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Measurement of Viscoelastic Material Model Parameters Using Fractional Derivative Group Shear Wave Speeds in Simulation and Phantom Data.
Article em En | MEDLINE | ID: mdl-31562083
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 the tissue. This article presents a method to use group shear wave speeds (gSWSs) at a series of derivative orders to characterize viscoelastic materials. This method uses a least squares fitting algorithm to match the experimental data to the calculated gSWS 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 the cases, the group speed method produced more repeatable characterization overall than fitting the phase velocity results from the peak of the 2-D Fourier transform. Results suggest that the linear attenuation model is a better fit than the Voigt model for the viscoelastic QIBA phantoms.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imagens de Fantasmas / Elasticidade / Técnicas de Imagem por Elasticidade Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imagens de Fantasmas / Elasticidade / Técnicas de Imagem por Elasticidade Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article