  Central to the clinical adoption of patient-specific modeling strategies is
demonstrating that simulation results are reliable and safe. 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. 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 summarizing contribution of the embedded fibers
to the overall artery stiffness. Results from a patient-specific coronary model
confirmed many of these findings. Collectively, this highlights the impact of
material property variation on predicted artery stresses and presents a
pipeline to explore and characterize uncertainty in computational biomechanics.
