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Updated: May 16, 2026
3D Whole-heart Myocardial Tissue Analysis
Published on: April 12, 2017
Three-dimensional regional strain analysis in porcine myocardial infarction: a 3T magnetic resonance tagging study
Sahar Soleimanifard1, Khaled Z Abd-Elmoniem, Tetsuo Sasano
1Department of Electrical and Computer Engineering, Johns Hopkins University, 3400 N, Charles Street, Clark Hall 201, Baltimore, MD 21218, USA.
Background:
Previous studies of mechanical strain anomalies in myocardial infarction (MI) have been largely limited to analysis of one-dimensional (1D) and two-dimensional (2D) strain parameters. Advances in cardiovascular magnetic resonance (CMR) methods now permit a complete three-dimensional (3D) interrogation of myocardial regional strain. The aim of this study was to investigate the incremental value of CMR-based 3D strain and to test the hypothesis that 3D strain is superior to 1D or 2D strain analysis in the assessment of viability using a porcine model of infarction.
Methods:
Infarction was induced surgically in 20 farm pigs. Cine, late gadolinium enhancement, and CMR tagging images were acquired at 11 days before (baseline), and 11 days (early) and 1 month (late) after induction of infarct. Harmonic phase analysis was performed to measure circumferential, longitudinal, and radial strains in myocardial segments, which were defined based on the transmurality of delayed enhancement. Univariate, bivariate, and multivariate logistic regression models of strain parameters were created and analyzed to compare the overall diagnostic accuracy of 3D strain analysis with 1D and 2D analyses in identifying the infarct and its adjacent regions from healthy myocardium.
Results:
3D strain differed significantly in infarct, adjacent, and remote segments (p<0.05) at early and late post-MI. In univariate, bivariate, and multivariate analyses, circumferential, longitudinal, and radial strains were significant factors (p<0.001) in differentiation of infarct and adjacent segments from baseline values. In identification of adjacent segments, receiver operating characteristic analysis using the 3D strain multivariate model demonstrated a significant improvement (p<0.01) in overall diagnostic accuracy in comparison with 2D (circumferential and radial) and 1D (circumferential) models (3D: 96%, 2D: 81%, and 1D: 71%). A similar trend was observed in identification of infarct segments.
Conclusions:
Cumulative 3D strain information accurately identifies infarcts and their neighboring regions from healthy myocardium. The 3D interrogation of myocardial contractility provides incremental diagnostic accuracy in delineating the dysfunctional and nonviable myocardium in comparison with 1D or 2D quantification of strain. The infarct neighboring regions are the major beneficiaries of the 3D assessment of regional strain.
