From the Institute of Diagnostic and Interventional Radiology (Y.L., M.Y., X.D., J.Z.) and Department of Cardiology (Z.L., C.S.), Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China 200233; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China (Y.W.); and Department of Radiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (B.L.).
Radiology. 2019 Nov;293(2):305-314. doi: 10.1148/radiol.2019190098. Epub 2019 Sep 24.
Background Direct intraindividual comparison of dynamic CT myocardial perfusion imaging (MPI) and machine learning (ML)-based CT fractional flow reserve (FFR) has not been explored for diagnosing hemodynamically significant coronary artery disease. Purpose To investigate the diagnostic performance of dynamic CT MPI and ML-based CT FFR for functional assessment of coronary stenosis. Materials and Methods Between January 2, 2017, and October 17, 2018, consecutive participants with stable angina were prospectively enrolled. All participants underwent dynamic CT MPI coronary CT angiography and invasive conventional coronary angiography (CCA) FFR within 2 weeks. Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic performance. Results Eighty-six participants (mean age, 67 years ± 12 [standard deviation]; 67 men) with 157 target vessels were included for final analysis. The mean radiation doses for dynamic CT MPI and coronary CT angiography were 3.6 mSv ± 1.1 and 2.7 mSv ± 0.8, respectively. Myocardial blood flow (MBF) was lower in ischemic segments compared with nonischemic segments and reference segments (defined as the territory of vessels without stenosis) (75 mL/100 mL/min ± 20 vs 148 mL/100 mL/min ± 22 and 169 mL/100 mL/min ± 34, respectively, both < .001). Similarly, CT FFR was also lower for hemodynamically significant lesions than for hemodynamically nonsignificant lesions (0.68 ± 0.1 vs 0.83 ± 0.1, respectively, < .001). MBF had the largest area under the ROC curve (AUC) (using 99 mL/100 mL/min as a cutoff) among all parameters, outperforming ML-based CT FFR (AUC = 0.97 vs 0.85, < .001). The vessel-based specificity and diagnostic accuracy of MBF were higher than those of ML-based CT FFR (93% vs 68%, < .001 and 94% vs 78%, respectively, = .04) whereas the sensitivity of both methods was similar (96% vs 88%, respectively, = .11). Conclusion Dynamic CT myocardial perfusion imaging was able to help accurately evaluate the hemodynamic significance of coronary stenosis using a reduced amount of radiation. In addition, the myocardial blood flow derived from dynamic CT myocardial perfusion imaging outperformed machine learning-based CT fractional flow reserve for identifying lesions causing ischemia. © RSNA, 2019 See also the editorial by Loewe in this issue.
背景 直接对动态 CT 心肌灌注成像（MPI）和基于机器学习（ML）的 CT 血流储备分数（FFR）进行个体内比较，尚未用于诊断有血流动力学意义的冠状动脉疾病。目的 探讨动态 CT MPI 和基于 ML 的 CT FFR 对冠状动脉狭窄功能评估的诊断性能。
材料与方法 2017 年 1 月 2 日至 2018 年 10 月 17 日，连续前瞻性纳入有稳定型心绞痛的参与者。所有参与者在 2 周内接受动态 CT MPI 冠状动脉 CT 血管造影和有创常规冠状动脉造影（CCA）FFR。使用受试者工作特征（ROC）曲线分析评估诊断性能。
结果 最终纳入 86 名参与者（平均年龄，67 岁±12[标准差]；67 名男性）和 157 个靶血管进行最终分析。动态 CT MPI 和冠状动脉 CT 血管造影的平均辐射剂量分别为 3.6 mSv±1.1 和 2.7 mSv±0.8。与非缺血节段和参照节段（定义为无狭窄血管的区域）相比，缺血节段的心肌血流（MBF）更低（分别为 75 mL/100 mL/min±20 与 148 mL/100 mL/min±22 和 169 mL/100 mL/min±34，均 <.001）。同样，有血流动力学意义的病变 CT FFR 也低于无血流动力学意义的病变（分别为 0.68±0.1 与 0.83±0.1，均 <.001）。MBF 在所有参数中具有最大的 ROC 曲线下面积（AUC）（以 99 mL/100 mL/min 作为截断值），优于基于 ML 的 CT FFR（AUC=0.97 与 0.85， <.001）。MBF 的血管基础特异性和诊断准确性均高于基于 ML 的 CT FFR（93%与 68%，均 <.001 和 94%与 78%， =.04），而两种方法的敏感性相似（96%与 88%， =.11）。
结论 采用低剂量辐射，动态 CT 心肌灌注成像能够准确评估冠状动脉狭窄的血流动力学意义。此外，基于动态 CT 心肌灌注成像的心肌血流在识别导致缺血的病变方面优于基于机器学习的 CT 血流储备分数。
