The influence of image quality on diagnostic performance of a machine learning-based fractional flow reserve derived from coronary CT angiography.

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).

METHODS

This nationwide retrospective study enrolled participants from 10 individual centers across China. FFR analysis was performed in 570 vessels in 437 patients. Invasive FFR and FFR 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 were studied.

RESULTS

Among 570 vessels, 216 were considered ischemia-specific by invasive FFR and 198 by FFR. Sensitivity and specificity of FFR 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, 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 as a noninvasive test in evaluating lesion-specific ischemia. Subjective image quality, vessel enhancement, and heart rate affect the diagnostic performance of FFR.

KEY POINTS

• FFRcan be used to evaluate lesion-specific ischemia. • Poor image quality negatively affects the diagnostic performance of FFR. • 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 FFRanalysis.