Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification.
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| Title: | Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification. |
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| Authors: | Berggren, Caleb C.1 (AUTHOR), Jiang, David1 (AUTHOR), Jack Wang, Y. F.1 (AUTHOR), Bergquist, Jake A.1,2,3 (AUTHOR), Rupp, Lindsay C.1,2,3 (AUTHOR), Liu, Zexin2,4 (AUTHOR), MacLeod, Rob S.1,2,3 (AUTHOR), Narayan, Akil2,4 (AUTHOR), Timmins, Lucas H.1,2,5,6 (AUTHOR) lucas.timmins@tamu.edu |
| Source: | Biomechanics & Modeling in Mechanobiology. Jun2024, Vol. 23 Issue 3, p927-940. 14p. |
| Subjects: | Polynomial chaos, Energy function, Strain energy, Distribution (Probability theory), Biomechanics, Coronary arteries |
| Abstract: | Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification–finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics. [ABSTRACT FROM AUTHOR] |
| Copyright of Biomechanics & Modeling in Mechanobiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 178048235 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Berggren%2C+Caleb+C%2E%22">Berggren, Caleb C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+David%22">Jiang, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jack+Wang%2C+Y%2E+F%2E%22">Jack Wang, Y. F.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bergquist%2C+Jake+A%2E%22">Bergquist, Jake A.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rupp%2C+Lindsay+C%2E%22">Rupp, Lindsay C.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Zexin%22">Liu, Zexin</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22MacLeod%2C+Rob+S%2E%22">MacLeod, Rob S.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Narayan%2C+Akil%22">Narayan, Akil</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Timmins%2C+Lucas+H%2E%22">Timmins, Lucas H.</searchLink><relatesTo>1,2,5,6</relatesTo> (AUTHOR)<i> lucas.timmins@tamu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Biomechanics+%26+Modeling+in+Mechanobiology%22">Biomechanics & Modeling in Mechanobiology</searchLink>. Jun2024, Vol. 23 Issue 3, p927-940. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Polynomial+chaos%22">Polynomial chaos</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+function%22">Energy function</searchLink><br /><searchLink fieldCode="DE" term="%22Strain+energy%22">Strain energy</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Biomechanics%22">Biomechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Coronary+arteries%22">Coronary arteries</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification–finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Biomechanics & Modeling in Mechanobiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10237-023-01814-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 927 Subjects: – SubjectFull: Polynomial chaos Type: general – SubjectFull: Energy function Type: general – SubjectFull: Strain energy Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Biomechanics Type: general – SubjectFull: Coronary arteries Type: general Titles: – TitleFull: Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Berggren, Caleb C. – PersonEntity: Name: NameFull: Jiang, David – PersonEntity: Name: NameFull: Jack Wang, Y. F. – PersonEntity: Name: NameFull: Bergquist, Jake A. – PersonEntity: Name: NameFull: Rupp, Lindsay C. – PersonEntity: Name: NameFull: Liu, Zexin – PersonEntity: Name: NameFull: MacLeod, Rob S. – PersonEntity: Name: NameFull: Narayan, Akil – PersonEntity: Name: NameFull: Timmins, Lucas H. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 16177959 Numbering: – Type: volume Value: 23 – Type: issue Value: 3 Titles: – TitleFull: Biomechanics & Modeling in Mechanobiology Type: main |
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