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Updated: Mar 23, 2026

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
Published on: January 24, 2016
Preliminary investigation of multiparametric strain Z-score (MPZS) computation using displacement encoding with
Julia Kar1, Brian Cupps2, Xiaodong Zhong2
1Department of Surgery, School of Medicine, Washington University, St. Louis, Missouri, USA. karj@wudosis.wustl.edu.
Purpose:
To describe and assess an automated normalization method for identifying sentinel (septal) regions of myocardial dysfunction in nonischemic, nonvalvular dilated cardiomyopathy (DCM), using an unprecedented combination of the navigator-gated 3D spiral displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI), radial point interpolation (RPIM) and multiparametric strain z-score (MPZS).
Materials And Methods:
Navigator-gated 3D spiral DENSE, in a 1.5T MRI machine, was used for acquiring the displacement encoded complex images, MR Analytical Software System (MASS) for automated boundary detection and automated meshfree RPIM for left-ventricular (LV) myocardial strain computation to analyze MPZS in 36 subjects (with n = 17 DCM patients). Pearson's r correlation established relations between global/sentinel MPZS and ejection fraction (EF). The time taken for combined RPIM-MPZS computations was recorded.
Results:
Maximum MPZS differences were seen between anteroseptal and posterolateral regions in the base (2.0 ± 0.3 vs. 0.9 ± 0.5) and the mid-wall (2.1 ± 0.4 vs. 1.0 ± 0.4). These regional differences were found to be consistent with historically documented septal injury in nonischemic DCM. Correlations were 0.6 between global MPZS and EF, and 0.7 between sentinel MPZS and EF. The time taken for combined RPIM-MPZS computations per subject was 18.9 ± 5.9 seconds.
Conclusion:
Heterogeneous contractility found in the sentinel regions with the current automated MPZS computation scheme and the correlation found between MPZS and EF may lead to the creation of a new clinical metric in LV DCM surveillance. J. MAGN. RESON. IMAGING 2016;44:993-1002.
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