A flexible framework for sequential estimation of model parameters in computational hemodynamics - Archive ouverte HAL Access content directly
Journal Articles Advanced Modeling and Simulation in Engineering Sciences Year : 2020

A flexible framework for sequential estimation of model parameters in computational hemodynamics

(1) , (1) , (2) , (3, 4) , (5)
1
2
3
4
5

Abstract

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. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (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. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A "Netlist" implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.
Fichier principal
Vignette du fichier
s40323-020-00186-x.pdf (5.86 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

hal-03113107 , version 1 (18-01-2021)

Identifiers

Cite

Christopher J Arthurs, Nan Xiao, Philippe Moireau, Tobias Schaeffter, Alberto Alberto Figueroa. A flexible framework for sequential estimation of model parameters in computational hemodynamics. Advanced Modeling and Simulation in Engineering Sciences, 2020, 7, ⟨10.1186/s40323-020-00186-x⟩. ⟨hal-03113107⟩
92 View
106 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More