High-resolution automated free-breathing coronary magnetic resonance angiography in comparison with coronary computed tomography angiography.

Aims: Clinical implementation of coronary magnetic resonance angiography (CMRA) is limited due to variability in image quality. A protocol utilizing an image navigator (iNAV) integrated with automated scan planning has been developed to facilitate consistent diagnostic image quality. The aim of this study was to evaluate the agreement of automated iNAV CMRA compared with coronary computed tomography angiography (CCTA) using Coronary Artery Disease-Reporting and Data System (CAD-RADS) to classify coronary artery disease (CAD).

Methods and results: Ninety-five individuals underwent automated iNAV CMRA at a resolution of 0.7 mm³ with a deep learning-assisted automated scan planning and trigger-delay detection protocol. CMRA and CCTA data sets were analysed using CAD-RADS to classify the per-patient severity of CAD. Additionally, the accuracy of both imaging modalities in predicting referral for invasive coronary angiography (ICA) and coronary revascularization was assessed. CMRA classification for CAD-RADS ≥ 1, ≥2, ≥3, and ≥4 agreed with CCTA for 80%, 73%, 63%, and 70% of cases, respectively. The area under the receiver operating characteristic curves with CAD-RADS ≥ 4 and ≥3 for CMRA and CCTA were comparable in predicting ICA referral (0.75 vs. 0.70, P = 0.687, and 0.70 vs. 0.70, P = 0.945) and revascularization (0.72 vs. 0.74, P = 0.811, and 0.68 vs. 0.76, P = 0.089).

Conclusion: A novel automated iNAV CMRA protocol was implemented, investigating individuals at risk of CAD. Using the CAD-RADS classification, there was moderate to good agreement between CMRA and CCTA. In patients with CAD-RADS ≥ 4 and ≥3, CMRA was as effective as CCTA in predicting ICA referral and revascularization.