Generating patient-specific computational models with point cloud data from human atrial electrophysiology studies.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Cardiology

Background:

• Atrial fibrillation (AF) is a prevalent arrhythmia with significant mortality.
• Computational models are valuable tools for understanding AF mechanisms.
• Accurate patient-specific models are needed for effective AF research and treatment.

Purpose of the Study:

• To develop a pipeline for generating patient-specific left atrial models using point cloud data.
• To evaluate the performance of these models by comparing simulated and measured electrograms.
• To investigate the impact of fibrotic tissue on electrogram morphology in AF models.

Main Methods:

• Developed a pipeline for patient-specific left atrial posterior wall models from point cloud data.
• Created models for paroxysmal AF patients (healthy tissue) and persistent AF patients (varying fibrosis).
• Compared simulated and measured electrograms using metrics like local activation time and cross-correlation.

Main Results:

• Simulated electrograms showed good correlation with measured data in terms of local activation time and cross-correlation peak.
• Significant differences in amplitude and duration were observed for some electrograms.
• Increased fibrosis density in models led to more multiphasic electrogram morphologies, consistent with other studies.

Conclusions:

• Point cloud data is sufficient for creating accurate left atrial models for AF simulation.
• This modeling approach can generate electrograms comparable to measured waveforms.
• The method holds promise for patient-specific studies, ablation planning, and arrhythmia research.