Evidence map›Paper›PMID 33369991›Full record

ArticleJournal of computer assisted tomography

Assessment of Virtual Monoenergetic Images in Run-off Computed Tomography Angiography: A Comparison Study to Conventional Images From Spectral Detector Computed Tomography.

Haiyan Ren, Yanhua Zhen, Zheng Gong, Chuanzhuo Wang, Zhihui Chang, Jiahe Zheng

Abstract readComparative Study
In one paragraph

Article in Journal of computer assisted tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Evaluation of ECG-Gated, High-Pitch Thoracoabdominal Angiographies With Dual-Source Photon-Counting Detector Computed Tomography.Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists · 2025
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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

6 authors.

Haiyan RenFrom the Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
Yanhua Zhen
Zheng Gong
Chuanzhuo Wang
Zhihui Chang
Jiahe Zheng

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe aims of this study were to evaluate image quality of virtual monoenergetic images (VMIs) compared with conventional images (CIs) from spectral detector CT (SDCT) and to explore the optimal energy level in run-off computed tomography angiography (CTA).

methodsThe data sets of 35 patients who received run-off CTA on the SDCT were collected in this retrospective study. Conventional images were generated via iterative reconstruction algorithm and VMI series from 40 to 120 keV were generated via spectral reconstruction algorithm. The objective indices including vascular attenuation, noise, signal-to-noise ratio, and contrast-to-noise ratio were compared. Two readers performed subjective evaluation using a 5-point scale.

resultsThe attenuation showed higher values compared with CIs at 40 to 60 keV (P < 0.001). The noise was similar in 60- to 80-keV VMIs and significantly decreased in 90- to 120-keV VMIs (P < 0.001) in comparison with CIs. The signal-to-noise ratio and contrast-to-noise ratio were improved in 40- to 60-keV VMIs compared with CIs (P < 0.05). The score of subjective assessment was higher than that of CIs in 50- to 70-keV VMIs (P < 0.001).

conclusionsVirtual monoenergetic images can provide improved image quality compared with CIs from SDCT in run-off CTA, and VMIs at 60 keV may be the best choice in evaluating lower extremity arteries.

Indexed as

AgedAged, 80 and overAlgorithmsComputed Tomography AngiographyFemaleFemoral ArteryHumansLower ExtremityMaleMiddle AgedRetrospective StudiesSignal-To-Noise Ratio

Identifiers

PMID33369991
PMCPMC7972299

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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.