Image-Based Biophysical Simulation of Intracardiac Abnormal Ventricular Electrograms.

Area of Science:

• Computational Biology
• Medical Imaging
• Cardiac Electrophysiology

Background:

• Intracardiac electrograms (EGM) are crucial for guiding radiofrequency ablation (RFA).
• Accurate simulation of EGM requires patient-specific models integrating imaging and electrophysiology.

Purpose of the Study:

• To develop and validate a computational framework for simulating intracardiac EGM.
• To generate patient-specific models for identifying potential RFA targets.

Main Methods:

• Constructed patient-specific heart models from 3D delayed-enhanced MRI (DE-MRI).
• Solved the Mitchell-Schaeffer model of cardiac electrophysiology (EP) to generate electrical signals.
• Simulated intracardiac EGM using a dipole model of a recording catheter.

Main Results:

• Simulated EGM qualitatively resembled clinical signals.
• Quantitative analysis revealed statistically significant differences between normal and abnormal simulated EGM.
• The simulation approach successfully distinguished between different EGM characteristics.

Conclusions:

• Demonstrated the feasibility of integrating simplified cardiac EP models with imaging data.
• The developed method can generate realistic intracardiac EGM signals.
• This approach represents a step towards noninvasive identification of ablation targets for RFA therapy.