Introduction of a Novel Image-Based and Non-Invasive Method for the Estimation of Local Elastic Properties of Great Vessels
Background: In the context of a growing demand for the use of in silico models to meet clinical requests, image-based methods play a crucial role. In this study, we present a parametric equation able to estimate the elasticity of vessel walls, non-invasively and indirectly, from information uniquely...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/13/2055 |
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| author | Benigno Marco Fanni Alessandra Pizzuto Giuseppe Santoro Simona Celi |
| author_facet | Benigno Marco Fanni Alessandra Pizzuto Giuseppe Santoro Simona Celi |
| author_sort | Benigno Marco Fanni |
| collection | DOAJ |
| description | Background: In the context of a growing demand for the use of in silico models to meet clinical requests, image-based methods play a crucial role. In this study, we present a parametric equation able to estimate the elasticity of vessel walls, non-invasively and indirectly, from information uniquely retrievable from imaging. Methods: A custom equation was iteratively refined and tuned from the simulations of a wide range of different vessel models, leading to the definition of an indirect method able to estimate the elastic modulus <i>E</i> of a vessel wall. To test the effectiveness of the predictive capability to infer the <i>E</i> value, two models with increasing complexity were used: a U-shaped vessel and a patient-specific aorta. Results: The original formulation was demonstrated to deviate from the ground truth, with a difference of 89.6%. However, the adoption of our proposed equation was found to significantly increase the reliability of the estimated E value for a vessel wall, with a mean percentage error of 9.3% with respect to the reference values. Conclusion: This study provides a strong basis for the definition of a method able to estimate local mechanical information of vessels from data easily retrievable from imaging, thus potentially increasing the reliability of in silico cardiovascular models. |
| first_indexed | 2024-03-09T21:59:14Z |
| format | Article |
| id | doaj.art-0969e3eb63364d1dbaad77127be33f48 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T21:59:14Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-0969e3eb63364d1dbaad77127be33f482023-11-23T19:52:08ZengMDPI AGElectronics2079-92922022-06-011113205510.3390/electronics11132055Introduction of a Novel Image-Based and Non-Invasive Method for the Estimation of Local Elastic Properties of Great VesselsBenigno Marco Fanni0Alessandra Pizzuto1Giuseppe Santoro2Simona Celi3BioCardioLab, Bioengineering Unit, Fondazione Toscana “G. Monasterio”, 54100 Massa, ItalyPediatric Cardiology Unit, Fondazione Toscana “G. Monasterio”, 54100 Massa, ItalyPediatric Cardiology Unit, Fondazione Toscana “G. Monasterio”, 54100 Massa, ItalyBioCardioLab, Bioengineering Unit, Fondazione Toscana “G. Monasterio”, 54100 Massa, ItalyBackground: In the context of a growing demand for the use of in silico models to meet clinical requests, image-based methods play a crucial role. In this study, we present a parametric equation able to estimate the elasticity of vessel walls, non-invasively and indirectly, from information uniquely retrievable from imaging. Methods: A custom equation was iteratively refined and tuned from the simulations of a wide range of different vessel models, leading to the definition of an indirect method able to estimate the elastic modulus <i>E</i> of a vessel wall. To test the effectiveness of the predictive capability to infer the <i>E</i> value, two models with increasing complexity were used: a U-shaped vessel and a patient-specific aorta. Results: The original formulation was demonstrated to deviate from the ground truth, with a difference of 89.6%. However, the adoption of our proposed equation was found to significantly increase the reliability of the estimated E value for a vessel wall, with a mean percentage error of 9.3% with respect to the reference values. Conclusion: This study provides a strong basis for the definition of a method able to estimate local mechanical information of vessels from data easily retrievable from imaging, thus potentially increasing the reliability of in silico cardiovascular models.https://www.mdpi.com/2079-9292/11/13/2055vascular modelingimagingfluid-structure interactioncomputational fluid dynamicsnumerical modelsmechanical properties |
| spellingShingle | Benigno Marco Fanni Alessandra Pizzuto Giuseppe Santoro Simona Celi Introduction of a Novel Image-Based and Non-Invasive Method for the Estimation of Local Elastic Properties of Great Vessels Electronics vascular modeling imaging fluid-structure interaction computational fluid dynamics numerical models mechanical properties |
| title | Introduction of a Novel Image-Based and Non-Invasive Method for the Estimation of Local Elastic Properties of Great Vessels |
| title_full | Introduction of a Novel Image-Based and Non-Invasive Method for the Estimation of Local Elastic Properties of Great Vessels |
| title_fullStr | Introduction of a Novel Image-Based and Non-Invasive Method for the Estimation of Local Elastic Properties of Great Vessels |
| title_full_unstemmed | Introduction of a Novel Image-Based and Non-Invasive Method for the Estimation of Local Elastic Properties of Great Vessels |
| title_short | Introduction of a Novel Image-Based and Non-Invasive Method for the Estimation of Local Elastic Properties of Great Vessels |
| title_sort | introduction of a novel image based and non invasive method for the estimation of local elastic properties of great vessels |
| topic | vascular modeling imaging fluid-structure interaction computational fluid dynamics numerical models mechanical properties |
| url | https://www.mdpi.com/2079-9292/11/13/2055 |
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