Stochastic Parameter Prediction in Cardiovascular Problems

Patient-specific modeling of cardiovascular flows with high-fidelity is challenging due to its dependence on accurately estimated velocity boundary profiles, which are essential for precise simulations and directly influence wall shear stress calculations - key in predicting cardiovascular diseases like atherosclerosis. This data, often derived from in vivo modalities like 4D flow MRI, suffers from low resolution and noise. To address this, we employ a stochastic data assimilation technique that integrates computational fluid dynamics with an advanced Ensemble-based Kalman filter, enhancing model accuracy while accounting for uncertainties. Our approach sequentially collects velocity data over time within the vascular model, enabling real-time refinement of unknown boundary estimations. The mathematical model uses the incompressible Navier-Stokes equation to simulate aortic blood flow. We consider unknown boundaries as constant, time-dependent, and space-time dependent in two- and three-dimensional models. In our 2-dimensional model, relative errors were as low as 0.996\% for constant boundaries and up to 2.63\% and 2.61\% for time-dependent and space-time dependent boundaries, respectively, over an observation span of two-time steps. For the 3-dimensional patient-specific model, the relative error was 7.37\% for space-time dependent boundaries. By refining the velocity boundary profile, our method improves wall shear stress predictions, enhancing the accuracy and reliability of models specific to individual cardiovascular patients. These advancements could contribute to better diagnosis and treatment of cardiovascular diseases.