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.
| Originalsprache | Englisch |
|---|---|
| Titel | Computing in Cardiology |
| Seitenumfang | 4 |
| Band | 51 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 2024 |
Publikationsreihe
| Name | Computing in Cardiology |
|---|---|
| ISSN (elektronisch) | 2325-887X |
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
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SDG 3 – Gute Gesundheit und Wohlergehen
Wissenschaftszweige
- 101027 Dynamische Systeme
- 102023 Supercomputing
- 101004 Biomathematik
- 101014 Numerische Mathematik
- 101028 Mathematische Modellierung
- 102009 Computersimulation
- 101 Mathematik
- 202027 Mechatronik
- 102019 Machine Learning
- 101024 Wahrscheinlichkeitstheorie
- 206001 Biomedizinische Technik
- 101020 Technische Mathematik
JKU-Schwerpunkte
- Digital Transformation
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