## Description

Impedance cardiography (ICG) is a non-invasive method for diagnosing several cardiac diseases. ICG measures thoracic impedance changes mainly originating from the blood flow pulsation into the aorta. This is because of blood’shigher electrical conductivity than the surrounding tissue types. Realistic simulation models for evaluating cardiac-synchronous impedance changes are necessary to show the feasibility of ICG in the clinical setting. The challenge is

how to generate tissue geometries for the simulation model. In this work, we

use semi-automatic segmentation methods to segment the aorta with 3D slicer

software. First, specific areas of interest with a digital brush are selected. Then,

the algorithm automatically generates the remaining regions of the aorta. Next,

we extract centerlines containing information about the positioning of various

points and the radius of the segmented aorta at the respective position. Finally,

we use the Python interface of Cubit software to create vertices for the points.

Each vertex is the center of a circle, which gets the radius assigned by it. The

circle is then generated as a surface, and an interpolation of the generated surfaces leads to the aortic model in Cubit, which then can be meshed.

We have set up a 3D FEM simulation model in openCFS software to evaluate

the thoracic impedance. The Laplace’s equation for the electric potential given

proper boundary conditions and respective material properties is solved.

Fig. 1 shows the meshed geometry of the aorta and the electric potential values

in the thorax domain by applying ICG. In the next step, more thorax tissues

will be segmented and added to the geometry to improve the simulation model.

Period | 30 Sep 2022 → 2 Oct 2022 |
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Event title | Modelling the Cardiac Function: Theory, Numerical Methods, Clinical Applications |

Event type | Conference |

Location | Cetraro, ItalyShow on map |

Degree of Recognition | International |

## Keywords

- Bioimpedance
- Impedance cardiography
- digital twin
- cardiovascular diseases