Related Experiment Video
Updated: Dec 3, 2025

Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography
Published on: May 10, 2022
A digital viscoelastic liver phantom for investigation of elastographic measurements
Pezhman Pasyar1, Sadegh Masjoodi2, Zahra Montazeriani1
1Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.
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.

