Evidence map›Paper›PMID 33936965›Full record

ArticleQuantitative imaging in medicine and surgery2021

Feasibility of low-dose contrast media in run-off CT angiography on dual-layer spectral detector CT.

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

Open access · diamondAbstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
0.8field-weighted citation impact, top 31% of its field
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

14 citing papers in PubMed, 16 citations in OpenAlex.

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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 at 1 institution in 1 country.

Haiyan RenDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
Yanhua ZhenDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
Zheng GongDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
Chuanzhuo WangDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
Zhihui ChangDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
Jiahe ZhengDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aim of the present study was to assess the feasibility of applying low-dose contrast media (CM), and to explore the optimal virtual monoenergetic images (VMIs) in run-off computed tomography (CT) angiography (CTA) on dual-layer spectral detector CT (SDCT).

methodsForty patients were randomly assigned into a control group using routine volume CM (group A) and an experimental group using half-volume CM (group B). In groups A and B, 120 kVp polychromatic conventional images were generated via hybrid iterative reconstruction algorithm defined as A1 and B1, respectively. Additionally, in group B, VMIs (range, 40-120 keV) were reconstructed via a spectral reconstruction algorithm defined as B2-B10. Vascular attenuation, noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and radiation dose were evaluated. Subjective evaluation was performed using a 5-point scale.

resultsThe patient demographics and radiation dose demonstrated no significant difference between groups A and B [dose length product (DLP): 1,823.45±512.68

conclusionsIt is feasible to perform run-off CTA using low-dose CM with VMI on SDCT. The VMIs at 40-50 keV were the optimal choice and did not compromise IQ.

Indexed as

contrast media (CM)lower extremitySpectral detector computed tomographyvirtual monoenergetic images (VMIs)

Identifiers

PMID33936965
PMCPMC8047342
OpenAlexW3134003185

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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.