Abstract
Radiofrequency ablation (RFA) is a minimally invasive procedure used to treat cardiac arrhythmia. Power plays an important role during the procedure as it generates the heat necessary to ablate the targeted tissue.
We developed a 3D in-silico model based on patient imaging data. A key aspect of our study is the placement of a dispersive patch at various positions on the torso, as the electrode’s location can impact both the effectiveness and safety of the procedure. Proper placement is crucial to ensure optimal current distribution and to minimize any potential risks.
We examine the impact of the patch location on both tissue power dissipation and the overall power dissipation within the torso geometry.
Our results show significant variations in tissue power based on the patch position and orientation. By analyzing these variations, we aim to identify optimal patch place- ments that maximize therapeutic efficacy.
We developed a 3D in-silico model based on patient imaging data. A key aspect of our study is the placement of a dispersive patch at various positions on the torso, as the electrode’s location can impact both the effectiveness and safety of the procedure. Proper placement is crucial to ensure optimal current distribution and to minimize any potential risks.
We examine the impact of the patch location on both tissue power dissipation and the overall power dissipation within the torso geometry.
Our results show significant variations in tissue power based on the patch position and orientation. By analyzing these variations, we aim to identify optimal patch place- ments that maximize therapeutic efficacy.
| Original language | English |
|---|---|
| Title of host publication | Computing in Cardiology |
| Number of pages | 4 |
| Volume | 51 |
| DOIs | |
| Publication status | Published - 2024 |
Publication series
| Name | Computing in Cardiology |
|---|---|
| ISSN (Electronic) | 2325-887X |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Fields of science
- 101027 Dynamical systems
- 102023 Supercomputing
- 101004 Biomathematics
- 101014 Numerical mathematics
- 101028 Mathematical modelling
- 102009 Computer simulation
- 101 Mathematics
- 202027 Mechatronics
- 102019 Machine learning
- 101024 Probability theory
- 206001 Biomedical engineering
- 101020 Technical mathematics
JKU Focus areas
- Digital Transformation
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