Coronary artery fractional flow reserve (FFR) is a critical physiological indicator for assessment of impaired blood flow caused by coronary artery stenosis. The wire-based invasive measurement of blood flow pressure gradient across stenosis is the gold standard for clinical measurement of FFR. However, it has the risk of vascular injury and requires the use of vasodilators, increasing the time and overall cost of interventional examination. Coronary imaging is playing an important role in clinical diagnosis of stenotic lesions, evaluation of severity of lesions, and planning of therapies. In recent years, the computation of FFR based on the physiological information of blood flow obtained from routinely collected coronary image data has become a research focus in this field. This technique reduces the cost of physiological assessment of coronary lesions and the use of pressure wires. It is beneficial to strengthen the physiological guidance in interventional therapy. In order to better understand this emerging technique, this paper highlights its implementation principle and diagnostic performance, analyzes practical problems and current challenges in clinical applications, and discusses possible future development.
To address the needs of geometric modeling and hemodynamic analysis in the noninvasive functional assessment of coronary heart disease, this study establishes a deep learning-based integrated framework for coronary artery segmentation, three-dimensional reconstruction, and hemodynamic analysis. The nnU-Net model was used to achieve automatic coronary artery segmentation, and point cloud modeling techniques based on the Point Cloud Library (PCL) were further applied to construct patient-specific coronary artery models. Based on the reconstructed models, coronary hemodynamic numerical simulations were performed to analyze the variation characteristics of key indicators, including the blood flow velocity field, fractional flow reserve derived from computed tomography (CT-FFR), and wall shear stress (WSS), under different degrees of coronary artery stenosis. The results showed that, with increasing stenosis severity, local flow acceleration and high-velocity jet flow in the stenotic segment were enhanced, while the pressure distal to the stenosis decreased, leading to a gradual reduction in CT-FFR. Meanwhile, WSS increased in the stenotic region and adjacent vessel walls, and the high-WSS region expanded. These results indicate that the combination of machine learning-based three-dimensional reconstruction and hemodynamic analysis can help compensate for the limitations of conventional CT imaging in functional assessment, providing a numerical simulation basis for the noninvasive functional evaluation of coronary artery stenosis.