Key result
Novel magnetocardiography state-space algorithm accurately segments pathological tissue with a 0.84 DICE score in simulation.
Why the study?
Non-invasive alternatives to invasive catheter electroanatomical mapping are needed to reduce patient risks and improve treatment planning and prevention.
Does a state-space current density estimation algorithm improve the localization of arrhythmogenic tissue compared to a pseudoinverse solution in a simulated heart model?
Does a state-space current density estimation algorithm improve the localization of arrhythmogenic tissue compared to a pseudoinverse solution in a simulated heart model?
A novel state-space algorithm using Kalman filtering and gradient descent can successfully estimate myocardial current density and localize pathological tissue in a simplified simulation, providing a foundation for non-invasive electroanatomical mapping.
Demonstrates simulation feasibility for non-invasive MCG current-density mapping; leaves open human validation for arrhythmia localization.
Electroanatomical mapping is a method for creating a model of the electrophysiology of the human heart. Medical professionals routinely locate and ablate the site of origin of cardiac arrhythmias with invasive catheterization. Non-invasive localization takes the form of electrocardiographic (ECG) or magnetocardiographic (MCG) imaging, where the goal is to reconstruct the electrical activity of the human heart. Non-invasive alternatives to catheter electroanatomical mapping would reduce patients' risks and open new venues for treatment planning and prevention. This work introduces a new system state-based method for estimating the electrical activity of the human heart from MCG measurements. Our model enables arbitrary propagation paths and velocities. A Kalman filter optimally estimates the current densities under the given measurements and model parameters. In an outer optimization loop, these model parameters are then optimized via gradient descent. This paper aims to establish the foundation for future research by providing a detailed mathematical explanation of the algorithm. We demonstrate the feasibility of our method through a simplified one-layer simulation. Our results show that the algorithm can learn the propagation paths from the magnetic measurements. A threshold-based segmentation into healthy and pathological tissue yields a DICE score of 0.84, a recall of 0.77, and a precision of 0.93.
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Engelhardt et al. (2023) studied Cardiac arrhythmias. State-space algorithm for current density estimation vs. Pseudoinverse solution was evaluated on DICE score for threshold-based segmentation into healthy and pathological tissue. A novel state-space algorithm for non-invasive estimation of cardiac current densities from magnetocardiographic signals achieved a DICE score of 0.84 for segmenting pathological tissue in a simulation.
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