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Three-Dimensional Analysis of Strain

Three-Dimensional Analysis of Strain

Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...

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Updated: Feb 28, 2026

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

Longitudinal Strain by Artificial Intelligence-Driven Automated Strain Analysis for Left Ventricular Function

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.

Journal of Clinical Ultrasound : JCU

|February 26, 2026

Summary

This study introduces an AI framework for accurate left ventricular (LV) dysfunction assessment in acute myocardial infarction (AMI). The AI model standardizes strain analysis, correlates with biomarkers, and localizes infarcts for better clinical decisions.

Keywords:

ST elevation acute myocardial infarction automated strain cardiac troponin T speckle‐tracking echocardiography

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Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation

Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation

Published on: October 20, 2016

Area of Science:

Cardiology
Medical Imaging
Artificial Intelligence

Background:

Echocardiography for acute myocardial infarction (AMI) faces challenges in accurately assessing left ventricular (LV) dysfunction and infarcts due to subjective analysis.
Standardized, quantitative methods are needed to overcome variability in conventional echocardiographic assessments.

Purpose of the Study:

To validate an artificial intelligence (AI)-driven automated strain framework for standardized quantification of myocardial deformation in AMI.
To assess the correlation of AI-derived strain parameters with clinical biomarkers of myocardial injury.

Main Methods:

A retrospective analysis of 102 AMI patients and 90 controls using 2D speckle-tracking echocardiography.
A ResNet-18 AI model was employed for global/regional longitudinal strain (LPSS) quantification and infarct localization via dual-task learning.
Training involved two-phase optimization with real-time data augmentation; statistical analyses correlated strain with LV ejection fraction (LVEF), cardiac troponin T (cTnT), and ST-segment elevation.

Main Results:

AI-derived global LPSS strongly correlated with LVEF (r = -0.609, p < 0.001), outperforming the wall motion score index.
Infarct-zone LPSS showed the strongest correlation with cTnT (r = 0.671, p < 0.001) and ST elevation (r = 0.321, p = 0.001).
Reproducibility was confirmed by Bland-Altman analysis (intra-observer bias: 0.7% ± 1.2%; interobserver: 1.1% ± 3.1%).

Conclusions:

AI-driven strain analysis offers a standardized approach to LV functional assessment in AMI.
The AI framework provides quantitative correlations with enzymatic and electrophysiological markers of injury.
Infarct localization and detection of compensatory mechanisms by AI support clinical decision-making in AMI management.