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A Digital Viscoelastic Liver Phantom for Investigation of Elastographic Measurements Publisher Pubmed



Pasyar P1 ; Masjoodi S2 ; Montazeriani Z1 ; Makkiabadi B1
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Authors Affiliations
  1. 1. Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran

Source: Computers in Biology and Medicine Published:2020


Abstract

To develop elastography imaging technologies and implement image reconstruction algorithms, testing is done with phantoms. Although the validation step is usually taken using real data and physical phantoms, their geometry as well as composition, biomechanical parameters, and details of applying stress cannot be modified readily. Such considerations have gained increasing importance with the growth of elastography techniques as one of the non-invasive medical imaging modalities, which can map the elastic properties and stiffness of soft tissues. In this article, we develop a digital viscoelastic phantom using computed tomography (CT) imaging data and several application software tools based on illustrations of normal liver anatomy so as to investigate the biomechanics of elastography via finite element modeling (FEM). Here we discuss how to create this phantom step by step, demonstrate typical shear wave elastography (SWE) experiments of applying transient stress to the liver model, and calculate quantitative measurements. In particular, shear wave velocities are investigated through a parametric study designed based on tissue stiffness and distance from the applied stress. According to the results of FEM analysis, low errors were obtained for shear wave velocity estimation for both mechanical stress (~2–5%) and acoustic radiation force (~3–7%). Results show that our model is a powerful framework and benchmark for simulating and implementing different algorithms in shear wave elastography, which can serve as a guide for upcoming researches and assist scientists to optimize their subsequent experiments in terms of design. © 2020 Elsevier Ltd
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