Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls
Introduction: Patient-specific computational models are a powerful tool for planning cardiovascular interventions. However, the in vivo patient-specific mechanical properties of vessels represent a major source of uncertainty. In this study, we investigated the effect of uncertainty in the elastic m...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Journal of Cardiovascular Development and Disease |
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| Online Access: | https://www.mdpi.com/2308-3425/10/3/109 |
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| author | Benigno Marco Fanni Maria Nicole Antonuccio Alessandra Pizzuto Sergio Berti Giuseppe Santoro Simona Celi |
| author_facet | Benigno Marco Fanni Maria Nicole Antonuccio Alessandra Pizzuto Sergio Berti Giuseppe Santoro Simona Celi |
| author_sort | Benigno Marco Fanni |
| collection | DOAJ |
| description | Introduction: Patient-specific computational models are a powerful tool for planning cardiovascular interventions. However, the in vivo patient-specific mechanical properties of vessels represent a major source of uncertainty. In this study, we investigated the effect of uncertainty in the elastic module (<i>E</i>) on a Fluid–Structure Interaction (FSI) model of a patient-specific aorta. Methods: The image-based <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>χ</mi></semantics></math></inline-formula>-method was used to compute the initial <i>E</i> value of the vascular wall. The uncertainty quantification was carried out using the generalized Polynomial Chaos (gPC) expansion technique. The stochastic analysis was based on four deterministic simulations considering four quadrature points. A deviation of about ±20% on the estimation of the <i>E</i> value was assumed. Results: The influence of the uncertain <i>E</i> parameter was evaluated along the cardiac cycle on area and flow variations extracted from five cross-sections of the aortic FSI model. Results of stochastic analysis showed the impact of <i>E</i> in the ascending aorta while an insignificant effect was observed in the descending tract. Conclusions: This study demonstrated the importance of the image-based methodology for inferring <i>E</i>, highlighting the feasibility of retrieving useful additional data and enhancing the reliability of in silico models in clinical practice. |
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| institution | Directory Open Access Journal |
| issn | 2308-3425 |
| language | English |
| last_indexed | 2024-03-11T06:22:49Z |
| publishDate | 2023-03-01 |
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| series | Journal of Cardiovascular Development and Disease |
| spelling | doaj.art-2cdd0c1fdd3b4847a449a2561d7779042023-11-17T11:47:33ZengMDPI AGJournal of Cardiovascular Development and Disease2308-34252023-03-0110310910.3390/jcdd10030109Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular WallsBenigno Marco Fanni0Maria Nicole Antonuccio1Alessandra Pizzuto2Sergio Berti3Giuseppe Santoro4Simona Celi5BioCardioLab, Bioengineering Unit, Fondazione Toscana “G. Monasterio”, 54100 Massa, ItalyBioCardioLab, Bioengineering Unit, Fondazione Toscana “G. Monasterio”, 54100 Massa, ItalyPediatric Cardiology Unit, Fondazione Toscana “G. Monasterio”, 54100 Massa, ItalyAdult 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, ItalyIntroduction: Patient-specific computational models are a powerful tool for planning cardiovascular interventions. However, the in vivo patient-specific mechanical properties of vessels represent a major source of uncertainty. In this study, we investigated the effect of uncertainty in the elastic module (<i>E</i>) on a Fluid–Structure Interaction (FSI) model of a patient-specific aorta. Methods: The image-based <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>χ</mi></semantics></math></inline-formula>-method was used to compute the initial <i>E</i> value of the vascular wall. The uncertainty quantification was carried out using the generalized Polynomial Chaos (gPC) expansion technique. The stochastic analysis was based on four deterministic simulations considering four quadrature points. A deviation of about ±20% on the estimation of the <i>E</i> value was assumed. Results: The influence of the uncertain <i>E</i> parameter was evaluated along the cardiac cycle on area and flow variations extracted from five cross-sections of the aortic FSI model. Results of stochastic analysis showed the impact of <i>E</i> in the ascending aorta while an insignificant effect was observed in the descending tract. Conclusions: This study demonstrated the importance of the image-based methodology for inferring <i>E</i>, highlighting the feasibility of retrieving useful additional data and enhancing the reliability of in silico models in clinical practice.https://www.mdpi.com/2308-3425/10/3/109uncertainty quantificationnumerical modelingimagingfluid–structure interactionmechanical propertiesmagnetic resonance imaging |
| spellingShingle | Benigno Marco Fanni Maria Nicole Antonuccio Alessandra Pizzuto Sergio Berti Giuseppe Santoro Simona Celi Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls Journal of Cardiovascular Development and Disease uncertainty quantification numerical modeling imaging fluid–structure interaction mechanical properties magnetic resonance imaging |
| title | Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls |
| title_full | Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls |
| title_fullStr | Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls |
| title_full_unstemmed | Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls |
| title_short | Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls |
| title_sort | uncertainty quantification in the in vivo image based estimation of local elastic properties of vascular walls |
| topic | uncertainty quantification numerical modeling imaging fluid–structure interaction mechanical properties magnetic resonance imaging |
| url | https://www.mdpi.com/2308-3425/10/3/109 |
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