Literature DB >> 27531694

Data assimilation for identification of cardiovascular network characteristics.

Rajnesh Lal1, Bijan Mohammadi1, Franck Nicoud1.   

Abstract

A method to estimate the hemodynamics parameters of a network of vessels using an Ensemble Kalman filter is presented. The elastic moduli (Young's modulus) of blood vessels and the terminal boundary parameters are estimated as the solution of an inverse problem. Two synthetic test cases and a configuration where experimental data are available are presented. The sensitivity analysis confirms that the proposed method is quite robust even with a few numbers of observations. The simulations with the estimated parameters recovers target pressure or flow rate waveforms at given specific locations, improving the state-of-the-art predictions available in the literature. This shows the effectiveness and efficiency of both the parameter estimation algorithm and the blood flow model.
Copyright © 2016 John Wiley & Sons, Ltd.

Keywords:  1D blood flow, ensemble Kalman filter, inverse problem, parameter estimation

Mesh:

Year:  2016        PMID: 27531694     DOI: 10.1002/cnm.2824

Source DB:  PubMed          Journal:  Int J Numer Method Biomed Eng        ISSN: 2040-7939            Impact factor:   2.747


  3 in total

1.  An optimal control approach to determine resistance-type boundary conditions from in-vivo data for cardiovascular simulations.

Authors:  Elisa Fevola; Francesco Ballarin; Laura Jiménez-Juan; Stephen Fremes; Stefano Grivet-Talocia; Gianluigi Rozza; Piero Triverio
Journal:  Int J Numer Method Biomed Eng       Date:  2021-08-15       Impact factor: 2.648

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

Authors:  Christopher J Arthurs; Nan Xiao; Philippe Moireau; Tobias Schaeffter; C Alberto Figueroa
Journal:  Adv Model Simul Eng Sci       Date:  2020-12-02

Review 3.  Inverse problems in blood flow modeling: A review.

Authors:  David Nolte; Cristóbal Bertoglio
Journal:  Int J Numer Method Biomed Eng       Date:  2022-05-24       Impact factor: 2.648

  3 in total

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