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dc.contributor.author
Hinzpeter, Ricarda
dc.contributor.author
Wagner, Matthias W.
dc.contributor.author
Wurnig, Moritz C.
dc.contributor.author
Seifert, Burkhardt
dc.contributor.author
Manka, Robert
dc.contributor.author
Alkadhi, Hatem
dc.date.accessioned
2017-12-22T12:31:20Z
dc.date.available
2017-11-22T08:59:10Z
dc.date.available
2017-12-22T12:31:20Z
dc.date.issued
2017
dc.identifier.issn
1932-6203
dc.identifier.other
10.1371/journal.pone.0186876
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/211953
dc.identifier.doi
10.3929/ethz-b-000211953
dc.description.abstract
Objective To investigate the feasibility and accuracy of texture analysis to distinguish through objective and quantitative image information between healthy and infarcted myocardium with computed tomography (CT). Materials and methods Twenty patients (5 females; mean age 56±10years) with proven acute myocardial infarction (MI) and 20 patients (8 females; mean age 42±15years) with no cardiac abnormalities (hereafter termed controls) underwent contrast-enhanced cardiac CT. Short axis CT images of the left ventricle (LV) were reconstructed at the slice thicknesses 1mm, 2mm, and 5mm. Two independent, blinded readers segmented the LV in controls and patients. Texture analysis was performed yielding first-level features based on the histogram (variance, skewness, kurtosis, entropy), second-level features based on the gray-level co-occurrence matrix (GLCM) (contrast, correlation, energy and homogeneity), and third-level features based on the gray-level run-length matrix (GLRLM). Results Inter-and intrareader agreement was good to excellent for all histogram (intraclass correlation coefficient (ICC):0.70–0.93) and for all GLCM features (ICC:0.66–0.99), and was variable for the GLRLM features (ICC:-0.12–0.99). Univariate analysis showed significant differences between patients and controls for 2/4 histogram features, 3/4 GLCM and for 6/11 GLRLM features and all assessed slice thicknesses (all,p<0.05). In a multivariate logistic regression model, the single best variable from each level, determined by ROC analysis, was included stepwise. The best model included kurtosis (OR 0.08, 95%CI:0.01–0.65,P = 0.018) and short run high gray-level emphasis (SRHGE, OR 0.97, 95%CI:0.94–0.99,P = 0.007), with an area-under-the-curve (AUC) of 0.90 (95%CI:0.80–0.99). The best results for kurtosis and SRHGE (AUC = 0.78) were obtained at a 5mm slice thickness. A cut-off value of 14.4 for kurtosis+0.013*SRHGE predicted acute MI with a sensitivity of 95% (specificity 55%). Conclusion Our study illustrates the feasibility of texture analysis for distinguishing healthy from acutely infarcted myocardium with cardiac CT using objective, quantitative features, with most reproducible and accurate results at a short axis slice thickness of 5mm.
en_US
dc.format
application/pdf
dc.language.iso
en
en_US
dc.publisher
PLOS
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Texture analysis of acute myocardial infarction with CT: First experience study
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2017-11-02
ethz.journal.title
PLoS ONE
ethz.journal.volume
12
en_US
ethz.journal.issue
11
en_US
ethz.journal.abbreviated
PLoS ONE
ethz.pages.start
e0186876
en_US
ethz.size
16 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
San Francisco, CA
ethz.publication.status
published
en_US
ethz.date.deposited
2017-11-22T08:59:18Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-12-22T12:31:24Z
ethz.rosetta.lastUpdated
2024-02-02T03:35:51Z
ethz.rosetta.versionExported
true
ethz.COinS
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