A Patchwork Method to Improve the Performance of Current Methods for Solving the Inverse Problem of...

Area of Science:

• Biomedical Engineering
• Computational Electrophysiology
• Medical Imaging

Background:

• Noninvasive electrocardiographic imaging (ECGI) reconstructs cardiac electrical activity from body surface potentials.
• Current ECGI methods show limitations in accurately reconstructing sinus rhythm and identifying breakthrough sites.

Purpose of the Study:

• To develop and evaluate a novel "patchwork method" (PM) that combines existing inverse algorithms for improved ECGI accuracy.
• To address the limitations of traditional methods in reconstructing cardiac electrical activity and breakthrough sites.

Main Methods:

• The patchwork method (PM) integrates the method of fundamental solutions (MFS) and the finite-element method (FEM).
• The PM selects the optimal reconstruction method at each heart node and time step based on the smallest residual in predicted torso potentials (computed via boundary element method - BEM).
• Performance was assessed using simulated ectopic and normal ventricular beats, including analysis with added Gaussian noise.

Main Results:

• The PM yielded more accurate cardiac potentials and activation maps (CC = 0.63 ± 0.01 and 0.61 ± 0.05) compared to MFS, FEM, and BEM.
• The PM successfully identified all epicardial breakthrough sites, outperforming traditional methods that often missed them.
• The PM demonstrated robustness and stability when subjected to Gaussian noise in torso potential data.

Conclusions:

• The patchwork method (PM) overcomes limitations of classical ECGI numerical methods, enhancing the accuracy of mapping cardiac activation during sinus and paced beats.
• This optimized ECGI solution offers a new pathway for advancing not only electrocardiographic imaging but also other inverse problems in science and medicine.