Wang Anbang, Zhang Heye, Xie Baihong, Gao Zhifan, Dong Yong, Peng Changnong, Liu Xiujian
IEEE Trans Biomed Eng. 2025 Feb;72(2):747-759. doi: 10.1109/TBME.2024.3469289. Epub 2025 Jan 21.
Fractional flow reserve (FFR) derived from coronary angiography, referred to as ICA-FFR, is a less-invasive alternative for invasive FFR measurement based on computational fluid dynamics. Blood flow into side branches influences the accuracy of ICA-FFR. However, properly compensating for side branch flow in ICA-FFR analysis is challenging. In this study, we proposed a physiological side branch flow model to comprehensively compensate side branch flow for ICA-FFR analysis with no need for reconstructing side branch geometry.
the physiological side branch flow model employed a reduced-order model to calculate the pressure distribution in vessel segments. The main coronary artery (without side branches) was delineated and divided based on bifurcation nodes. The model compensates for flow to invisible side branches within each segment and flow to visible side branches at each bifurcation node. Lastly, ICA-FFR based on physiological side branch flow model (ICA-FFR) was calculated from a single angiographic view. Functional stenosis is defined by FFR 0.80.
Our study involved 223 vessels from 172 patients. Using invasive FFR as a reference, the Pearson correlation coefficient of ICA-FFR was 0.93. ICA-FFR showed a high AUC (AUC = 0.96) and accuracy (91.9) in predicting functional stenosis.
The proposed model accurately compensates for flow to side branches without their geometry in ICA-FFR analysis. ICA-FFR analysis exhibits high feasibility and diagnostic performance in identifying functional stenosis.
基于冠状动脉造影的血流储备分数(FFR),即ICA-FFR,是一种基于计算流体动力学的侵入性FFR测量的微创替代方法。流入侧支的血流会影响ICA-FFR的准确性。然而,在ICA-FFR分析中正确补偿侧支血流具有挑战性。在本研究中,我们提出了一种生理侧支血流模型,以全面补偿ICA-FFR分析中的侧支血流,而无需重建侧支几何形状。
生理侧支血流模型采用降阶模型来计算血管段中的压力分布。根据分叉节点对主要冠状动脉(无侧支)进行描绘和划分。该模型补偿每个段内流向不可见侧支的血流以及每个分叉节点处流向可见侧支的血流。最后,从单个血管造影视图计算基于生理侧支血流模型的ICA-FFR(ICA-FFR)。功能性狭窄由FFR<0.80定义。
我们的研究涉及172例患者的223条血管。以侵入性FFR为参考,ICA-FFR的Pearson相关系数为0.93。ICA-FFR在预测功能性狭窄方面显示出高AUC(AUC = 0.96)和准确性(91.9)。
所提出的模型在ICA-FFR分析中准确补偿了流向侧支的血流,而无需其几何形状。ICA-FFR分析在识别功能性狭窄方面具有很高的可行性和诊断性能。