Parameter Identification for Ultrasound Shear Wave Elastography Simulation
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 b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2021-08-01
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| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2021-1008 |
| _version_ | 1818826167920099328 |
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| author | Neidhardt M. Ohlsen J. Hoffmann N. Schlaefer A. |
| author_facet | Neidhardt M. Ohlsen J. Hoffmann N. Schlaefer A. |
| author_sort | Neidhardt M. |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T00:23:22Z |
| format | Article |
| id | doaj.art-5d14421770d04caa97aa82b87c8cb9c1 |
| institution | Directory Open Access Journal |
| issn | 2364-5504 |
| language | English |
| last_indexed | 2024-12-19T00:23:22Z |
| publishDate | 2021-08-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj.art-5d14421770d04caa97aa82b87c8cb9c12022-12-21T20:45:25ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042021-08-0171353810.1515/cdbme-2021-1008Parameter Identification for Ultrasound Shear Wave Elastography SimulationNeidhardt M.0Ohlsen J.1Hoffmann N.2Schlaefer A.3Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology,Hamburg, GermanyThe Dynamics Group, Hamburg University of Technology,Hamburg, GermanyThe Dynamics Group, Hamburg University of Technology,Hamburg, GermanyInstitute of Medical Technology and Intelligent Systems, Hamburg University of Technology,Hamburg, GermanyElasticity 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.https://doi.org/10.1515/cdbme-2021-1008ultrasoundshear wave elastographyshear wave simulationhigh-frequency us imagingabaqus |
| spellingShingle | Neidhardt M. Ohlsen J. Hoffmann N. Schlaefer A. Parameter Identification for Ultrasound Shear Wave Elastography Simulation Current Directions in Biomedical Engineering ultrasound shear wave elastography shear wave simulation high-frequency us imaging abaqus |
| title | Parameter Identification for Ultrasound Shear Wave Elastography Simulation |
| title_full | Parameter Identification for Ultrasound Shear Wave Elastography Simulation |
| title_fullStr | Parameter Identification for Ultrasound Shear Wave Elastography Simulation |
| title_full_unstemmed | Parameter Identification for Ultrasound Shear Wave Elastography Simulation |
| title_short | Parameter Identification for Ultrasound Shear Wave Elastography Simulation |
| title_sort | parameter identification for ultrasound shear wave elastography simulation |
| topic | ultrasound shear wave elastography shear wave simulation high-frequency us imaging abaqus |
| url | https://doi.org/10.1515/cdbme-2021-1008 |
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