Evidence map›Paper›PMID 42667384›Full record

ArticleRadiologie (Heidelberg, Germany)2026

Feasibility of reducing radiation dose and contrast dose while improving image quality in dual-energy CT pulmonary angiography : Use of low-energy virtual monochromatic images with deep learning image reconstruction algorithm.

Zhanli Ren, Tianli Wang, Li Shen, Min Zhang, Dong Han, Nan Yu, Zhanliang Ren

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Article in Radiologie (Heidelberg, Germany), 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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5 · Who and what money

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7 authors.

Zhanli RenAffiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang western road-2#, 712000, Xianyang, Shaanxi, China.
Tianli WangShaanxi Fashion Engineering University, No.1 of Tongwen Road, Fengxi New City of Xixian New Area, 712046, Xi'an, Shaanxi, China.
Li ShenAffiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang western road-2#, 712000, Xianyang, Shaanxi, China.
Min ZhangAffiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang western road-2#, 712000, Xianyang, Shaanxi, China.
Dong HanAffiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang western road-2#, 712000, Xianyang, Shaanxi, China.
Nan YuAffiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang western road-2#, 712000, Xianyang, Shaanxi, China.
Zhanliang RenAffiliated Hospital of Shaanxi University of Chinese Medicine, Weiyang western road-2#, 712000, Xianyang, Shaanxi, China. renzhanliang@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe explored the feasibility of reducing both radiation and contrast doses while improving image quality using low-energy virtual monochromatic images (VMIs) in dual-energy computed tomography (CT) pulmonary angiography (DECTPA) with deep learning image reconstruction at a high setting (DLIR-H). MATERIALS AND

methodsA total of 60 patients scheduled for CTPA were randomly divided into two groups: group A (n = 30) with 120 kV, contrast dose of 0.8 mL/kg, and adaptive statistical iterative reconstruction‑V (ASIR-V) at a 60% strength level, and group B (n = 30) with dual-energy CT (DECT), contrast dose of 0.6 mL/kg, and DLIR‑H at 40 keV, 50 keV, 60 keV and 70 keV VMIs. The CT and standard deviation (SD) values of the pulmonary arteries were measured to calculate the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image quality was subjectively scored blindly by two radiologists using a five-point scale.

resultsThere were no differences in general patient demographics between the two groups (all p > 0.05). In group B, the contrast and radiation doses decreased by 29.6% and 53.1%, respectively, compared to group A (p < 0.05). Compared to group A, group B had significantly higher CT and SNR values for the 40 keV-60 keV VMIs and higher CNR for the 40 keV-70 keV VMIs (all p < 0.05), with similar image noise at 60 keV. The two radiologists had substantial agreement in subjective scoring (kappa > 0.80). There were significantly higher scores for the 40 keV-60 keV VMIs in group B compared to group A (all p < 0.05), with the 40 keV VMIs having the highest scores.

conclusionCombining DLIR‑H with VMIs, especially 40 keV, in DECTPA leads to reduced contrast and radiation doses while improving image quality.

Indexed as

Computed tomographyDeep learningImage reconstructionPulmonary angiographyVirtual monochromatic imaging

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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 Socratic graph.