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Parameter identification for ultrasound shear wave elastography simulation
Citation Link: https://doi.org/10.15480/882.3812
Publikationstyp
Journal Article
Date Issued
2021-08-01
Sprache
English
TORE-DOI
Volume
7
Issue
1
Start Page
35
End Page
38
Article Number
20211108
Citation
Current Directions in Biomedical Engineering 7 (1): 20211108 (2021-08-01)
Publisher DOI
Scopus ID
Publisher
De Gruyter
Elasticity of soft tissue is a valuable information to physicians in treatment and diagnosis of diseases. The elastic properties of tissue can be estimated with ultrasound (US) shear wave imaging (SWEI). In US-SWEI, a force push is applied inside the tissue and the resulting shear wave is detected by high-frequency imaging. The properties of the wave such as the shear wave velocity can be mapped to tissue elasticity. Commonly, wave features are extracted by tracking the peak of the shear wave, estimating the phase velocity or with machine learning methods. To tune and test these methods, often simulation data is employed since material properties and excitation can be accurately controlled. Subsequent validation on real US-SWEI data is in many cases performed on tissue phantoms such as gelatine. Clearly, validation performance of these procedures is dependent on the accuracy of the simulated tissue phantom and a thorough comparison of simulation and experimental data is needed. In this work, we estimate wave parameters from 400 US-SWEI data sets acquired in various homogeneous gelatine phantoms. We tune a linear material model to these parameters. We report an absolute percentage error for the shear wave velocity between simulation and phantom experiment of <2.5%. We validate our material model on unknown gelatine concentrations and estimate the shear wave velocity with an error <3.4% for in-range concentrations indicating that our material model is in good agreement with US-SWEI measurements.
Subjects
Abaqus
High-Frequency US Imaging
Shear Wave Elastography
Shear Wave Simulation
Ultrasound
DDC Class
600: Technik
610: Medizin
Funding Organisations
More Funding Information
This work was partially funded by the TUHH i3 initiative.
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