Key result
Elastic image registration significantly improved the correlation of radial strain estimation compared to block-matching (0.83 vs 0.63; p=0.05) with smaller bias and limits of agreement (p<0.01).
Absolute Event Rate: 0.83% vs 0.63%
p-value: p=0.05
Elastic image registration with embedded regularization provides superior radial strain estimation compared to traditional block-matching speckle tracking in an in-vivo sheep model.
Elastic registration may refine radial strain estimates in preclinical models; leaves open human validation before clinical consideration.
Despite the availability of multiple solutions for assessing myocardial strain, little is currently known about the relative performance of the different methods. In this study, we sought to contrast two popular strain estimation techniques directly (block-matching and registration) in an in-vivo setting by comparing both to a gold standard reference measurement. Mid short-axis images were acquired with a GE Vivid7 ultrasound system in five open-chest sheep instrumented with ultrasonic microcrystals. Radial (∈ RR ) and circumferential strain (∈ CC ) were estimated during four stages: at rest, during esmolol and dobutamine infusion, and during acute ischemia. While the correlations of a well-validated block matching approach and the elastic registration method were comparable for the ∈ CC component (0.82 vs 0.81 respectively), imposing regularization during the motion estimation process (i.e. as embedded in the elastic registration method), showed to be advantageous to estimate ∈ RR as it improved performance significantly (0.63 vs 0.83; p=0.05). Moreover, the bias and the limits of agreement were also smaller in this direction (p<;0.01).
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Heyde et al. (2011) studied Myocardial strain (n=5). Elastic image registration vs. Block-matching was evaluated on Correlation of radial strain (ERR) estimation with gold standard (p=0.05). Elastic image registration significantly improved the correlation of radial strain estimation compared to block-matching (0.83 vs 0.63; p=0.05) with smaller bias and limits of agreement (p<0.01).
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