Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Area of Science:

• Biomedical Engineering
• Computational Biology
• Cardiovascular Research

Background:

• Hemodynamic factors from pulsatile blood flow are critical in vascular health and diseases, including atherosclerosis.
• Computational methods like CFD and FSI are used to quantify these forces from medical imaging.

Purpose of the Study:

• To review methods for obtaining accurate hemodynamic factors regulating vascular cells.
• To describe patient-specific simulation pipelines and their uncertainties.
• To discuss advancements combining simulations with machine learning for vascular disease research.

Main Methods:

• Review of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-structure interaction (FSI) simulations.
• Description of patient-specific simulation pipelines: medical imaging, image processing, meshing, boundary conditions, solvers, and analysis.
• Exploration of machine learning integration with biomechanical simulations.

Main Results:

• Detailed hemodynamic forces can be quantified using various medical imaging modalities.
• Patient-specific simulation pipelines involve multiple steps with inherent uncertainties.
• Machine learning offers faster and more cost-effective hemodynamic factor analysis.

Conclusions:

• Accurate hemodynamic factor quantification is essential for understanding vascular cell regulation.
• Standardization and uncertainty evaluation are crucial in simulation pipelines.
• Integrating machine learning with simulations enhances accessibility for vascular disease research and personalized care.