Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease

Area of Science:

• Cardiovascular Science
• Biomedical Engineering
• Computational Modeling

Background:

• Physics-based computational models are vital for simulating cardiovascular hemodynamics, tissue mechanics, and physiology in both healthy and diseased states.
• Computational fluid dynamics (CFD) applications have expanded from surgical planning to uncovering disease mechanisms and guiding experimental design.

Purpose of the Study:

• To review the expanding applications of cardiovascular modeling beyond surgical planning.
• To highlight advancements in modeling methodologies and their clinical impact.
• To outline future challenges and open problems in the field.

Main Methods:

• Review of physics-based computational models, including computational fluid dynamics (CFD).
• Discussion of multiscale modeling integrating multiple physical processes.
• Exploration of recent developments in image processing, fluid-structure interaction, modeling under uncertainty, and reduced-order modeling.

Main Results:

• Cardiovascular models are increasingly incorporating multiple physical processes across various scales.
• Patient-specific modeling is gaining clinical adoption for congenital and acquired cardiovascular diseases.
• Advancements in modeling methodology enable clinically relevant simulations.

Conclusions:

• Traditional CFD alone is insufficient for complex clinical and biological problems.
• Coupling CFD with multiscale biological, physical, and physiological models is essential for comprehensive mechanobiological system modeling.
• Integrated modeling approaches are crucial for advancing cardiovascular research and clinical care.