Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.

Abstract

Objective: The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency of the pre-and post-contrast performance of 2D deep learning-based highly accelerated cardiac cine (DL cine) imaging by evaluating the image quality and the quantification of biventricular volumes and function in the clinical setting.

Material and methods: Thirty patients (20 men, mean age 53.7 ± 17.8 years) underwent cardiac magnetic resonance on a 1.5 T scanner for clinical indications, and pre- and post-contrast DL cine images were acquired with a short-axis view. Image-quality was scored according to three main criteria: the 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 and compared between the pre- and post-contrast DL cine images.

Results: The actual median time of 2D DL cine acquisition was 38.4 ± 9.1 s. There were no significant differences in the image quality scores between pre- and post-contrast DL cine images (p > 0.05). In the volume and functional analysis, there was no significant difference in terms of biventricular EDV, ESV, SV, EF, and LVM (p > 0.05).

Conclusions: The performance of 2D DL cine is equivalent before and after contrast injection for the assessment of image quality and ventricular function in the clinical setting.