Robust Phase Velocity Dispersion Estimation of Viscoelastic Materials Used for Medical Applications Based on the Multiple Signal Classification Method

Ultrasound shear wave elastography (SWE) is emerging as a promising imaging modality for the noninvasive evaluation of tissue mechanical properties. One of the ways to explore the viscoelasticity is through analyzing the shear wave velocity dispersion curves. To explore the dispersion, it is necessary to estimate the shear wave velocity at each frequency. An increase of the available spectrum to be used for phase velocity estimation is significant for a tissue dispersion analysis in vivo. A number of available methods suffer because the available spectrum that one can work with is limited. We present an alternative method to the classical 2-D Fourier transform (2D-FT) that uses the multiple signal classification (MUSIC) technique to provide robust estimation of the $k$ -space and phase velocity dispersion curves. We compared results from the MUSIC method with the 2D-FT technique twofold: by searching for maximum peaks and gradient-based strategy. We tested this method on digital phantom data created using finite-element methods (FEMs) in viscoelastic media as well as on the experimental phantoms used in the Radiological Society of North America Quantitative Imaging Biomarker Alliance effort for the standardization of shear wave velocity in liver fibrosis applications. In addition, we evaluated the algorithm with different levels of added noise for FEMs. The MUSIC algorithm provided dispersion curves estimation with lower errors than the conventional 2D-FT method. The MUSIC method can be used for the robust evaluation of shear wave velocity dispersion curves in viscoelastic media.