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Related Experiment Video

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Efficient identification of scars using heterogeneous model hierarchies.

Fatemeh Chegini1,2, Alena Kopaničáková1,2, Rolf Krause1,2

  • 1Institute of Computational Science, USI, Lugano, Switzerland.

Europace : European Pacing, Arrhythmias, and Cardiac Electrophysiology : Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology
|March 22, 2021
PubMed
Summary

Estimating myocardial scar conductivity parameters is crucial for heart disease diagnosis. Combining electrophysiology models of varying complexity accelerates scar detection from endocardial mapping data.

Keywords:
Endocardial mappingHeterogeneous model hierarchiesInverse problemMultilevel trust-regionScar detection

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Area of Science:

  • Computational electrophysiology
  • Cardiac modeling
  • Medical imaging analysis

Background:

  • Myocardial scar detection and quantification are vital for diagnosing heart conditions and creating personalized simulation models.
  • Scar tissue exhibits distinct electrical conduction properties compared to healthy tissue.
  • Accurate estimation of conductivity parameters is essential for understanding scar behavior.

Purpose of the Study:

  • To estimate conductivity-related parameters from endocardial mapping data for myocardial scar characterization.
  • To accelerate the computationally intensive inverse problem of parameter estimation.
  • To combine electrophysiology models of different complexities for improved efficiency and accuracy.

Main Methods:

  • Distributed parameter estimation by minimizing the misfit between simulated and measured endocardial electrical activity.
  • Utilizing the monodomain model with regularization for scar tissue modeling.
  • Implementing a recursive multilevel trust-region method with grid and model hierarchies (monodomain-eikonal).

Main Results:

  • The multilevel solver demonstrated significantly improved speed compared to single-level solvers.
  • Numerical examples confirmed the efficiency and data-dependent estimation quality.
  • Endocardial mapping data of realistic density proved sufficient for quantitatively estimating scar location, size, and shape near the endocardial surface.

Conclusions:

  • Monodomain models are recommended for scar reconstruction due to significant differences observed with eikonal models in certain situations.
  • Eikonal models offer substantial computational acceleration, enabling the use of complex electrophysiology models for myocardial scar estimation.
  • The developed methods provide a faster and effective approach for myocardial scar characterization using endocardial mapping data.