A simulation study to compare Shear Wave Speed estimators of the Reverberant Shear Wave Elastography approach

Abstract

Reverberant shear wave elastography is quantitative elastography approach that involves the generation of a reverberant field that propagates in all directions within the medium. Based on the generation of the field, the information about tissues stiffness can be obtained by measuring the shear wave speed (SWS). This approach has showed encouraging results for characterization of tissues such as the experiments already carried out in vivo. The aim of this study is to evaluate the quality of SWS estimators for acoustic reverberant fields in heterogeneous media to achieve this purpose, three estimators were tested: Curve fitting, Phase Gradient and a Convolutional Neural Network. Parameters such as time of processing, accuracy, resolution and contrast between background and inclusion were used to determine the best estimator. The results show the inclusion zone a maximum accuracy of 94.75% can be obtained with the deep learning estimator; while, in the background, the maximum accuracy was obtained with the phase gradient estimator and was 96.85%. Even though all three estimators show good results, the execution time, lateral resolution, contrast and CNR have better results with the deep learning estimator.