Evidence map›Paper›PMID 40603771›Full record

ArticleEuropean radiology2026

Clinical value of the 70-kVp ultra-low-dose CT pulmonary angiography with deep learning image reconstruction.

Yicun Zhang, Luotong Wang, Dian Yuan, Ke Qi, Mengyuan Zhang, Weiting Zhang, Jianbo Gao, Jie Liu

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Article in European radiology, 2026. 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

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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

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3 citing papers in PubMed.

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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

8 authors.

Yicun ZhangDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Luotong WangCT Imaging Research Center, GE HealthCare China, Beijing, China.
Dian YuanDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Ke QiDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Mengyuan ZhangDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Weiting ZhangDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jianbo GaoDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jie LiuDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. liujieict@163.com.ORCID http://orcid.org/0000-0001-6504-0589

Funding

the Science and Technology Research Project of Henan Provincial Health Commission No.212102310142
6 · The paper itself

Abstract

objectiveThis study aims to assess the feasibility of "double-low," low radiation dosage and low contrast media dosage, CT pulmonary angiography (CTPA) based on deep-learning image reconstruction (DLIR) algorithms. MATERIALS AND

methodsOne hundred consecutive patients (41 females; average age 60.9 years, range 18-90) were prospectively scanned on multi-detector CT systems. Fifty patients in the conventional-dose group (CD group) underwent CTPA with 100 kV protocol using the traditional iterative reconstruction algorithm, and 50 patients in the low-dose group (LD group) underwent CTPA with a 70 kVp DLIR protocol. Radiation and contrast agent doses were recorded and compared between groups. Objective parameters were measured and compared. Two radiologists evaluated images for overall image quality, artifacts, and image contrast separately on a 5-point scale. The furthest visible branches were compared between groups.

resultsCompared to the control group, the study group reduced the dose-length product by 80.3% (p < 0.01) and the contrast media dose by 33.3%. CT values, SD values, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) showed no statistically significant differences (all p > 0.05) between the LD and CD groups. The overall image quality scores were comparable between the LD and CD groups (p > 0.05), with good in-reader agreement (k = 0.75). More peripheral pulmonary vessels could be assessed in the LD group compared with the CD group.

conclusion70 kVp combined with DLIR reconstruction for CTPA can further reduce radiation and contrast agent dose while maintaining image quality and increasing the visibility on the pulmonary artery distal branches. KEY POINTS: Question Elevated radiation exposure and substantial doses of contrast media during CT pulmonary angiography (CTPA) augment patient risks. Findings The "double-low" CT pulmonary angiography protocol can diminish radiation doses by 80.3% and minimize contrast doses by one-third while maintaining image quality. Clinical relevance With deep learning algorithms, we confirmed that CTPA images maintained excellent quality despite reduced radiation and contrast dosages, helping to reduce radiation exposure and kidney burden on patients. The "double-low" CTPA protocol, complemented by deep learning image reconstruction, prioritizes patient safety.

Indexed as

Computed Tomography AngiographyDeep LearningPulmonary ArteryPulmonary EmbolismRadiation DosageRadiographic Image Interpretation, Computer-AssistedAdolescentAdultAgedAged, 80 and overAlgorithmsContrast MediaFeasibility StudiesFemaleHumansMaleContrast MediaContrast mediaDeep learningDose reductionPulmonary arteryRadiation dosage

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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.