[A study on three-dimensional reconstruction of coronary arteries and numerical simulation of fractional flow reserve based on deep learning].
J, W., L, W., & W, Z. (2026). [A study on three-dimensional reconstruction of coronary arteries and numerical simulation of fractional flow reserve based on deep learning].. Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengx. https://doi.org/10.7507/1001-5515.202603042
J W, L W, W Z. [A study on three-dimensional reconstruction of coronary arteries and numerical simulation of fractional flow reserve based on deep learning].. Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengx. 2026; doi: 10.7507/1001-5515.202603042
J W, L W, W Z. [A study on three-dimensional reconstruction of coronary arteries and numerical simulation of fractional flow reserve based on deep learning].[J]. Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengx. 2026. DOI: 10.7507/1001-5515.202603042.
@article{j2026,
author = {Wei J and Wang L and Zhang W},
title = {[A study on three-dimensional reconstruction of coronary arteries and numerical simulation of fractional flow reserve based on deep learning].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengx},
year = {2026},
doi = {10.7507/1001-5515.202603042},
note = {PMID: 42656106},
}
TY - JOUR AU - Wei J AU - Wang L AU - Zhang W TI - [A study on three-dimensional reconstruction of coronary arteries and numerical simulation of fractional flow reserve based on deep learning]. T2 - Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengx PY - 2026 DO - 10.7507/1001-5515.202603042 AN - PMID:42656106 ER -
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. 针对冠心病无创功能评估中几何建模与血流动力学分析需求，本文构建了一种基于深度学习的冠状动脉分割、三维重建与血流动力学联合分析框架。利用nnU-Net实现冠状动脉自动分割，并结合点云库（PCL）建模技术实现冠状动脉的个性化建模，在此基础上开展冠状动脉的血流动力学数值模拟，分析血流速度场、基于计算机断层扫描（CT）计算的血流储备分数（CT-FFR）及壁面剪切应力（WSS）等关键指标随冠状动脉狭窄程度的变化特性。结果表明，随着冠状动脉狭窄程度增加，狭窄段局部血流加速和高速射流增强，狭窄远端压力下降并导致CT-FFR逐渐降低；同时，狭窄区域及其邻近壁面WSS升高，高WSS区域范围扩大。研究结果表明基于机器学习的三维重建与血流动力学分析相结合的方法可有效弥补传统CT影像在功能评估方面的不足，为冠状动脉狭窄的无创功能评估提供数值模拟依据。.