1. Detection of Hemodynamically Significant Coronary Stenosis: CT Myocardial Perfusion versus Machine Learning CT Fractional Flow Reserve
Myocardial blood flow (MBF) derived from CT myocardial perfusion imaging (MPI) was lower in ischemic segments than in nonischemic segments (75 vs 148 mL/100 mL/min; P < .001).
MBF outperformed machine learning (ML)–based coronary CT fractional flow reserve (FFR) for identifying flow-limiting lesions (AUC = 0.97 vs 0.85; P < .001).
The vessel-based specificity and diagnostic accuracy of MBF were higher than those of ML-based coronary CT angiography–derived 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).
A B
In a 70-year-old with chest pain, A, ML-based CT FFR revealed simulated FFR value of 0.63 in the distal left anterior descending (LAD) artery and, B, CT MPI depicted reduction of MBF within middle anterior wall of LAD territory.
Li Y et al et al. Published Online: September 24, 2019