Article ; Online: Uncertainty quantification of the wall thickness and stiffness in an idealized dissected aorta.
Journal of the mechanical behavior of biomedical materials
2024 Volume 151, Page(s) 106370
Abstract: Personalized treatment informed by computational models has the potential to markedly improve the outcome for patients with a type B aortic dissection. However, existing computational models of dissected walls significantly simplify the characteristic ... ...
| Abstract | Personalized treatment informed by computational models has the potential to markedly improve the outcome for patients with a type B aortic dissection. However, existing computational models of dissected walls significantly simplify the characteristic false lumen, tears and/or material behavior. Moreover, the patient-specific wall thickness and stiffness cannot be accurately captured non-invasively in clinical practice, which inevitably leads to assumptions in these wall models. It is important to evaluate the impact of the corresponding uncertainty on the predicted wall deformations and stress, which are both key outcome indicators for treatment optimization. Therefore, a physiology-inspired finite element framework was proposed to model the wall deformation and stress of a type B aortic dissection at diastolic and systolic pressure. Based on this framework, 300 finite element analyses, sampled with a Latin hypercube, were performed to assess the global uncertainty, introduced by 4 uncertain wall thickness and stiffness input parameters, on 4 displacement and stress output parameters. The specific impact of each input parameter was estimated using Gaussian process regression, as surrogate model of the finite element framework, and a δ moment-independent analysis. The global uncertainty analysis indicated minor differences between the uncertainty at diastolic and systolic pressure. For all output parameters, the 4 |
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| MeSH term(s) | Humans ; Uncertainty ; Aorta ; Aortic Dissection ; Blood Pressure ; Models, Cardiovascular ; Stress, Mechanical |
| Language | English |
| Publishing date | 2024-01-03 |
| Publishing country | Netherlands |
| Document type | Journal Article |
| ZDB-ID | 2378381-3 |
| ISSN | 1878-0180 ; 1751-6161 |
| ISSN (online) | 1878-0180 |
| ISSN | 1751-6161 |
| DOI | 10.1016/j.jmbbm.2024.106370 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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