parameter estimation by sequential testing

#Parameter estimation by sequential testing manual

Furthermore, in the case of FSI models, the parameters defining the structural stiffness also affect the solution greatly. Due to the scarcity of direct data on flow and pressure, achieving pathophysiologically accurate results often requires specification of boundary conditions via reduced order models such as lumped parameter networks (LPN). In particular, three-dimensional models of blood flow in the vasculature-with or without fluid-structure interaction (FSI)-have applications in non-invasive diagnostics, medical device design, surgical planning, and disease research. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.Ĭomputational models of hemodynamics are powerful tools for studying the cardiovascular system in health and disease. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN.

The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions.

#Parameter estimation by sequential testing manual

Current workflows are manual and time-consuming. A major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc.