Evidence map›Paper›PMID 39500749›Full record

ArticleCardiovascular and interventional radiology2025

In-Stent Restenosis in Peripheral Arterial Disease: Ultra-High-Resolution Photon-Counting Versus Third-Generation Dual-Source Energy-Integrating Detector CT Phantom Study in Seven Different Stent Types.

Theresa-Marie Dachs, Sven R Hauck, Maximilian Kern, Catharina Klausenitz, Maximilian Hoffner, Melanie Schernthaner, Hanaa Abdel-Rahman, Albert Hannover, Andreas Strassl, Irene Steiner and 2 more

Abstract readComparative Study
In one paragraph

Article in Cardiovascular and interventional radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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

12 authors.

Theresa-Marie DachsDepartment of Bio-Medical Imaging and Image-Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Sven R HauckDepartment of Bio-Medical Imaging and Image-Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Maximilian KernDepartment of Bio-Medical Imaging and Image-Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Catharina KlausenitzDepartment of Bio-Medical Imaging and Image-Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Maximilian HoffnerDepartment of Bio-Medical Imaging and Image-Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Melanie SchernthanerDepartment of Bio-Medical Imaging and Image-Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Hanaa Abdel-RahmanDepartment of Bio-Medical Imaging and Image-Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Albert HannoverDepartment of Bio-Medical Imaging and Image-Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Andreas StrasslDepartment of Bio-Medical Imaging and Image-Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Irene SteinerCenter for Medical Data Science, Institute of Medical Statistics, Medical University of Vienna, Vienna, Austria.
Christian LoeweDepartment of Bio-Medical Imaging and Image-Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Martin A FunovicsDepartment of Bio-Medical Imaging and Image-Guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria. martin.funovics@meduniwien.ac.at.ORCID http://orcid.org/0000-0002-7300-1288

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe visualization of peripheral in-stent restenosis using energy-integrating detector CT is challenging due to deficient spatial resolution and artifact formation. This study compares the first clinically available photon-counting detector CT to third-generation dual-source energy-integrating detector CT. MATERIALS AND

methodsNylon cylinders with central bores (4 mm, 2 mm), mimicking 75% and 95% stenoses, were placed inside seven different 8-mm diameter stents and filled with diluted contrast medium. Phantoms were scanned with photon-counting detector CT at slice thicknesses of 0.2 mm (available only in this scanner type), 0.5 mm, and 1.0 mm versus 0.5 mm and 1.0 mm in energy-integrating detector CT at matched CT dose indices. Contrast-to-noise ratios were calculated from attenuation rates. Residual lumen size was measured as full width at half-maximum. Subjective image quality was assessed by two independent blinded raters.

resultsMean contrast-to-noise ratio was lowest in photon-counting detector CT at 0.2 mm slice thickness (0%, 75%, and 95% in-stent restenosis: 6.11 ± 0.6, 5.27 ± 0.54, and 5.02 ± 0.66) and highest at 1.0 mm slice thicknesses with similar measurements in photon-counting detector CT and energy-integrating detector CT (11.46 ± 1.08, 9.94 ± 1.01, 8.26 ± 1.0 vs. 3.34 ± 1.0, 9.92 ± 0.38, 7.94 ± 1.07). Mean full width at half-maximum measurements in photon-counting detector CT at 0.2 mm slice thickness for 0%, 75%, and 95% in-stent restenosis were 8.00 ± 0.37, 3.98 ± 0.34, and 1.92 ± 0.16 mm. Full width at half-maximum was least precise in 95% in-stent restenosis at 1.0 mm slice thickness with similar measurements between scanners (1.57 ± 0.33 vs. 1.71 ± 0.15 mm). Interrater correlation coefficient was 0.75 [95% CI: [0.53; 0.86]; subjective scores were best at 0.2 mm slice thickness in photon-counting detector CT (19.43 ± 0.51 and 19.00 ± 0.68).

conclusionIn phantom in-stent restenosis in 8 mm stents, we observed similar full width at half-maximum for photon-counting detector CT and energy-integrating detector CT in 0% and 75% in-stent restenosis, but at 95% in-stent restenosis, FWHM tended to be more accurate in smaller slice thicknesses in both scanners. Subjective image assessment yielded best results at 0.2 mm slice thickness in photon-counting detector CT despite lower contrast-to-noise ratio.

Indexed as

Peripheral Arterial DiseasePhantoms, ImagingStentsComputed Tomography AngiographyContrast MediaHumansPhotonsTomography, X-Ray ComputedContrast MediaEnergy-integrating detector CTIn-stent restenosisPhantom studyUltra-high-resolution photon-counting detector CT

Identifiers

PMID39500749
PMCPMC11706893

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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.