Evidence map›Paper›PMID 41409389›Full record

ArticleJournal of clinical imaging science2025

Comparison of image quality in carotid dual-energy computed tomography angiography at 55 keV virtual monoenergetic imaging using deep learning and adaptive iterative reconstruction algorithm.

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical imaging science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Xiaohan LiuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Chong WangDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Juan LongDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Yang WuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Zhongxiao LiuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Meng YuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Chenzi WangDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Wenbei XuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
He ZhangDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Aiyun SunCT Imaging Research Center, GE HealthCare China, Shanghai, China.
Shuai ZhangCT Imaging Research Center, GE HealthCare China, Shanghai, China.
Kai XuDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Yankai MengDepartment of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study aims to evaluate the image quality of 55 keV virtual monoenergetic imaging (VMI) in carotid dual-energy computed tomography (CT) angiography (DE-CTA) reconstructed using deep learning image reconstruction (DLIR) algorithms and traditional iterative reconstruction algorithms. Material and Methods: This prospective study included 48 patients who underwent DE-CTA examinations at our institution between December 2024 and January 2025. Image reconstructions were performed using 50% strength adaptive statistical iterative reconstruction-Veo (ASIR-V 50%), low and high strengths DLIR (DLIR-L and DLIR-H) algorithms. Objective image quality was evaluated by measuring background noise (standard deviation), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at key anatomical locations, including the aortic arch, common carotid artery, carotid bifurcation, and internal carotid artery. Two senior radiologists conducted subjective assessments of image quality, focusing on image noise, artifacts, and vessel continuity, and the clarity of vascular wall margin. Results: Compared with ASIR-V 50% and DLIR-L, DLIR-H significantly improved image quality by reducing background noise and increasing SNR and CNR ( Conclusion: At 55 keV VMI in carotid DE-CTA, DLIR-H significantly enhanced image quality, particularly by reducing noise and preserving fine anatomical structures. Its efficacy was especially notable in patients with BMI ≥24 kg/m

Indexed as

Carotid computed tomography angiographyDeep learningDual-energy computed tomographyImage qualityIterative reconstruction

Identifiers

PMID41409389
PMCPMC12707594

What Socratic holds

Textmetadata
LicenceCC BY-NC-SA
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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 Socratic graph.