Efficient Methods for Post Myocardial Infarction Ventricular Tachycardia Modeling: from Image Processing to Electrophysiological Simulation

Cardiovascular diseases are the leading cause of death worldwide with an estimate of 17.9 million in
2019, as reported by theWorld Health Organization (WHO) [1]. Among the numerous diseases included
in this category, ischemic heart disease is one of the most frequent, with an estimate of 8.8 million deaths
in 2019 [1]. A common result of ischemic heart disease is myocardial infarction (MI), which occurs when
the blood flow to an area of the heart is blocked. This damages the heart tissue resulting in necrosis
[2]. Approximately 10% of the patients that survive a previous MI event have an increased risk of
death in the following months or years after hospital discharge. In these cases, up to 50% of the deaths
can be secondary to a sustained Ventricular Tachycardia (VT) or Ventricular Fibrillation (VF) event [2].
Ventricular Tachycardia is a heart arrhythmia that occurs when a fast and abnormal heart rate originates
in the ventricles. A well-known mechanism for VT is an action potential wave re-entry caused by
a unidirectional conduction block in slow conductive areas of the myocardium [2]. These areas contain
a complex mixture of scar (i.e. infarcted tissue) and surviving myocytes that is often referred to as
heterogeneous or border zone tissue [3]. For these cases, ablation therapy is the preferred surgical approach.
Approximately 50% of the patients that undergo ablation therapy show VT recurrence before
five years after surgery [4]. In order to improve the outcome, a critical part of the procedure is to
properly localize and ablate the arrhythmic substrate [4].
In this context, it is hypothesized that a combination of scar lesion imaging, cardiac electrophysiology
modeling, and artificial intelligence, can improve the localization of VT ablation targets, the detection
of incomplete ablation procedures, and the selection of the ablation strategy. Previous studies have
already investigated the usage of digital twin technologies for VT therapy planning [3, 5, 6]. However,
the VT modeling pipeline is extensive and still presents several challenges.
First, the manual segmentation of the heart chambers from Late Gadolinium Enhanced (LGE) MRI
images is a tedious procedure that is prone to inter- and intra-operator variability [7]. The segmentation
of the heart chambers is a necessary step for the generation of anatomical 3D models. Furthermore,
it allows to analyze, locate, and quantify myocardial scar, which can be used to guide the ablation
procedure [8]. Lastly, simulating VT in-silico is very dependant on the selected model and simulation
parameters. Previous studies have addressed how different parameter combinations affect the
inducibility of VT re-entrant activity [5, 6]. These studies usually rely on finite element method (FEM)
simulations on very detailed geometries, which can require up to several hours of run-time per simulation.
This inevitably constrains the number of parameter combinations that can be studied.
With these challenges in mind, the main contributions of this study will be:
• Literature review of the state-of-the-art methods in Late Gadolinium Enhanced (LGE) image
processing and segmentation.
• Literature review of the state-of-the-art methods for electrophysiology modeling and simulation
of virtual VT inducibility.
• Evaluation of a deep learning method for automatic myocardium segmentation from LGE images,
with a possible extension to automatically locate and quantify scar tissue. This automatic
segmentation method will focus on the potential advantages compared to manual segmentation
approaches in terms of reproducibility and time savings [7].
• Study of virtual VT inducibility in a set of porcine models after MI with focus on optimal selection
of model parameters. This task will be carried out using the Lattice-Boltzmann method, a monodomain
solver which allows to perform electrophysiology simulations of VT re-entrant activity,
with the advantage of being faster than other FEM approaches [9].
References
[1] World Health Statistics 2021: Monitoring Health for the SDGs: Sustainable Development Goals. Geneva, Switzerland:
World Health Organization, 2021. Licence: CC BY-NC-SA 3.0 IGO.
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method for fast cardiac electrophysiology simulation from 3d images,” in Medical Image Computing and Computer-
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