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Tissue Conductivity Anisotropy is Insufficient to Capture Experimental Lesion Morphology in Cardiac PFA Modelling

  • Argyrios Petras*
  • , Gerard Amoros Figueras
  • , Zoraida Moreno Weidmann
  • , Tomás García-Sánchez
  • , David Viladés Medel
  • , Aurel Neic
  • , Ed Vigmond
  • , Antoni Ivorra
  • , Jose M. Guerra
  • , Luca Gerardo-Giorda
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

Pulsed field ablation (PFA) is a promising treatment for cardiac arrhythmia, but the mechanisms of lesion formation in cardiac tissue remain unclear. Existing computational models typically assume isotropic media and rely on electric field thresholds, which fail to reproduce experimentally observed lesion morphology. We extend our previous work by incorporating cardiac fiber orientation and anisotropic conductivity into a porcine open-chest geometry. Simulations with varying anisotropy ratios showed only minor effects on lesion dimensions, and results did not match experimental data. These findings indicate that anisotropy alone is insufficient to explain lesion geometry in ventricular PFA, and additional mechanisms such as directional electroporation or thermal effects must be considered for accurate modeling.
Original languageEnglish
Title of host publicationComputing in Cardiology
Number of pages4
Volume52
Edition1
DOIs
Publication statusPublished - 2025

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Fields of science

  • 101027 Dynamical systems
  • 102003 Image processing
  • 102023 Supercomputing
  • 102001 Artificial intelligence
  • 101004 Biomathematics
  • 102035 Data science
  • 101014 Numerical mathematics
  • 101028 Mathematical modelling
  • 101013 Mathematical logic
  • 102009 Computer simulation
  • 101 Mathematics
  • 202027 Mechatronics
  • 102019 Machine learning
  • 101024 Probability theory
  • 206003 Medical physics
  • 206001 Biomedical engineering
  • 101020 Technical mathematics

JKU Focus areas

  • Digital Transformation

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