Texture analysis of myocardial infarction in CT: Comparison with visual analysis and impact of iterative reconstruction

Eur J Radiol. 2019 Apr;113:245-250. doi: 10.1016/j.ejrad.2019.02.037. Epub 2019 Feb 27.

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).

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.

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).

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.

Keywords: Computed tomography; Iterative reconstruction; Machine learning; Myocardial infarction; Texture analysis.

Publication types

  • Comparative Study
  • Evaluation Study

MeSH terms

  • Aged
  • Algorithms
  • Case-Control Studies
  • Female
  • Heart / diagnostic imaging
  • Humans
  • Image Processing, Computer-Assisted
  • Machine Learning
  • Male
  • Middle Aged
  • Myocardial Infarction / diagnostic imaging*
  • Myocardial Infarction / pathology
  • Radiation Dosage
  • Radiographic Image Interpretation, Computer-Assisted / methods
  • Radionuclide Imaging
  • Tomography, X-Ray Computed / methods