  High-fidelity patient-specific modeling of cardiovascular flows and
hemodynamics is challenging. Direct blood flow measurement inside the body with
in-vivo measurement modalities such as 4D flow magnetic resonance imaging (4D
flow MRI) suffer from low resolution and acquisition noise. In-vitro
experimental modeling and patient-specific computational fluid dynamics (CFD)
models are subject to uncertainty in patient-specific boundary conditions and
model parameters. Furthermore, collecting blood flow data in the near-wall
region (e.g., wall shear stress) with experimental measurement modalities poses
additional challenges. In this study, a computationally efficient data
assimilation method called reduced-order modeling Kalman filter (ROM-KF) was
proposed, which combined a sequential Kalman filter with reduced-order modeling
using a linear model provided by dynamic mode decomposition (DMD). The goal of
ROM-KF was to overcome low resolution and noise in experimental and uncertainty
in CFD modeling of cardiovascular flows. The accuracy of the method was
assessed with 1D Womersley flow, 2D idealized aneurysm, and 3D patient-specific
cerebral aneurysm models. Synthetic experimental data were used to enable
direct quantification of errors using benchmark datasets. The accuracy of
ROM-KF in reconstructing near-wall hemodynamics was assessed by applying the
method to problems where near-wall blood flow data were missing in the
experimental dataset. The ROM-KF method provided blood flow data that were more
accurate than the computational and synthetic experimental datasets and
improved near-wall hemodynamics quantification.
