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.