Evidence map›Paper›PMID 42171886›Full record

ArticleEJNMMI physics2026

A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans.

Jingxi Hu, Yuqian Huang, Qiyang Zhang, Yingying Hu, Yumo Zhao, Xu Zhang, Wei Fan, Hairong Zheng, Dong Liang, Zhanli Hu and 3 more

Abstract read
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
0cells of the map it votes in
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

The trial behind it

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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

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Jingxi Hu *Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Yuqian Huang *Sun Yat-sen University Cancer Center, Guangzhou, 510062, China.
Qiyang Zhang *Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Yingying HuSun Yat-sen University Cancer Center, Guangzhou, 510062, China.
Yumo ZhaoUnited Imaging Healthcare Group, Central Research Institute, Shanghai, 201807, China.
Xu ZhangSun Yat-sen University Cancer Center, Guangzhou, 510062, China.
Wei FanSun Yat-sen University Cancer Center, Guangzhou, 510062, China.
Hairong ZhengResearch Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Dong LiangResearch Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Zhanli HuResearch Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Changhui JiangNational Innovation Center for Advanced Medical Devices, Shenzhen, 518055, China. ch.jiang@nmed.org.cn.
Xiangjian HeUniversity of Nottingham Ningbo China, Ningbo, 315000, China. Sean.He@nottingham.edu.cn.
Na ZhangResearch Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. na.zhang@siat.ac.cn.ORCID http://orcid.org/0000-0001-9510-4520

Funding

National Key Research and Development Program of China 2024YFE0202400National Natural Science Foundation of China 12326607, 82372038Natural Science Foundation of Guangdong Province in China 2023B1515120007, 2024B1515040018Shenzhen Excellent Technological Innovation Talent Training Project of China RCJC20200714114436080Shenzhen-Hong Kong-Macao Science and Technology Plan Project (Category C Project) under Shenzhen Municipal Science and Technology Innovation Commission SGDX20230821092359002Shenzhen Medical Research Fund of China B2301002Shenzhen Science and Technology Program KJZD20230923114009018Shenzhen Science and Technology Program of China KJZD20240903101307010, JCYJ20220818101804009Yongjiang Technology Innovation Project 2022A-097-G
6 · The paper itself

Abstract

backgroundPediatric lymphoma patients undergo multiple

methodsTwo hundred pediatric patients with FDG-avid lymphoma imaged on a total-body PET/CT scanner were retrospectively analyzed. From 10-min list-mode data, non-attenuation-corrected (NAC) PET images at 3-300 s were retrospectively reconstructed, and 600-s attenuation-corrected (AC) PET served as the reference. Whole-body coronal maximum-intensity projections (MIPs) were converted into 2D images and divided into 64 × 64 patches. For each acquisition duration, an independent latent diffusion-based autoencoder was trained to map the corresponding short-duration NAC PET MIPs to AC-like coronal PET MIPs. The intended clinical application was rapid pre-CT triage, with primary emphasis on the ultra-short 3-s setting, while longer durations were analyzed comparatively across count levels. Image quality was evaluated using PSNR, SSIM, and NMSE, as well as lesion ROI-based SUVmax and SUVmean. Three benchmark models (U-Net, GAN, and DDPM) were trained under identical settings for comparison. In addition, two nuclear medicine physicians assessed image quality using 5-point Likert scales, and lesion-level ROC and ALROC analyses were performed.

resultsSnapPET substantially improved image quality over NAC input for all acquisition times. For 3-s scans, PSNR increased from 28.6 ± 4.3 to 35.3 ± 6.0 and SSIM from 0.85 ± 0.09 to 0.98 ± 0.02, while NMSE decreased from 0.23 ± 0.18 to 0.03 ± 0.01 (all p < 0.001). SUV metrics in lesion ROIs showed high correlation with reference AC PET, with minimal bias. Compared with UNet, GAN, and DDPM, SnapPET achieved the best quantitative metrics and the highest lesion-level ROC and ALROC curves, indicating superior detectability and localization. Reader scores for noise suppression, artifact reduction, contrast retention, and lesion discrimination were consistently higher for SnapPET images, particularly for 3- and 15-s acquisitions.

conclusionSnapPET enables rapid pediatric PET imaging by generating high-quality, AC-like 2D images directly from ultra-short NAC acquisitions. The method improves image quality, preserves quantitative accuracy, and enhances lesion detectability compared with both NAC input and competing deep-learning baselines. This CT-sparing screening and triage approach has the potential to reduce reliance on attenuation-correction CT and support personalized, lower-dose CT strategies in pediatric lymphoma care.

Indexed as

Deep learningPositron emission tomographyRadiation

Identifiers

PMID42171886
PMCPMC13247023

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LicenceCC BY-NC-ND
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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.