Evidence map›Paper›PMID 26960541›Full record

ArticleEuropean radiology2016

A noise-optimized virtual monochromatic reconstruction algorithm improves stent visualization and diagnostic accuracy for detection of in-stent re-stenosis in lower extremity run-off CT angiography.

Stefanie Mangold, Carlo N De Cecco, U Joseph Schoepf, Ricardo T Yamada, Akos Varga-Szemes, Andrew C Stubenrauch, Damiano Caruso, Stephen R Fuller, Thomas J Vogl, Konstantin Nikolaou and 2 more

Abstract read
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Article in European radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.8field-weighted citation impact, top 10% 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

12 citing papers in PubMed, 26 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

12 authors at 3 institutions in 3 countries.

Stefanie MangoldDepartment of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Carlo N De CeccoDepartment of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
U Joseph SchoepfDepartment of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA. schoepf@musc.edu.
Ricardo T YamadaDepartment of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Akos Varga-SzemesDepartment of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Andrew C StubenrauchDepartment of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Damiano CarusoDepartment of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Stephen R FullerDepartment of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Thomas J VoglDepartment of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Konstantin NikolaouDepartment of Diagnostic and Interventional Radiology, Eberhard-Karls University Tuebingen, Tuebingen, Germany.
Thomas M TodoranDivision of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA.
Julian L WichmannDepartment of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Medical University of South Carolina · USGoethe University Frankfurt · DEUniversity of Tübingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the impact of noise-optimized virtual monochromatic imaging (VMI+) on stent visualization and accuracy for in-stent re-stenosis at lower extremity dual-energy CT angiography (DE-CTA). MATERIAL AND

methodsWe evaluated third-generation dual-source DE-CTA studies in 31 patients with prior stent placement. Images were reconstructed with linear blending (F_0.5) and VMI+ at 40-150 keV. In-stent luminal diameter was measured and contrast-to-noise ratio (CNR) calculated. Diagnostic confidence was determined using a five-point scale. In 21 patients with invasive catheter angiography, accuracy for significant re-stenosis (≥50 %) was assessed at F_0.5 and 80 keV-VMI+ chosen as the optimal energy level based on image-quality analysis.

resultsAt CTA, 45 stents were present. DSA was available for 28 stents whereas 12 stents showed significant re-stenosis. CNR was significantly higher with ≤80 keV-VMI+ (17.9 ± 6.4-33.7 ± 12.3) compared to F_0.5 (16.9 ± 4.8; all p < 0.0463); luminal stent diameters were increased at ≥70 keV (5.41 ± 1.8-5.92 ± 1.7 vs. 5.27 ± 1.8, all p < 0.001) and diagnostic confidence was highest at 70-80 keV-VMI+ (4.90 ± 0.48-4.88 ± 0.63 vs. 4.60 ± 0.66, p = 0.001, 0.0042). Sensitivity, negative predictive value and accuracy for re-stenosis were higher with 80 keV-VMI+ (100, 100, 96.4 %) than F_0.5 (90.9, 94.1, 89.3 %).

conclusion80 keV-VMI+ improves image quality, diagnostic confidence and accuracy for stent evaluation at lower extremity DE-CTA. KEY POINTS: • The impact of noise-optimized virtual monochromatic imaging on stent visualization was assessed. • Virtual monochromatic imaging significantly improves stent lumen visualization and diagnostic confidence. • At 80 keV diagnostic performance for detection of in-stent restenosis was increased. • 80 keV virtual monochromatic images are recommended for stent evaluation of lower extremity vasculature.

Indexed as

AlgorithmsStentsAdultAgedComputed Tomography AngiographyFemaleGraft Occlusion, VascularHumansIschemiaLower ExtremityMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySignal-To-Noise RatioCT angiographyDiagnostic accuracyDual-energyStent visualizationVirtual monochromatic imaging

Identifiers

PMID26960541
OpenAlexW2294311553

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Read underepoch 390

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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.