Geometric regularization for 2-D myocardial strain quantification in mice: an in-silico study

Abstract

Myocardial strain quantification in the mouse based on 2-D speckle tracking using real-time ultrasound datasets is feasible but remains challenging. The major difficulty lies in the fact that the frame rate-to-heart rate ratio is relatively low, causing significant decorrelation between subsequent frames. In this setting, regularization is therefore particularly important to discard motion estimates that are improbable. Different regularization methods have been proposed, among which is a class of regularizers based on enforcing preset geometrical characteristics of the motion field. To date, these regularization methods have not been contrasted. The aim of this study was thus to compare the performance of different geometric regularizers in the setting of myocardial motion and strain estimation in murine echocardiography using simulated datasets. In normal models, restricting the spatial curvature of the motion fields resulted in worse radial strain estimates (mean root-mean-square [RMS] error increased from 0.06 to 0.09; p < 0.05), but better circumferential strain estimates (mean RMS error decreased from 0.035 to 0.01; p < 0.05). More accurate circumferential strain estimates were also obtained by convolving a Gaussian function with the lateral motion components (mean RMS error decreased to 0.015; p < 0.05). In infarcted models, no significant differences were found between regularized and nonregularized radial strains. However, for circumferential strain, the curvature method yielded better strain estimates in all regions (mean RMS error decreased from 0.043 to 0.015; p < 0.05), whereas the Gaussian method only improved strain assessment in the remote myocardium (mean RMS error decreased to 0.021; p < 0.05).