Parameter Estimation in FSI models

and its application to cardiac contraction

Joint work Cristóbal Bertoglio

Reidmen Aróstica - April 16, 2024, University of Groningen - Brief Summary

Table of Content

Motivation, Dynamical System and Noisy Data
Parameter Estimation - Algorithm
Parameter estimation using solid, fluid or joint measurements
Summary

Motivation and Dynamical System

Brief Explanation

Motivation

Domain evolution in time Pfaller et al. (2019)

HO_real

Heart sample G. Sommer et al. (2015)

Fluid Dynamics $\mathcal{O}(\Delta T^{1/2})$

Solid Model and Parameter of interest

FSI Algorithm

Noise for fluid and solid

Noise $\approx 1.4e^{-3}[m]$ of solid displacement and $\approx 0.22 [m/s]$ of velocity.

Parameter Estimation

How we manage it?💡

General Idea💡

ROUKF

Estimation of parameters

Using an acquisition time of $20 [ms], \sigma = 0.5$

Estimation of epicardial stiffness $\alpha_{epi}$

Estimation of epicardial damping $\beta_{epi}$

Estimation of $\alpha_{epi}$ and $\beta_{epi}$

Estimation of contractility $\sigma_0$

Estimation of epicardial stiffness $\alpha_{epi}$ at 10[ms]

Estimation of epicardial damping $\beta_{epi}$ at 10[ms]

Estimation of $\sigma_0$ for $\sigma_0 = 0.25$ at 10[ms]

V. Summary

Summary

✓ To the best of the author's knowledge, 'velocity images' for parameter estimation has not been assess before.
✓ Our preliminary results show that they can improve the prediction!
✓ Work in progress: FSI (two-ways) convergence rate and solid surface images.

Contact Information

c.a.bertoglio@rug.nl
r.a.arostica.barrera@rug.nl

Thank you!

cardiomath

https://bit.ly/30NtCtD