Scholarlink

<b>Purpose</b> <p>Computationally derived fractional flow reserve (FFR) has emerged as a noninvasive alternative for evaluating the functional severity of coronary artery stenosis and supporting clinical decision-making. However, the intrinsic reliability of FFR under physiological variability remains an important unresolved issue. This uncertainty is difficult to quantify using high-fidelity simulations alone because of their prohibitive computational cost. The purpose of this study is to quantify the uncertainty of FFR estimates under stochastic, pulsatile physiological boundary conditions and to identify the dominant physiological contributors to FFR variability.</p> <b>Methods</b> <p>An accurate and computationally efficient uncertainty quantification framework was developed by integrating a reduced-order model derived from three-dimensional simulations with polynomial chaos expansion. The framework couples a calibrated one-dimensional coronary blood flow solver with physiologically informed lumped parameter networks, enabling efficient simulation of realistic pulsatile coronary hemodynamics. Seventeen physiological parameters related to myocardial mechanics and systemic circulation were treated as stochastic inputs, and global sensitivity analysis was conducted to assess their contributions to FFR variability.</p> <b>Results</b> <p>The sensitivity analysis identified the myocardial compression parameter as the dominant contributor to FFR variability, followed by selected systemic and cardiac parameters that contribute to aortic pressure. Across all simulated configurations, FFR demonstrated high robustness under broad physiological uncertainty, including cases near clinically relevant decision thresholds. A strong linear relationship was consistently observed between the mean FFR and its standard deviation, indicating that intrinsic hemodynamic variability changes systematically with stenosis severity; however, the magnitude of this variability remained limited within the physiological parameter space investigated.</p> <b>Conclusion</b> <p>This study provides a quantitative basis for interpreting the physiological robustness of FFR and for characterizing its intrinsic variability under realistic pulsatile hemodynamic conditions. The limited variability observed near clinically relevant decision thresholds supports the reliability of cycle-averaged FFR as a functional index of coronary stenosis severity. Within the physiological parameter space investigated, the limited influence of detailed pulsatile inflow information on cycle-averaged FFR also supports the practical use of steady or quasi-steady assumptions in many computational FFR workflows.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月24日

查看全文
  • Wonjin Choi,
  • Bon-Kwon Koo,
  • Jung-Kyu Han,
  • Inpyo Lee,
  • Hyun Jin Kim

摘要

Purpose

Computationally derived fractional flow reserve (FFR) has emerged as a noninvasive alternative for evaluating the functional severity of coronary artery stenosis and supporting clinical decision-making. However, the intrinsic reliability of FFR under physiological variability remains an important unresolved issue. This uncertainty is difficult to quantify using high-fidelity simulations alone because of their prohibitive computational cost. The purpose of this study is to quantify the uncertainty of FFR estimates under stochastic, pulsatile physiological boundary conditions and to identify the dominant physiological contributors to FFR variability.

Methods

An accurate and computationally efficient uncertainty quantification framework was developed by integrating a reduced-order model derived from three-dimensional simulations with polynomial chaos expansion. The framework couples a calibrated one-dimensional coronary blood flow solver with physiologically informed lumped parameter networks, enabling efficient simulation of realistic pulsatile coronary hemodynamics. Seventeen physiological parameters related to myocardial mechanics and systemic circulation were treated as stochastic inputs, and global sensitivity analysis was conducted to assess their contributions to FFR variability.

Results

The sensitivity analysis identified the myocardial compression parameter as the dominant contributor to FFR variability, followed by selected systemic and cardiac parameters that contribute to aortic pressure. Across all simulated configurations, FFR demonstrated high robustness under broad physiological uncertainty, including cases near clinically relevant decision thresholds. A strong linear relationship was consistently observed between the mean FFR and its standard deviation, indicating that intrinsic hemodynamic variability changes systematically with stenosis severity; however, the magnitude of this variability remained limited within the physiological parameter space investigated.

Conclusion

This study provides a quantitative basis for interpreting the physiological robustness of FFR and for characterizing its intrinsic variability under realistic pulsatile hemodynamic conditions. The limited variability observed near clinically relevant decision thresholds supports the reliability of cycle-averaged FFR as a functional index of coronary stenosis severity. Within the physiological parameter space investigated, the limited influence of detailed pulsatile inflow information on cycle-averaged FFR also supports the practical use of steady or quasi-steady assumptions in many computational FFR workflows.