Prognostic Significance of Targeted Magnetic Resonance Coronary Angiography to Predict Cardiac Events in Patients with Known or Suspected Coronary Artery Disease

Background: Magnetic resonance coronary angiography (MRCA) is one of the most promising tools for noninvasive imaging of coronary arteries without radiation exposure or contrast media administration. However, knowledge about the prognostic value of MRCA is limited. Materials and Methods: The present study included 389 patients with known or suspected coronary artery disease (CAD) underwent clinical cardiac magnetic resonance (CMR) including MRCA imaging. The presence of a significant coronary artery stenosis was defined by visual estimation of ≥50% diameter reduction using targeted MRCA images. Patients were followed up for hard cardiac events (cardiac death or non-fatal myocardial infarction) and major adverse cardiac events (MACE) which also included hospitalization for heart failure and late revascularization (>180 days after the CMR study). Results: The average age was 68±11 years and 48% were male. One hundred and thirty-nine patients had significant stenosis on MRCA. During a median follow-up period of 53.9 months, 23 hard cardiac events and 52 MACE, occurred. Patients with significant coronary artery stenosis had higher rates of hard cardiac events (annual event rate 3.12% versus 0.56%, HR 5.52, 95% CI 2.17 to 14.01, p<0.001) and MACE (annual event rate 6.44% versus 1.83%, HR 3.49, 95% CI 1.98 to 6.14, p<0.001) than those without significant stenosis. Multivariable analyses identified significant coronary artery stenosis as an independent predictor of hard cardiac events (HR 3.35, 95% CI 1.13 to 9.96, p=0.03) and MACE (HR 2.00, 95% CI 1.02 to 3.90, p=0.04). MRCA presented an incremental prognostic value over clinical factors, left ventricular ejection fraction, and myocardial scarring to predict hard cardiac events (p=0.03). Conclusion: Targeted MRCA demonstrated independent and incremental prognostic values to predict future cardiac events in patients with known or suspected CAD. Keywords: Cardiac magnetic resonance imaging; Coronary artery disease; Magnetic resonance coronary angiography; Prognosis

Effects of Deep Learning Reconstruction Technique in High-Resolution Non-contrast Magnetic Resonance Coronary Angiography at a 3-Tesla Machine

Purpose: To evaluate the effects of deep learning reconstruction (DLR) in qualitative and quantitative image quality of non-contrast magnetic resonance coronary angiography (MRCA). Methods: Ten healthy volunteers underwent conventional MRCA (C-MRCA) and high-resolution (HR) MRCA on a 3T magnetic resonance imaging with a voxel size of 1.8 × 1.1 × 1.7 mm3 and 1.8 × 0.6 × 1.0 mm3, respectively, for C-MRCA and HR-MRCA. High-resolution magnetic resonance coronary angiography was also reconstructed with the DLR technique (DLR-HR-MRCA). We compared the contrast-to-noise ratio (CNR) and visual evaluation scores for vessel sharpness and traceability of proximal and distal coronary vessels on a 4-point scale among 3 image series. Results: The vascular CNR value on the C-MRCA and the DLR-HR-MRCA was significantly higher than that on the HR-MRCA in the proximal and distal coronary arteries (13.9 ± 6.4, 11.3 ± 4.4, and 7.8 ± 2.6 for C-MRCA, DLR-HR-MRCA, and HR-MRCA, P < .05, respectively). Mean visual evaluation scores for the vessel sharpness and traceability of proximal and distal coronary vessels were significantly higher on the HR-DLR-MRCA than the C-MRCA ( P < .05, respectively). Conclusion: Deep learning reconstruction significantly improved the CNR of coronary arteries on HR-MRCA, resulting in both higher visual image quality and better vessel traceability compared with C-MRCA.