Home
Resource
Finite element characterization of soft tissue viscoelastic parameters in acoustic radiation force imaging within the region of excitation
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(4.76 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Zhao, Xiaodong. Finite element characterization of soft tissue viscoelastic parameters in acoustic radiation force imaging within the region of excitation. Retrieved from https://doi.org/doi:10.7282/T37D2X44

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleFinite element characterization of soft tissue viscoelastic parameters in acoustic radiation force imaging within the region of excitation
NameZhao, Xiaodong (author); Pelegri, Assimina A. (chair); Cuitino, Alberto (internal member); WENG, GEORGE (internal member); LaMattina, Bruce (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2015
Other Date2015-10 (degree)
SubjectMechanical and Aerospace Engineering, Acoustic radiation force impulse imaging, Viscoelasticity
Extent1 online resource (xiv, 121 p. : ill.)
DescriptionBiomechanical imaging techniques based on acoustic radiation force (ARF) have been developed to characterize the viscoelasticity of soft tissue by measuring the motion induced with ARF noninvasively. The unknown stress distribution in the region of excitation (ROE) limits an accurate inverse characterization of soft tissue viscoelasticity. Thus, many assumptions have been made for both qualitative and quantitative ARF imaging within the ROE. In this thesis, the finite element method is applied to study the dynamic behavior of soft tissue in ARF imaging within the ROE to investigate the assumptions that are made in these imaging methods and decide what factors affect the accuracy of these assumptions. To this end, the effects of global boundary conditions and local heterogeneity are investigated, and simplified quantitative inversion model is evaluated for their limitations in describing soft tissue dynamic behavior. In order to improve quantitative estimation within the ROE, an inverse finite element (FE) characterization procedure based on a Bayesian formulation is presented, which takes full advantage of the prior information of the imaging system that are discarded in the simplified inversion models. The Bayesian approach formulates the known model parameters of the FE models as probability distributions, and aims to estimate a reasonable quantification of the probability distributions of soft tissue mechanical properties rather than best-fit values from an optimization procedure, which are not a practical or a comprehensive description of the estimation in the presence of measurement noise and model parameter uncertainty. To make the Bayesian approach computationally feasible, the Gaussian Process metamodeling method is applied as a statistical approximation of the complex FE model. A comprehensive numerical study in ARF induced creep imaging shows that the Bayesian approach with FE model improves the estimation results even in the presence of large uncertainty levels of the model parameters and provides a potential to improve the ROE-response-based imaging methods where the unknown stress limits an accurate inverse FE characterization.
NotePh.D.
NoteIncludes bibliographical references
Noteby Xiaodong Zhao
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T37D2X44
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024