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Calibration of a three-state cell death model for cardiomyocytes and its application in radiofrequency ablation

Research output: Contribution to journalArticlepeer-review

Abstract

Objective. Thermal cellular injury follows complex dynamics and subcellular processes can heal the inflicted damage if insufficient heat is administered during the procedure. This work aims to the identification of irreversible cardiac tissue damage for predicting the success of thermal treatments. Approach. Several approaches exist in the literature, but they are unable to capture the healing process and the variable energy absorption rate that several cells display. Moreover, none of the existing models is calibrated for cardiomyocytes. We consider a three-state cell death model capable of capturing the reversible damage of a cell, we modify it to include a variable energy absorption rate and we calibrate it for cardiac myocytes. Main results. We show how the thermal damage predicted by the model response is in accordance with available data in the literature on myocytes for different temperature distributions. When coupled with a computational model of radiofrequency catheter ablation, the model predicts lesions in agreement with experimental measurements. We also present additional experiments (repeated ablations and catheter movement) to further illustrate the potential of the model. Significance. We calibrated a three-state cell death model to provide physiological results for cardiac myocytes. The model can be coupled with ablation models and reliably predict lesion sizes comparable to experimental measurements. Such approach is robust for repeated ablations and dynamic catheter-cardiac wall interaction, and allows for tissue remodelling in the predicted damaged area, leading to more accurate in-silico predictions of ablation outcomes.
Original languageEnglish
Article number065003
JournalPhysiological Measurement
Volume44
Issue number6
DOIs
Publication statusPublished - 01 Jun 2023

Fields of science

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

JKU Focus areas

  • Digital Transformation

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