Detection of Hemodynamically Significant Coronary Stenosis: CT Myocardial Perfusion versus Machine Learning CT Fractional Flow Reserve

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 P < .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, P < .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, P < .001). The vessel-based specificity and diagnostic accuracy of MBF were higher than those of ML-based CT FFR (93% vs 68%, P < .001 and 94% vs 78%, respectively, P = .04) whereas the sensitivity of both methods was similar (96% vs 88%, respectively, P = .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 Online supplemental material is available for this article. See also the editorial by Loewe in this issue.