Measurement of Viscoelastic Material Model Parameters Using Fractional Derivative Group Shear Wave Speeds in Simulation and Phantom Data

Abstract

While ultrasound shear wave elastography originally focused on tissue stiffness under the assumption of elasticity, recent work has investigated the higher order, viscoelastic properties of tissue. This paper presents a method to use group shear wave speeds at a series of derivative orders to characterize viscoelastic materials. This method uses a least squares fitting algorithm to match experimental data to calculated group shear wave speed data, using an assumed material model and excitation geometry matched to the experimental imaging configuration. Building on a previous study that used particle displacement, velocity, and acceleration signals, this study extends the analysis to a continuous range of fractional derivative orders between 0 and 2. The method can be applied to any material model. Herein, material characterization was performed for three different two-parameter models and three different three-parameter models. This group speed based method was applied to both shear wave simulations with ultrasonic tracking and experimental acquisitions in viscoelastic phantoms (similar to the Phase II Quantitative Imaging Biomarkers Alliance (QIBA) phantoms). In both cases, the group speed method produced more repeatable characterization overall than fitting the phase velocity results from the peak of the 2D Fourier transform. Results suggest the linear attenuation model is a better fit than the Voigt model for the viscoelastic QIBA phantoms.

Why Are Viscosity and Nonlinearity Bound to Make an Impact in Clinical Elastographic Diagnosis?

“…The viscoelasticity of soft tissues implies the search for high order models that characterize the dispersion associated with shear wave propagation. As input, some studies have used the shear wave group velocity, which approximates as a series of derivative orders [58,59]. Likewise, taking advantage of the acoustoelasticity phenomenon, wherein the shear wave velocity is altered when a stress is applied due to wave propagation, new parameters become measurable [60][61][62].…”

Ultrasound Shear Wave Elasticity Imaging With Spatio-Temporal Deep Learning

“…We present a deep learning approach for local elasticity estimation from real 3D ultrasound data. This task has been addressed with conventional methods by extracting the shear wave velocity as an explicit feature ( [10], [41]). In contrast, deep learning methods allow estimates without explicit feature extraction, intensive pre-processing and manual tuning.…”

Acoustic Radiation Force: A Review of Four Mechanisms for Biomedical Applications

“…Numerous other elastographic modalities using A-ARF were developed in the next decade. These modalities include but not limited to: SuperSonic Imaging [32], Harmonic Motion Imaging [33], crawling wave estimator [34], Shearwave Dispersion Ultrasound Vibrometry (SDUV) [35], Lamb wave dispersion ultrasound vibrometry (LDUV) [36], attenuation measuring ultrasound shear wave elastography (AMUSE) [37], Acoustic Radiation Force Induced Creep-Recovery [38], Local Phase Velocity Based Imaging [39], Viscoelastic Response (VisR) Imaging [40], Shear Wave Spectroscopy for quantification of tissue viscoelasticity [41], fractional derivative group shear estimation [42], reverberant shear wave fields for estimation of shear wave speed [43], twopoint frequency shift for shear wave attenuation measurement [44], and spatially-modulated ultrasound radiation force (STL-SWE/SMURF) [45]. A detailed description of the clinical applications of selected technologies based on mechanism A-ARF can be found in [24], [12].…”