Singular Value Decomposition Applied to Cardiac Strain from MR Imaging for Selection of Optimal Cardiac...

To use singular value decomposition (SVD) in heart failure (HF) to reveal primary spatiotemporal strain patterns in the left ventricle (LV), then develop and test a time-independent metric of cardiac dyssynchrony on the basis of the circumferential uniformity ratio estimate (CURE) computed with SVD (CURE-SVD) in both a canine model of HF with or without left bundle branch block (LBBB) and a clinical cohort referred for cardiac resynchronization therapy (CRT).

The research was approved by the institutional review board and conformed with HIPAA requirements. All subjects provided informed consent. In both the canine model (n = 13) and the clinical cohort (80 CRT candidates; mean age, 65.2 years; range, 18.5-86.9 years), regional strains were derived by using cardiac magnetic resonance (MR) displacement encoding with stimulated echoes. CURE-SVD was compared with the standard CURE (averaged over systolic phases). Statistical methods included the Wilcoxon rank-sum test, Hodges-Lehmann estimator, Bland-Altman test, multivariable logistic regression, and receiver operating characteristic analysis.

In the canine model, the median difference in CURE-SVD (range, 0-1) for LBBB-HF group versus narrow-QRS-HF group (-0.40; 95% confidence interval [CI]: -0.79, -0.31) was similar to that for CURE (-0.43; 95% CI: -0.72, -0.34]). In 80 CRT candidates, CURE-SVD and CURE were highly correlated (r = 0.90; P < .0001). The multivariable model for CRT response with CURE-SVD demonstrated excellent performance without the need for time averaging over cardiac phases (area under the receiver operating characteristic curve = 0.96, P < .0001).

SVD of circumferential strain in HF identifies primary LV spatiotemporal contraction patterns with minimal user input, while the time-independent CURE-SVD parameter has excellent performance in a canine model of dyssynchrony and is strongly associated with CRT response in patients with HF.