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.
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Notes
Pilot studies demonstrated that 2 × oversampling notably reduced fluctuations in the Sobol indices and was thus sufficient to ensure stability in PCE results and conclusions drawn (Supp. Figure 2).
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Acknowledgements
The authors thank Prof. Jeff Weiss and Dr. Steve Maas for their insightful discussions and assistance with the FEBio Software Suite, and Drs. Habib Samady and David Molony for access to the clinical data.
Funding
This work was supported, in part, by the National Institutes of Health grants R01 HL-150608 (L.H.T.), U24 EB-029012 (R.S.M., A.N.), P41 GM-103545 (R.S.M.), R24 GM-136986 (R.S.M.), American Heart Association grant 23PRE1019455 (C.C.B.), National Science Foundation GRFP (L.C.R.), and the Nora Eccles Treadwell Foundation for Cardiovascular Research (R.S.M.).
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Conceptualization was performed by R.S.M., A.N., L.H.T; methodology by all authors; formal analysis and investigation by all authors; writing—original draft preparation by C.C.B., D.J., Y.F.J.W., L.H.T; writing—review and editing by all authors; funding acquisition by R.S.M., A.N., L.H.T.
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Berggren, C.C., Jiang, D., Jack Wang, Y.F. et al. Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification. Biomech Model Mechanobiol (2024). https://doi.org/10.1007/s10237-023-01814-2
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DOI: https://doi.org/10.1007/s10237-023-01814-2