Reliability of respiratory-gated real-time two-dimensional cine incorporating deep learning reconstruction for the assessment of ventricular function in an adult population

This study aimed to assess the image quality and accuracy of respiratory-gated real-time two-dimensional (2D) cine incorporating deep learning reconstruction (DLR) for the quantification of biventricular volumes and function compared with those of the standard reference, that is, breath-hold 2D balanced steady-state free precession (bSSFP) cine, in an adult population. Twenty-four patients (15 men, mean age 50.7 ± 16.5 years) underwent cardiac magnetic resonance for clinical indications, and 2D DLR and bSSFP cine were acquired on the short-axis view. The image quality scores were based on three main criteria: blood-to-myocardial contrast, endocardial edge delineation, and presence of motion artifacts throughout the cardiac cycle. Biventricular end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), and left ventricular mass (LVM) were analyzed. The 2D DLR cine had significantly shorter scan time than bSSFP (41.0 ± 11.3 s vs. 327.6 ± 65.8 s; p < 0.0001). Despite an analysis of endocardial edge definition and motion artifacts showed significant impairment using DLR cine compared with bSSFP (p  0.05). Moreover, the linear regression yielded good agreement between the two techniques (r ≥ 0.76). However, the LVM was underestimated for DLR cine (109.8 ± 34.6 g) compared with that for bSSFP (116.2 ± 40.2 g; p = 0.0291). Respiratory-gated 2D DLR cine is a reliable technique that could be used in the evaluation of biventricular volumes and function in an adult population.