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Quantitative assessment of myocardial scar heterogeneity using cardiovascular magnetic resonance texture analysis to risk stratify patients post-myocardial infarction

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T. Gibbs, A.D.M. Villa, E. Sammut, S. Jeyabraba, G. Carr-White, T.F. Ismail, G. Mullen, B. Ganeshan, A. Chiribiri

Original languageEnglish
JournalClinical Radiology
Early online date27 Sep 2018
Publication statusPublished - 2018


King's Authors


Aim To determine whether heterogeneity of cardiac scar, as assessed by cardiovascular magnetic resonance (CMR) texture analysis, may provide insight into better risk stratification for patients with previous myocardial infarction (MI). Materials and methods Patients with previous MI (n=76) were followed for a median of 371.5 days after late gadolinium enhancement (LGE) CMR. The primary endpoint was a composite of ventricular tachycardia, ventricular fibrillation, or unexplained syncope. Areas of LGE were identified and manually segmented on a short-axis projection. The characteristics of the scar heterogeneity were evaluated via CMR texture analysis. This is a filtration-histogram technique, where images are filtered using the Laplacian of a Gaussian filter to extract features different sizes (2–6 mm in radius) corresponding to fine, medium, and coarse texture scales followed by a quantification step using histogram analysis (skewness and kurtosis). Results Patients suffering arrhythmic events during the follow-up period demonstrated significantly higher kurtosis (coarse-scale, p=0.005) and lower skewness (fine-scale, p=0.046) compared to those suffering no arrhythmic events. Furthermore, Kaplan–Meier analysis showed significantly higher coarse kurtosis (p=0.004), and lower fine skewness (p=0.035) were able to predict increased incidence of ventricular arrhythmic events. Conclusions In this pilot study, indices of texture analysis reflecting textural heterogeneity were significantly associated with a greater incidence of arrhythmic events. Further work is required to delineate the role of texture analysis techniques in risk stratification post-MI.

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