Diagnostic performance of myocardial blood flow quantification in coronary artery disease by magnetic resonance.

Background Mathematical modelling of magnetic resonance (MR) perfusion imaging data allows myocardial blood flow (MBF) quantification and can potentially improve the diagnosis and prognostication of obstructive coronary artery disease (CAD). The diagnostic performance of distributed parameter (DP) modelling in detecting obstructive CAD has not yet been assessed. A model assessment in per vessel against per patient analysis has not been fully assessed yet in a single MR study. This work compares the diagnostic performance of DP modelling against the standard Fermi modelling, for the detection of obstructive CAD, in per vessel against per patient analysis. Methods After informed consent, a pilot cohort of 28 subjects with known or suspected CAD underwent adenosine stress-rest magnetic resonance perfusion imaging at 3T. Data were analysed using Fermi and DP modelling against invasive coronary angiography and fractional flow reserve, acquired in all subjects. Obstructive CAD was defined as luminal stenosis of ≥70% alone, or luminal stenosis ≥50% and fractional flow reserve ≤0.80. Results On ROC analysis, the diagnostic performance of all methods was improved in per patient analysis. DP modelling outperformed the standard Fermi model, in per vessel and per patient analysis. In per patient analysis, DP modelling-derived MBF at stress demonstrated the highest sensitivity and specificity (0.96, 0.92) in detecting obstructive CAD, against Fermi modelling (0.78, 0.88) and visual assessments (0.79, 0.88), respectively. Conclusions DP modelling consistently outperformed Fermi modelling and showed that it may have merit for robustly stratifying patients with at least one vessel with obstructive CAD