Evidence map›Paper›PMID 41619102›Full record

ArticleEJNMMI physics2026

Scanner-integrated reconstruction versus post-processing deep learning for low-count

Qigang Long, Yan Tian, Yun Hu, Zhenchun Xu, Wenqian Zhang, Shanshan Xu, Wei Liu, Jingzheng Jin, Yunsong Peng

Abstract read
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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.

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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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3 · Its place in the literature

Who cites it

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

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.
Yun Hu *School of Medical Information Engineering, Zunyi Medical University, Zunyi, 563000, China.
Zhenchun XuDepartment of Nuclear Medicine, Chongqing Jiulongpo People's Hospital, Chongqing, 400000, China.
Wenqian ZhangDepartment of Nuclear Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
Shanshan XuSchool of Medical Information Engineering, Zunyi Medical University, Zunyi, 563000, China.
Wei LiuDepartment of Oncology, First Affiliated Hospital of Army Medical University, Chongqing, 400038, PR China.
Jingzheng JinDepartment of Gastroenterology, General Hospital of Tianjin Medical University, Tianjin, 300041, China. jjz13132153359@163.com.
Yunsong PengGuizhou Province International Science and Technology Cooperation Base for Precision Imaging Diagnosis and Treatment, Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou Province, China. 13554308327@163.com.

Funding

Guizhou Provincial People's Hospital Talent Fund [2022]-5the Youth Science and Technology Talent Support Program of Guizhou Provincial Association for Science and Technology the Youth Science and Technology Talent Support Program of Guizhou Provincial Association for Science and Technology
6 · The paper itself

Abstract

objectivesTo compare two deep learning (DL) approaches for low-count PET/CT: deep progressive reconstruction (DPR), a scanner-integrated reconstruction-level method, and a deep-learning image-domain post-processing enhancement (POST; RaDynPET).

methodsSixty-seven patients who underwent whole-body

resultsBoth DPR and POST achieved higher reader scores than time-matched OSEM. Inter-reader agreement was substantial to almost perfect. POST was superior at 30 s, whereas DPR was at 60 s. D60 and P30 met both NI margins, whereas D30 failed overall quality and P60 failed CNR. Concordance with O120 was excellent by CCC, and Bland-Altman showed small biases with limited proportional effects. CNR and SNR increased monotonically with DPR, while POST yielded gains at 30 s that attenuated at 60 s. TBR improvements were confined to DPR.

conclusionBoth DPR and POST improved or preserved image quality while enabling scan-time reduction, with excellent agreement with the clinical reference. POST is supported for 1/4 acquisition time, whereas DPR is favored from 1/2 time onward.

Indexed as

18F-FDG PET/CTDeep learningImage qualityImage reconstructionLow-count

Identifiers

PMID41619102
PMCPMC12953836

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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.