A Multi-physics model of flow from coronary angiography: Insights to microvascular function

Area of Science:

• Cardiovascular research
• Medical imaging analysis
• Computational fluid dynamics

Background:

• Coronary microvascular dysfunction (CMD) affects millions, characterized by impaired vasodilation and reduced myocardial blood flow.
• Current invasive diagnostics (IMR, CFR) have limited clinical adoption due to complexity.
• Coronary angiography provides flow data for CMD diagnosis but is underutilized.

Purpose of the Study:

• To develop and validate a computational fluid dynamics (CFD) model for analyzing coronary angiography data.
• To introduce a contrast intensity profile (CIP) to quantify contrast dynamics.
• To assess the impact of coronary lumped parameter model (LPM) variables on CIP.

Main Methods:

• A 3D-0D coupled multi-physics CFD model was created and calibrated.
• Simulations focused on contrast injection and washout during angiography.
• Sensitivity analyses explored the influence of LPM parameters on CIP.

Main Results:

• The CFD model successfully produced physiologically relevant hemodynamic outcomes.
• Model calibration allowed for effective simulation of angiography processes.
• Sensitivity studies indicated resistance significantly influences CIP slopes compared to capacitance.

Conclusions:

• A novel modeling framework shows potential for extracting coronary microcirculation information from angiography.
• The approach requires in vivo validation for clinical application.
• Future clinical studies are necessary to confirm the model's utility in diagnosing CMD.