Literature DB >> 24148775

Coronary artery stent imaging with CT using an integrated electronics detector and iterative reconstructions: first in vitro experience.

Jochen von Spiczak1, Fabian Morsbach, Sebastian Winklhofer, Thomas Frauenfelder, Sebastian Leschka, Thomas Flohr, David Maintz, Harald Seifarth, Alexander C Bunck, Paul Stolzmann, Hatem Alkadhi.   

Abstract

BACKGROUND: Despite continuous improvements in CT technology, accurate stent lumen delineation remains challenging.
PURPOSE: The aim was to evaluate the quality of coronary stent lumen delineation with CT using a detector with integrated electronics.
METHODS: Twelve coronary stents placed in plastic tubes and filled with contrast agent (CT number 250 HU) were imaged with either a 128-section dual-source CT machine equipped with conventional detector or with integrated electronics. On both scanners, images were reconstructed with filtered back projection (slice thickness 0.6 mm; increment 0.4 mm) and sinogram-affirmed iterative reconstruction (slice thickness 0.6 mm; increment 0.4 mm), and with iterative reconstruction (slice thickness 0.5 mm; increment 0.3 mm) on the integrated scanner. Two blinded, independent readers assessed image quality, noise, in-stent diameter, in-stent attenuation, and image sharpness by using signal intensity profiles across stents.
RESULTS: Interreader agreement for image quality assessment was substantial (κ = 0.798). Both readers rated best image quality in data sets from integrated detector at highest spatial resolution (86 or 72% of stents rated best quality). Image noise was significantly lower in data sets scanned with integrated detector, being lowest at 0.6 mm slice thickness (14.3 vs 21.0 HU; P < .001). Differences between measured and true in-stent diameters and differences in attenuation across stents were smallest, and average/maximum image sharpness was highest in data sets from the integrated detector using iterative reconstructions.
CONCLUSION: CT coronary stent imaging is significantly improved by using a detector with integrated electronics combined with iterative reconstructions.
Copyright © 2013 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computed tomography; Coronary artery; Detector; Iterative reconstruction; Stent

Mesh:

Year:  2013        PMID: 24148775     DOI: 10.1016/j.jcct.2013.08.003

Source DB:  PubMed          Journal:  J Cardiovasc Comput Tomogr        ISSN: 1876-861X


  5 in total

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Authors:  Stefanie Mangold; Paola M Cannaó; U Joseph Schoepf; Julian L Wichmann; Christian Canstein; Stephen R Fuller; Giuseppe Muscogiuri; Akos Varga-Szemes; Konstantin Nikolaou; Carlo N De Cecco
Journal:  Eur Radiol       Date:  2015-09-15       Impact factor: 5.315

2.  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.

Authors:  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; Thomas M Todoran; Julian L Wichmann
Journal:  Eur Radiol       Date:  2016-03-09       Impact factor: 5.315

3.  Cardiac CT: Technological Advances in Hardware, Software, and Machine Learning Applications.

Authors:  Frederic Commandeur; Markus Goeller; Damini Dey
Journal:  Curr Cardiovasc Imaging Rep       Date:  2018-06-29

4.  Image quality and radiation dose of lower extremity CT angiography using 70 kVp, high pitch acquisition and sinogram-affirmed iterative reconstruction.

Authors:  Li Qi; Felix G Meinel; Chang Sheng Zhou; Yan E Zhao; U Joseph Schoepf; Long Jiang Zhang; Guang Ming Lu
Journal:  PLoS One       Date:  2014-06-10       Impact factor: 3.240

5.  1024-pixel image matrix for chest CT - Impact on image quality of bronchial structures in phantoms and patients.

Authors:  André Euler; Katharina Martini; Bettina Baessler; Matthias Eberhard; Friederike Schoeck; Hatem Alkadhi; Thomas Frauenfelder
Journal:  PLoS One       Date:  2020-06-16       Impact factor: 3.240

  5 in total

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