Evidence map›Paper›PMID 40924047›Full record

ArticleJapanese journal of radiology2026

Comparison of DLIR and ASIR-V algorithms for virtual monoenergetic imaging in carotid CTA under a triple-low protocol.

Juan Long, Chenzi Wang, Meng Yu, Xiaohan Liu, Wenbei Xu, Zhongxiao Liu, Chong Wang, Yang Wu, Aiyun Sun, Shuai Zhang and 3 more

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Japanese journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

13 authors.

Juan Long *Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Chenzi Wang *Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Meng Yu *Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Xiaohan LiuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Wenbei XuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Zhongxiao LiuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Chong WangDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Yang WuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Aiyun SunCT Imaging Research Center, GE HealthCare China, Shanghai, China.
Shuai ZhangCT Imaging Research Center, GE HealthCare China, Shanghai, China.
Chunfeng HuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Kai XuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Yankai MengDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China. mengyankai@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStroke, frequently associated with carotid artery disease, is evaluated using carotid computed tomography angiography (CTA). Dual-energy CTA (DE-CTA) enhances imaging quality but presents challenges in maintaining high image clarity with low-dose scans.

objectivesTo compare the image quality of 50 keV virtual monoenergetic images (VMI) generated using Deep Learning Image Reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-V (ASIR-V) algorithms under a triple-low scanning protocol in carotid CTA.

methodsA prospective study was conducted with 120 patients undergoing DE-CTA. The control group (Group 1), with a noise index (NI) of 4.0 and a contrast agent dose of 0.5 mL/kg, used the ASIR-V algorithm. The experimental group was divided into four subgroups: Group 2 (ASIR-V 50%), Group 3 (DLIR-L), Group 4 (DLIR-M), and Group 5 (DLIR-H), with a higher NI of 13.0 and a reduced contrast agent dose of 0.4 mL/kg. Objective image quality was assessed through signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and standard deviation (SD), while subjective quality was evaluated using a 5-point Likert scale. Radiation dose and contrast agent volume were also measured.

resultsThe triple-low scanning protocol reduced radiation exposure by 53.2%, contrast agent volume by 19.7%, and injection rate by 19.8%. The DLIR-H setting outperformed ASIR-V, demonstrating superior image quality, better noise suppression, and improved contrast in small vessels. VMI at 50 keV showed enhanced diagnostic clarity with minimal radiation and contrast agent usage.

conclusionThe DLIR algorithm, particularly at high settings, significantly enhances image quality in DE-CTA VMI under a triple-low scanning protocol, offering a better balance between radiation dose reduction and image clarity.

Indexed as

AlgorithmsCarotid ArteriesCarotid Artery DiseasesComputed Tomography AngiographyDeep LearningRadiographic Image Interpretation, Computer-AssistedAgedAged, 80 and overContrast MediaFemaleHumansMaleMiddle AgedProspective StudiesRadiation DosageSignal-To-Noise RatioContrast MediaCarotid arteryDeep learning image reconstructionDual-energy CTImage qualityTriple-low scanVirtual monochromatic images

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Registered trials

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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.