From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (C.T., C.N.D.C., S.B., M.R., T.W.M., T.M.D., R.R.B., U.J.S.), and Division of Cardiology, Department of Medicine (R.R.B., D.H.S., U.J.S.), Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr, Charleston, SC 29425-2260; Department of Computed Tomography-Research & Development, Siemens Healthcare GmbH, Forchheim, Germany (K.L.G., C.C., C.S., M.S.); Department of Corporate Technology, Siemens SRL, Brasov, Romania (L.M.I.); and Department of Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ (S.R., P.S.).
Radiology. 2018 Jul;288(1):64-72. doi: 10.1148/radiol.2018171291. Epub 2018 Apr 10.
Purpose To compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)-FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFR) and FFR derived from coronary CT angiography based on machine learning algorithm (hereafter, FFR)-against coronary CT angiography and quantitative coronary angiography (QCA). Materials and Methods A total of 85 patients (mean age, 62 years ± 11 [standard deviation]; 62% men) who had undergone coronary CT angiography followed by invasive FFR were included in this single-center retrospective study. FFR values were derived on-site from coronary CT angiography data sets by using both FFR and FFR. The performance of both techniques for detecting lesion-specific ischemia was compared against visual stenosis grading at coronary CT angiography, QCA, and invasive FFR as the reference standard. Results On a per-lesion and per-patient level, FFR showed a sensitivity of 79% and 90% and a specificity of 94% and 95%, respectively, for detecting lesion-specific ischemia. Meanwhile, FFR resulted in a sensitivity of 79% and 89% and a specificity of 93% and 93%, respectively, on a per-lesion and per-patient basis (P = .86 and P = .92). On a per-lesion level, the area under the receiver operating characteristics curve (AUC) of 0.89 for FFR and 0.89 for FFR showed significantly higher discriminatory power for detecting lesion-specific ischemia compared with that of coronary CT angiography (AUC, 0.61) and QCA (AUC, 0.69) (all P < .0001). Also, on a per-patient level, FFR (AUC, 0.91) and FFR (AUC, 0.91) performed significantly better than did coronary CT angiography (AUC, 0.65) and QCA (AUC, 0.68) (all P < .0001). Processing time for FFR was significantly shorter compared with that of FFR (40.5 minutes ± 6.3 vs 43.4 minutes ± 7.1; P = .042). Conclusion The FFR algorithm performs equally in detecting lesion-specific ischemia when compared with the FFR approach. Both methods outperform accuracy of coronary CT angiography and QCA in the detection of flow-limiting stenosis.
比较两种基于计算流体力学的冠状动脉 CT 血管造影（CCTA）衍生的血流储备分数（FFR）（以下简称 FFR）和基于机器学习算法的 CCTA 衍生的 FFR（以下简称 FFR）技术与 CCTA 和定量冠状动脉造影（QCA）的比较。
本单中心回顾性研究共纳入 85 例（平均年龄 62 岁±11[标准差]；62%为男性）行 CCTA 后行有创 FFR 的患者。分别使用 FFR 和 FFR 从 CCTA 数据集现场获得 FFR 值。比较两种技术在检测病变特异性缺血方面的性能，将 CCTA 的视觉狭窄分级、QCA 和有创 FFR 作为参考标准。
在病变水平和患者水平上，FFR 检测病变特异性缺血的敏感性分别为 79%和 90%，特异性分别为 94%和 95%。同时，FFR 在病变水平和患者水平上的敏感性分别为 79%和 89%，特异性分别为 93%和 93%（P=.86 和 P=.92）。在病变水平上，FFR 和 FFR 的受试者工作特征曲线下面积（AUC）为 0.89，明显高于 CCTA（AUC，0.61）和 QCA（AUC，0.69）的鉴别能力（均 P<.0001）。同样，在患者水平上，FFR（AUC，0.91）和 FFR（AUC，0.91）的表现明显优于 CCTA（AUC，0.65）和 QCA（AUC，0.68）（均 P<.0001）。FFR 的处理时间明显短于 FFR（40.5 分钟±6.3 比 43.4 分钟±7.1；P=.042）。
与 FFR 方法相比，FFR 算法在检测病变特异性缺血方面具有相同的效果。两种方法在检测血流受限性狭窄方面均优于 CCTA 和 QCA 的准确性。
