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Texture analysis of myocardial infarction in CT: Comparison with visual analysis and impact of iterative reconstruction.
- Source :
-
European journal of radiology [Eur J Radiol] 2019 Apr; Vol. 113, pp. 245-250. Date of Electronic Publication: 2019 Feb 27. - Publication Year :
- 2019
-
Abstract
- Objectives: To compare texture analysis (TA) with subjective visual diagnosis of myocardial infarction (MI) in cardiac computed tomography (CT) and to evaluate the impact of iterative reconstruction (IR).<br />Methods: Ten patients (4 women, mean age 68 ± 11 years) with confirmed chronic MI and 20 controls (8 women, mean age 52 ± 11 years) with no cardiac abnormality underwent contrast-enhanced cardiac CT with the same protocol. Images were reconstructed with filtered back projection (FBP) and with advanced modeled IR at strength levels 3-5. Subjective diagnosis of MI was made by three independent, blinded readers with different experience levels. Classification of MI was performed using machine learning-based decision tree models for the entire data set and after splitting into training and test data to avoid overfitting.<br />Results: Subjective visual analysis for diagnosis of MI showed excellent intrareader (kappa: 0.93) but poor interreader agreement (kappa: 0.3), with variable performance at different image reconstructions. TA showed high performance for all image reconstructions (correct classifications: 94%-97%, areas under the curve: 0.94-0.99). After splitting into training and test data, overall lower performances were observed, with best results for IR at level 5 (correct classifications: 73%, area under the curve: 0.65).<br />Conclusions: As compared with subjective, nonreliable visual analysis of inexperienced readers, TA enables objective and reproducible diagnosis of chronic MI in cardiac CT with higher accuracy. IR has a considerable impact on both subjective and objective image analysis.<br /> (Copyright © 2019 Elsevier B.V. All rights reserved.)
- Subjects :
- Aged
Algorithms
Case-Control Studies
Female
Heart diagnostic imaging
Humans
Image Processing, Computer-Assisted
Machine Learning
Male
Middle Aged
Myocardial Infarction pathology
Radiation Dosage
Radiographic Image Interpretation, Computer-Assisted methods
Radionuclide Imaging
Tomography, X-Ray Computed methods
Myocardial Infarction diagnostic imaging
Subjects
Details
- Language :
- English
- ISSN :
- 1872-7727
- Volume :
- 113
- Database :
- MEDLINE
- Journal :
- European journal of radiology
- Publication Type :
- Academic Journal
- Accession number :
- 30927955
- Full Text :
- https://doi.org/10.1016/j.ejrad.2019.02.037