Texture-based Probability Mapping in cardiac LGE Images: A novel automatic assessment of myocardial injury following revascularized STEMI

Background Scar size is critical to left ventricular (LV) remodeling and adverse outcomes following myocardial infarction (MI). Late Gadolinium-enhancement (LGE) in cardiac magnetic resonance imaging is the gold standard for assessing MI size. Texture-based probability mapping (TPM) is a novel machine learning-based analysis of LGE images. This proof-of-concept study investigates the potential clinical implications of temporal changes in TPM during the first year following an acute revascularized MI. Methods 41 patients with first-time acute ST-elevation MI were included in this study. All patients had a single-vessel disease and were successfully revascularized by primary percutaneous coronary intervention. LGE images were obtained two days, one week, two months, and one year post-MI. MI size by TPM was compared with manual LGE-based MI calculation, LV remodeling, and biomarkers. Results TPM showed a significant increase in infarct size from the second month through the first year (p<0.01). MI size estimated by TPM at all different time points demonstrated strong correlations with peak Troponin T levels. At one week, TPM assessment correlated positively with maximum C-reactive protein (r=0.54, p<0.01), and at two months, TPM positively correlated with N-Terminal Pro Brain Natriuretic Peptide. Conclusion This proof-of-concept study suggests that TPM may provide additional information to conventional LGE-based MI analysis of scar formation, LV remodeling, and biomarkers following an acute revascularized MI. Highlights Texture-based probability mapping (TPM) was used to analyze consecutive cardiac magnetic resonance images acquired during the first year after ST-elevation myocardial infarction (STEMI). TPM size was related to biomarkers of inflammation, myocardial injury and stress. TPM is a step toward automatic image processing.