Qin Peiming, Yi Yan, Xu Cheng, Zou Limiao, Jia Fenggang, Guo Jian, Wang Ming, Zhang Yan, Wang Ziquan, Dong Pei, Wu Dijia, Wang Xiaodong, Wang Yining
Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China.
Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Quant Imaging Med Surg. 2025 Sep 1;15(9):7909-7921. doi: 10.21037/qims-24-2172. Epub 2025 Aug 19.
Current computed tomography angiography-derived fractional flow reserve (CT-FFR) diagnosis leaves room for improvement in diagnosing coronary heart disease. In this study, the computed fluid dynamics boundary condition optimization method was used to calculate CT-FFR aiming to improve the diagnostic accuracy of CT-FFR for coronary heart disease.
The two enhancement approaches are as follows: (A) inlet flow optimization, which involves determining the total coronary inlet flow rate by summing the myocardial blood flow (MBF) across the entire left ventricular myocardium; and (B) inlet & outlet flow optimization: building upon method A, where the outlet flow of coronary artery branches is calculated through blood supply area analysis.
A total of 100 fractional flow reserve pressure guide wire measurement sites from 47 cases were used to evaluate the above two methods comparing with the traditional computed fluid dynamics method without computed tomography perfusion (CTP) images. In traditional method, the accuracy was 88%, the sensitivity was 91.4% (95% confidence interval: 75.8-97.7%), and the specificity was 86.2% (95% confidence interval: 74.8-93.1%). In Method A, the accuracy improved by 5% (93%), the sensitivity remained unchanged (91.4%, 95% confidence interval: 75.8-97.7%), and the specificity increased by 7.6% (93.8%, 95% confidence interval: 84.2-98%). In Method B, the accuracy increased by 6% (94%), the sensitivity increased to 100% (95% confidence interval: 87.7-100%), and the specificity increased by 4.8% (94%, 95% confidence interval: 80.3-96.2%).
The computed fluid dynamics calculation, guided by MBF values from stress CTP imaging, helps enhance the consistency between CT-FFR calculation and invasive fractional flow reserve measurements.
目前基于计算机断层扫描血管造影的血流储备分数（CT-FFR）诊断在冠心病诊断方面仍有改进空间。在本研究中，采用计算流体动力学边界条件优化方法计算CT-FFR，旨在提高CT-FFR对冠心病的诊断准确性。
两种增强方法如下：（A）入口血流优化，即通过计算整个左心室心肌的心肌血流量（MBF）总和来确定冠状动脉总入口血流速率；（B）入口和出口血流优化：在方法A的基础上，通过供血区域分析计算冠状动脉分支的出口血流。
共使用47例患者的100个血流储备分数压力导丝测量部位，与无计算机断层扫描灌注（CTP）图像的传统计算流体动力学方法比较，评估上述两种方法。传统方法的准确率为88%，灵敏度为91.4%（95%置信区间：75.8 - 97.7%），特异性为86.2%（95%置信区间：74.8 - 93.1%）。在方法A中，准确率提高了5%（93%），灵敏度不变（91.4%，95%置信区间：75.8 - 97.7%），特异性提高了7.6%（93.8%，95%置信区间：84.2 - 98%）。在方法B中，准确率提高了6%（94%），灵敏度提高到100%（95%置信区间：87.7 - 100%），特异性提高了4.8%（94%，95%置信区间：80.3 - 96.2%）。
在应力CTP成像的MBF值指导下进行计算流体动力学计算，有助于提高CT-FFR计算与有创血流储备分数测量之间的一致性。
