Evidence map›Paper›PMID 39932542›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.

Yu Lin, Kang Wang, Zhe Zheng, Haojun Yu, Shuguang Chen, WenXin Tang, Yibo He, Huaping Gao, Runjun Yang, Yunzhe Xie and 4 more

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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. Review
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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

14 authors.

Yu Lin *Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
Kang Wang *Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, No. 138 in Yixueyuan Road, Shanghai, 200032, P.R. China.
Zhe ZhengShanghai Institute of Medical Imaging, Shanghai, 200032, China.
Haojun YuShanghai Institute of Medical Imaging, Shanghai, 200032, China.
Shuguang ChenShanghai Institute of Medical Imaging, Shanghai, 200032, China.
WenXin TangShanghai Institute of Medical Imaging, Shanghai, 200032, China.
Yibo HeShanghai Institute of Medical Imaging, Shanghai, 200032, China.
Huaping GaoShanghai Institute of Medical Imaging, Shanghai, 200032, China.
Runjun YangShanghai Institute of Medical Imaging, Shanghai, 200032, China.
Yunzhe XieShanghai Institute of Medical Imaging, Shanghai, 200032, China.
Junjie YangShanghai Institute of Medical Imaging, Shanghai, 200032, China.
Xiaoguang HouShanghai Institute of Medical Imaging, Shanghai, 200032, China.
Shuo WangDigital Medical Research Center, School of Basic Medical Sciences, Fudan University, No. 138 in Yixueyuan Road, Shanghai, 200032, P.R. China. shuowang@fudan.edu.cn.
Hongcheng ShiShanghai Institute of Medical Imaging, Shanghai, 200032, China. shi.hongcheng@zs-hospital.sh.cn.ORCID 0000-0003-1922-1359

Funding

Chinese National Key Clinical Specialty Program YWP2022-007Innovative Medical Device Application Demonstration Program of Shanghai Municipal Commission of Economy and Informatization 23SHS01200Key Program of Ministry of Industry and Information Technology of China CEIEC-2022-ZM02-0219National Key Research and Development Program of China 2022YFC2406902
6 · The paper itself

Abstract

purposeThis study aims to develop and validate a deep learning framework designed to eliminate the second CT scan of dual-tracer total-body PET/CT imaging.

methodsWe retrospectively included three cohorts of 247 patients who underwent dual-tracer total-body PET/CT imaging on two separate days (time interval:1-11 days). Out of these, 167 underwent [

resultsThe MAE for whole-body pseudo-ACCT images ranged from 97.64 to 112.59 HU across four tracers. The deep learning-based ASC PET images demonstrated high similarity to the ground-truth PET images. The MAE of SUV for whole-body PET images was 0.06 for [

conclusionThe proposed deep learning framework, combining RegGAN and non-rigid registration, shows promise in reducing CT radiation dose for dual-tracer total-body PET/CT imaging, with successful validation across multiple tracers.

Indexed as

Deep LearningImage Processing, Computer-AssistedPositron Emission Tomography Computed TomographyWhole Body ImagingAdultAgedFemaleHumansMaleMiddle AgedRadiopharmaceuticalsRetrospective StudiesRadiopharmaceuticalsDeep learningDual-tracerImage synthesisRadiation exposureRegistrationTotal-body PET/CT

Identifiers

PMID39932542

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.