A Bayesian approach for characterization of soft tissue viscoelasticity in acoustic radiation force imaging

Abstract

Biomechanical imaging techniques based on acoustic radiation force (ARF) have been developed to characterize the viscoelasticity of soft tissue by measuring the motion excited by ARF non-invasively. The unknown stress distribution in the region of excitation limits an accurate inverse characterization of soft tissue viscoelasticity, and single degree-of-freedom simplified models have been applied to solve the inverse problem approximately. In this study, the ARF-induced creep imaging is employed to estimate the time constant of a Voigt viscoelastic tissue model, and an inverse finite element (FE) characterization procedure based on a Bayesian formulation is presented. The Bayesian approach aims to estimate a reasonable quantification of the probability distributions of soft tissue mechanical properties in the presence of measurement noise and model parameter uncertainty. Gaussian process metamodeling is applied to provide a fast statistical approximation based on a small number of computationally expensive FE model runs. Numerical simulation results demonstrate that the Bayesian approach provides an efficient and practical estimation of the probability distributions of time constant in the ARF-induced creep imaging. In a comparison study with the single degree of freedom models, the Bayesian approach with FE models improves the estimation results even in the presence of large uncertainty levels of the model parameters.

A 2D finite element model for shear wave propagation in biological soft tissues: Application to magnetic resonance elastography

“…This approach is of interest because it provides information on the tissue properties while accounting for boundary conditions and other factors influencing shear wave propagation . The development of numerical models is also useful for the reconstruction process of the mechanical properties in MRE, for instance by identification process . Because of the viscoelastic nature of soft tissues, both the elasticity and the viscosity play a major role in the shear wave properties .…”

“…[12][13][14] The development of numerical models is also useful for the reconstruction process of the mechanical properties in MRE, for instance by identification process. [15][16][17][18][19][20][21] Because of the viscoelastic nature of soft tissues, both the elasticity and the viscosity play a major role in the shear wave properties. 22 It is therefore essential to properly implement the influence of viscosity and heterogeneity of the medium in such numerical tools.…”

Bayesian inference to identify parameters in viscoelasticity

“…Hernandez et al (2015) employed the Bayesian approach for the probabilistic identification of five viscoelastic parameters. Zhao and Pelegri (2016) used BI together with a finite element model to identify the time constant of a Voigt-based tissue model. All mentioned studies (except Zhao and Pelegri 2016) are based on actual experimental data.…”

“…Zhao and Pelegri (2016) used BI together with a finite element model to identify the time constant of a Voigt-based tissue model. All mentioned studies (except Zhao and Pelegri 2016) are based on actual experimental data. Kenz et al (2013) compared asymptotic theory, bootstrapping, and Bayesian estimation for a viscoelastic wave propagation model.…”

“…Furthermore, none of them have investigated the influence of the number of measurement data. Except for Zhao and Pelegri (2016) none of the studies for viscoelasticity are able to compare the resulting values with the input values. Finally, none of the aforementioned studies have compared the estimated parameter values for different types of tests (e.g.…”