Imaging-based integrative models of the heart: closing the loop between experiment and simulation

Area of Science:

• Cardiovascular Research
• Biomedical Engineering
• Computational Biology

Background:

• Understanding cardiac electrical conduction is crucial for diagnosing and treating heart conditions.
• Individual variations in cardiac anatomy and fiber orientation significantly influence electrical propagation.
• Accurate modeling requires high-resolution data on both ventricular geometry and electrical activity.

Purpose of the Study:

• To develop and integrate methodologies for detailed mapping and modeling of individual canine hearts.
• To investigate the relationship between cardiac anatomical structure and electrical conduction patterns.
• To validate computational models against experimentally measured epicardial activation.

Main Methods:

• High-density epicardial electrode arrays for mapping ventricular activation.
• Diffusion tensor magnetic resonance imaging (DTMRI) for high-resolution ventricular geometry and fiber orientation measurement.
• Computational simulation of electrical conduction using integrated experimental data.

Main Results:

• Successful integration of high-density electrophysiological mapping and DTMRI-based structural modeling.
• Computational models demonstrated reasonably accurate reproduction of measured epicardial activation patterns.
• The study established a framework for personalized cardiac electrical modeling.

Conclusions:

• The developed methodologies enable accurate electrical mapping and computational modeling of individual hearts.
• This approach enhances the understanding of the interplay between cardiac anatomy and electrical conduction.
• This work lays the foundation for future personalized cardiac electrophysiology research and clinical applications.