LAUSR

Objective To investigate the effect of image quality of coronary CT angiography (CCTA) on the diagnostic performance of a machine learning–based CT-derived fractional flow reserve (FFR CT ). Methods This nationwide retrospective study enrolled participants from 10 individual centers across China. FFR CT analysis was performed in 570 vessels in 437 patients. Invasive FFR and FFR CT values ≤ 0.80 were considered ischemia-specific. Four-score subjective assessment based on image quality and objective measurement of vessel enhancement was performed on a per-vessel basis. The effects of body mass index (BMI), sex, heart rate, and coronary calcium score on the diagnostic performance of FFR CT were studied. Results Among 570 vessels, 216 were considered ischemia-specific by invasive FFR and 198 by FFR CT . Sensitivity and specificity of FFR CT for detecting lesion-specific ischemia were 0.82 and 0.93, respectively. Area under the curve (AUC) of high-quality images (0.93, n = 159) was found to be superior to low-quality images (0.80, n = 92, p = 0.02). Objective image quality and heart rate were also associated with diagnostic performance of FFR CT , whereas there was no statistical difference in diagnostic performance among different BMI, sex, and calcium score groups (all p > 0.05, Bonferroni correction). Conclusions This retrospective multicenter study supported the FFR CT as a noninvasive test in evaluating lesion-specific ischemia. Subjective image quality, vessel enhancement, and heart rate affect the diagnostic performance of FFR CT . Key Points • FFR CT can be used to evaluate lesion-specific ischemia. • Poor image quality negatively affects the diagnostic performance of FFR CT . • CCTA with ≥ score 3, intracoronary enhancement degree of 300–400 HU, and heart rate below 70 bpm at scanning could be of great benefit to more accurate FFR CT analysis.