Practical settings for shear wave speed estimation using the framework of Reverberant Shear Wave Elastography: A numerical simulation study

Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov:2021:3877-3881. doi: 10.1109/EMBC46164.2021.9630470.

Abstract

Reverberant shear wave elastography (RSWE) has become a promising approach to quantifying soft tissues' viscoelastic properties by the propagating shear wave speed (SWS) estimation based on the particle velocity autocorrelation. In this work, three different practical settings were evaluated for the SWS estimation by numerical simulations of an isotropic, homogenous, and elastic medium: first, the 2D representation of the particle velocity, second, the spatial autocorrelation computation, and third, the selection of the curve fitting domain. We conclude that the 2D autocorrelation function using the Wiener-Khinchin theorem provides up to 127 times faster results than traditional autocorrelation methods. Additionally, we state that extracting the magnitude and phase from the Fourier transform of the temporal domain, applying the 2D-autocorrelation on a mobile square window sized at least two wavelengths, and fitting the monotonically decreasing part of the autocorrelation profile's central lobe results in more accurate (13.2% of bias) and precise (5.3% of CV) estimations than other practical settings.Clinical relevance- Affections in soft tissues' biomechanical properties are related to pathologies, such as tumor cancer, muscular degenerative diseases, or fibrosis. These changes are quantified by the SWS and its derived viscoelastic parameters. RSWE is a promising approach for their characterization. In this work, we evaluated alternative elections of practical settings within the methodology. Numerical simulations indicate they lead to faster and more reliable local SWS estimations than conventional settings.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computer Simulation
  • Elasticity Imaging Techniques*
  • Fourier Analysis
  • Insular Cortex
  • Phantoms, Imaging