Feasibility of accelerated non-contrast-enhanced whole-heart bSSFP coronary MRA by deep learning constrained compressed sensing

The balanced steady-state free precession (bSSFP) sequence is widely used for navigated whole-heart coronary magnetic resonance angiography (MRA) for the evaluation of coronary anatomy and abnormalities due to its inherently high blood signal intensity and blood-myocardial contrast. However, the main drawback of this approach is that the scan time is longer and prone to interference with motion artifacts. In this study, we investigated the utility of whole-heart coronary MRA using accelerated bSSFP with compressed sensing artificial intelligence (CSAI) technique at 3 Tesla. The results demonstrated the adopted CS-AI technique yielded high image quality within a clinically feasible acquisition time in healthy subjects and patients with suspected CAD.