Image-Based Personalization of Cardiac Anatomy for Coupled Electromechanical Modeling.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Medical Imaging

Background:

• Computational models of cardiac electromechanics (EM) are vital for clinical applications, including simulating patient physiology and treatment responses.
• Current methods using structured meshes struggle to accurately represent complex cardiac anatomy from high-fidelity imaging data.
• Limitations in geometric accuracy hinder the full potential of patient-specific EM models.

Purpose of the Study:

• To review the state-of-the-art in image-based personalization of cardiac anatomy for EM modeling.
• To present novel tools for the automatic generation of anatomically accurate, patient-specific cardiac models.
• To enable biophysically detailed, strongly coupled EM simulations with improved anatomical fidelity.

Main Methods:

• Utilizing high-resolution unstructured meshes for discretizing physics, electrophysiology, and mechanics.
• Employing efficient and scalable solvers to manage the computational demands of detailed models.
• Developing automated workflows for generating patient-specific anatomical models from clinical images.

Main Results:

• Demonstrated the capability to build anatomically and structurally accurate patient-specific cardiac models.
• Achieved strongly coupled EM simulations with unprecedented levels of anatomical and biophysical detail.
• Overcame limitations of structured meshes in representing complex cardiac geometries.

Conclusions:

• The developed tools facilitate automated generation of patient-specific cardiac models.
• High-resolution unstructured meshes and scalable solvers enable detailed EM simulations.
• This approach advances the application of computational EM modeling in personalized cardiovascular medicine.