A review of computational methodologies to predict the fractional flow reserve in coronary arteries with stenosis

Area of Science:

• Cardiovascular Engineering
• Medical Imaging Analysis
• Computational Hemodynamics

Background:

• Coronary artery disease (CAD) poses a significant global health risk, necessitating advanced diagnostic tools.
• Fractional flow reserve (FFR) assessment is crucial for managing CAD.
• Medical image-based computational fluid dynamics (CFD) offers a promising non-invasive approach to FFR evaluation.

Purpose of the Study:

• To comprehensively review state-of-the-art computational methodologies for predicting FFR in coronary arteries.
• To identify key considerations including blood rheology, boundary conditions, fluid-structure interaction (FSI), and multiscale modeling (MM).
• To highlight achievements, gaps, and challenges in non-invasive FFR computation for improved cardiovascular disease diagnosis.

Main Methods:

• Literature review of computational methodologies for FFR prediction.
• Analysis of medical image-based CFD simulations using anatomical vascular geometries.
• In-depth examination of factors influencing hemodynamic evaluation: blood rheology, boundary conditions, FSI, and MM.

Main Results:

• Significant advancements in non-invasive FFR computation using CFD are evident.
• Key considerations for accurate FFR prediction include detailed hemodynamic modeling and patient-specific parameters.
• The review identifies areas requiring further research to refine computational FFR methodologies.

Conclusions:

• Computational methodologies, particularly CFD, show great potential for non-invasive FFR assessment in coronary stenosis.
• Addressing factors like FSI and multiscale modeling is critical for enhancing diagnostic accuracy.
• Further research is needed to bridge existing gaps and improve patient care for cardiovascular diseases.