Evidence map›Paper›PMID 41254528›Full record

ArticleBMC medical imaging2025

Impact of contrast enhancement boost and super-resolution deep learning reconstruction on pediatric congenital heart disease CTA scans: ultra-low contrast dose.

Xinyan Zhou, Junyi Li, Tengfei Ke, Daoqiang Xiong, Fei Liu, Na Tan, Xirui Duan, Xiaolan Du, Feifei Zhou, Wan Shen and 5 more

Abstract read
In one paragraph

Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 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

15 authors.

Xinyan Zhou *Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
Junyi Li *The First Affiliated Hospital of Kunming Medical University, Kunming, China.
Tengfei KeDepartment of Radiology, Yunnan Cancer Hospital (The Third Affiliated Hospital of Kunming Medical University), Kunming, China.
Daoqiang XiongDepartment of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
Fei LiuDepartment of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
Na TanDepartment of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
Xirui DuanDepartment of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
Xiaolan DuDepartment of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
Feifei ZhouDepartment of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
Wan ShenDepartment of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
Rong QianDepartment of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
Guochen LiDepartment of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
Guangrong ZhengDepartment of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
Lei TangSchool of Media and Design Arts, Yunnan University of Finance and Economics, Kunming, China. 515028730@qq.com.
Chengde LiaoDepartment of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China. chengdeliao@qq.com.

Funding

Key Laboratory of Cardiovascular Disease of Yunnan Province Project 2018DG008Yunnan Provincial Department of Education Scientific Research Fund Project 2024J0261Yunnan Talents Support Program XDYC-MY-2022-0064
6 · The paper itself

Abstract

objectiveTo evaluate the feasibility of using contrast enhancement boost (CE-Boost) combined with super-resolution deep learning reconstruction (SR-DLR) to reduce contrast agent dosage in pediatric patients with congenital heart disease (CHD).

methodsA total of 72 pediatric CHD patients were divided into the low-contrast-dose group (CE-Boost group, injection of 0.5 mL per kilogram of body weight, n = 36) or the standard-scan group (CE-CT group, 1.5 mL per kilogram of body weight, n = 36), both groups undergoing imaging with SR-DLR. The two imaging protocols were compared based on radiation dose, objective image quality, subjective evaluation, and diagnostic accuracy. To quantitatively evaluate image quality, CT attenuation (HU) and standard deviation (SD) values were measured within ROIs at the four-chamber plane for cardiac chambers; an anonymized dataset was assessed using a double-blind methodology by two independent readers blinded to the clinical information and prior diagnoses of the pediatric patients.

resultsCE-Boost combined with SR-DLR significantly reduced contrast agent usage (62.3% reduction compared to CE-CT, P < 0.001) while maintaining image quality comparable to the conventional contrast protocol (P > 0.05). There was no significant difference in radiation dose parameters, including dose-length product (DLP, mGy·cm) and volume-weighted CT dose index (CTDIvol, mGy) (all P > 0.1), while the effective dose (ED) in the CE-Boost group was slightly lower but not significant (0.36 vs. 0.43 mSv, P = 0.078). Additionally, the CE-Boost group's image quality metrics (CT values, SNR, CNR) remained stable, with no significant difference in subjective scores (P = 0.660).

conclusionCE-Boost combined with SR-DLR enables a significant reduction in contrast agent usage in pediatric CHD imaging while maintaining comparable image quality to conventional contrast protocols and optimizing SNR and CNR. This approach ensures diagnostic readability while minimizing contrast exposure, highlighting its feasibility and clinical value in pediatric CHD imaging.

Indexed as

Computed Tomography AngiographyContrast MediaDeep LearningHeart Defects, CongenitalAdolescentChildChild, PreschoolDouble-Blind MethodFeasibility StudiesFemaleHumansInfantMaleRadiation DosageRadiographic Image Interpretation, Computer-AssistedContrast Media

Identifiers

PMID41254528
PMCPMC12625121

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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.