Effects of Pulsatile Flow on Fractional Flow Reserve Assessed Using a Reduced-Order Model

Area of Science:

• Cardiovascular Physiology
• Computational Fluid Dynamics
• Medical Imaging Analysis

Background:

• Fractional flow reserve (FFR) is a noninvasive method to assess coronary artery stenosis.
• Quantifying FFR reliability under physiological variability is challenging due to computational costs.
• Understanding FFR uncertainty is crucial for accurate clinical decision-making.

Purpose of the Study:

• To quantify FFR estimate uncertainty under stochastic, pulsatile physiological conditions.
• To identify key physiological factors contributing to FFR variability.
• To evaluate the robustness of computational FFR in clinical scenarios.

Main Methods:

• Developed an uncertainty quantification framework integrating reduced-order models and polynomial chaos expansion.
• Coupled a 1D coronary blood flow solver with lumped parameter networks for pulsatile hemodynamics.
• Performed global sensitivity analysis on 17 physiological parameters to assess FFR variability contributions.

Main Results:

• Myocardial compression was the dominant contributor to FFR variability.
• FFR showed high robustness across a wide range of physiological uncertainties.
• A linear relationship between mean FFR and standard deviation was observed, indicating systematic variability changes with stenosis severity.

Conclusions:

• This study provides a quantitative assessment of FFR's physiological robustness and variability.
• Limited FFR variability near decision thresholds supports its reliability for coronary stenosis assessment.
• Steady or quasi-steady assumptions are practical for many computational FFR workflows due to limited pulsatile inflow influence.