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 < 0.01), the two sequences demonstrated no significant difference in terms of biventricular EDV, ESV, SV, and EF (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.