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Updated: Feb 28, 2026
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
Published on: February 12, 2011
Tensión Longitudinal por Análisis Automatizado de Tensión Impulsado por Inteligencia Artificial para la Evaluación de
Liping Guo1, Jia Wen1, Lang Qin1
1Department of Medical Ultrasound, Liuzhou Worker's Hospital, the Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, People's Republic of China.
Purpose:
Accurate evaluation of left ventricular (LV) dysfunction and infarct localization in acute myocardial infarction (AMI) remains challenging due to subjective variability in conventional echocardiographic analysis. This study validates an artificial intelligence (AI)-driven automated strain framework for standardized quantification of myocardial deformation and its correlation with clinical biomarkers.
Methods:
A retrospective cohort of 102 first-onset ST-elevation AMI patients and 90 age-/sex-matched controls underwent 2D speckle-tracking echocardiography. A modified ResNet-18 architecture processed standardized apical views (112 × 112 pixels, 25-frame cycles) through dual-task learning: global/regional longitudinal strain (LPSS) quantification and infarct localization. Training employed a two-phase optimization-myocardial tracking followed by strain regression and infarct classification. Real-time augmentation included speckle noise and cardiac-phase variations. Statistical analyses assessed correlations between strain parameters, LV ejection fraction (LVEF), cardiac troponin T (cTnT), and ST-segment elevation.
Results:
AI-derived global LPSS strongly correlated with LVEF (r = -0.609; p < 0.001), outperforming the wall motion score index (r = 0.291). Infarct-zone LPSS demonstrated the strongest associations with cTnT (r = 0.671; p < 0.001) and ST elevation (r = 0.321; p = 0.001). Remote myocardium exhibited compensatory hyperkinesis (LPSS = -17.93%). Bland-Altman analysis confirmed reproducibility (intra-observer bias: 0.7% ± 1.2%; interobserver: 1.1% ± 3.1%).
Conclusions:
AI-driven strain analysis standardized LV functional assessment in AMI, providing quantitative correlations with enzymatic and electrophysiological injury markers. Its ability to localize infarcts and detect compensatory mechanisms supports clinical decision-making, bridging gaps between echocardiography and advanced imaging.
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