Evidence map›Paper›PMID 40471320›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

Clinical validation of a deep learning model for low-count PET image enhancement.

Qigang Long, Yan Tian, Boyang Pan, Zhenchun Xu, Wenqian Zhang, Libo Xu, Wenhuan Fan, Taotao Pan, Nan-Jie Gong

Abstract readValidation Study
PubMed Publisher
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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

9 authors.

Qigang Long *School of Medical Information Engineering, Zunyi Medical University, Zunyi, 563000, China.
Yan Tian *Department of Nuclear Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
Boyang PanLaboratory for Intelligent Medical Imaging, Tsinghua Cross-Strait Research Institute, Xiamen, 361000, China.
Zhenchun XuDepartment of Nuclear Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
Wenqian ZhangDepartment of Nuclear Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
Libo XuRadioDynamic Medical, Shanghai, 200333, China.
Wenhuan FanDepartment of Nuclear Medicine, Chongqing General Hospital, Chongqing University, Chongqing, 400013, China.
Taotao PanSchool of Medical Information Engineering, Zunyi Medical University, Zunyi, 563000, China. pttwss@163.com.
Nan-Jie GongShanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, 200062, China. nanjie.gong@gmail.com.ORCID 0000-0001-9249-413X

Funding

Guizhou Provincial Youth Science and Technology Talents Growth Project [2024]138the Project of Science and Technology Planning of Zunyi City HZ[2022]38
6 · The paper itself

Abstract

purposeTo investigate the effects of the deep learning model RaDynPET on fourfold reduced-count whole-body PET examinations.

methodsA total of 120 patients (84 internal cohorts and 36 external cohorts) undergoing

resultsIn internal cohorts, R30 demonstrated significantly higher image quality scores than G30 and G120. R30 showed excellent agreement with G120 for liver and lesion SUV values and surpassed G120 in liver SNR and CNR. Liver SNR and CNR of R30 were comparable to G120 in thin group, and the CNR of R30 was comparable to G120 in young age group. In external cohorts, R30 maintained strong SUV agreement with G120, with lesion-level sensitivity and specificity of 95.45% and 98.41%, respectively. There was no statistical difference in lesion detection between R30 and G120. RaDynPET achieved the highest PSNR and SSIM among deep learning methods.

conclusionThe RaDynPET model effectively restored high image quality while maintaining SUV agreement for

Indexed as

Deep LearningImage EnhancementImage Processing, Computer-AssistedPositron Emission Tomography Computed TomographyAdultAgedAged, 80 and overFemaleFluorodeoxyglucose F18HumansMaleMiddle AgedSignal-To-Noise RatioFluorodeoxyglucose F18Deep learningImage enhancementImage qualityLow-countPET/CT

Identifiers

PMID40471320

What OpenQuestion holds

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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 OpenQuestion graph.