRadiopharmaceuticalsFluorodeoxyglucose F18Radiopharmaceuticals[18F]FDGParametric imagePatlak plotSelf-supervised neural networkTotal-body PET

Identifiers

PMID39621094

What OpenQuestion holds

Textmetadata
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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.