Premium
A Bayesian approach for characterization of soft tissue viscoelasticity in acoustic radiation force imaging
Author(s) -
Zhao Xiaodong,
Pelegri Assimina A.
Publication year - 2016
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.2741
Subject(s) - viscoelasticity , uncertainty quantification , inverse problem , finite element method , bayesian probability , posterior probability , degrees of freedom (physics and chemistry) , probability distribution , bayesian inference , gaussian process , bayesian linear regression , computer science , mathematics , algorithm , gaussian , physics , mathematical analysis , artificial intelligence , statistics , machine learning , quantum mechanics , thermodynamics
Summary 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. Copyright © 2015 John Wiley & Sons, Ltd.