Characterization of non-transmural scar tissue patterns: what can we learn from local impedance and electrograms?

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Regions with pathologically altered substrate have been identified as potentially responsible for atrial fibrillation (AFib) and atrial flutter (AFlut) maintenance. However, these areas are not well characterized using magnetic resonance imaging or low voltage maps as they entail drawbacks when concluding on the underlying substrate. Local impedance (LI) measurements have recently gained attention in radiofrequency ablation and substrate characterization because changes in conductivity may indicate alterations in myocardial electrophysiology. LI values are expected to distinguish between healthy and scar tissue independently from the atrial rhythm, which can improve the understanding of underlying substrate. Moreover, based on impedance reconstruction simulations performed at our institute, non fully transmural patterns are able to be detected when a certain scar thickness is considered. Up to our knowledge, using voltage to characterize the atrial substrate by means of electrograms does not account for non full transmurality. To prove this can give more insights towards the use of LI measurements when accounting for scar detection at ablation procedures.


Student Project
At the beginning, an exploration in the literature in order to understand the electrical properties of the cardiac tissue, as well as the current state-of-the-art, is needed. After that you will get to prepare the tissue patch mesh to compute electrograms on its surface with the OpenCARP software. Different scar transmurality patterns should be modeled in order to generate several clinical scenarios.
Once the electrograms are computed, you will compare the voltage results with local impedance reconstruction simulations already obtained on the same tissue patterns. This analysis can be performed at different scar thicknesses to test the limitations of the simulation set up.

Skills needed

  • The thesis will be conducted in English.
  • Python and Matlab programming skills.
  • Basic knowledge of the following: use of the command line, meshes, signal processing.