Evidence map›Paper›PMID 40423253›Full record

ArticleTomography (Ann Arbor, Mich.)2025

Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study.

Haoyan Li, Zhenpeng Chen, Shuaiyi Gao, Jiaqi Hu, Zhihao Yang, Yun Peng, Jihang Sun

Abstract readEvaluation Study
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 2025. 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
–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

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

7 authors.

Haoyan LiDepartment of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China.ORCID 0000-0002-4820-5473
Zhenpeng ChenQingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China.ORCID 0000-0002-5130-435X
Shuaiyi GaoDepartment of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China.
Jiaqi HuDepartment of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China.
Zhihao YangDepartment of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China.
Yun PengDepartment of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China.
Jihang SunDepartment of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing 100045, China.

Funding

Beijing Municipal Administration of Hospitals Incubating Program PX2022050Natural Science Foundation of Xinjiang Uygur Autonomous Region. 2022D01A306Xinjiang Hospital of Beijing Children' s Hospital science program 2022005
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Deep LearningImage Processing, Computer-AssistedRadiographic Image Interpretation, Computer-AssistedRadiography, Dual-Energy Scanned ProjectionTomography, X-Ray ComputedAlgorithmsHumansPhantoms, ImagingRadiation Dosagedeep learningdual-energy CTimage enhancementimage reconstructionmulti-slice CTU-Net model

Identifiers

PMID40423253
PMCPMC12116077

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.