Untrained Network for Super-resolution for Non-contrast-enhanced Wholeheart MRI Acquired using Cardiac-triggered REACT (SRNN-REACT)

Abstract

Background: Three-dimensional (3D) whole-heart magnetic resonance imaging (MRI) is an excellent tool to check the heart anatomy of patients with congenital and acquired heart disease. However, most 3D whole-heart MRI acquisitions take a long time to perform, and the sequence used is susceptible to banding artifacts.

Purpose: To validate an unsupervised neural network that can reduce acquisition time and improve image quality for 3D whole-heart MRI by superresolving low-resolution images.

Methods: The results of the super-resolution neural network (SRNN) were compared with bilinear interpolation, a state-of-the-art method known as AdapSR, and the ground truth high-resolution images qualitatively and quantitatively. Thirty pediatric patients with varying congenital and acquired heart diseases were included in this study. Results from the SRNN without a ground truth image were compared qualitatively with the contrast-enhanced whole-heart images. Signal-to-noise ratio (SNR) was used to quantitatively compare each of the methods and the high-resolution ground truth.

Results: As confirmed by both the quantitative and qualitative results, the SRNN improves image quality. Furthermore, because it only requires a lowresolution acquisition, the use of the SRNN reduces acquisition time.

Conclusion: The SRNN lessens noise and eliminates artifacts while maintaining correct anatomical structure in the images.

Keywords: 3D whole-heart MRI; Deep neural network; Non-contrast imaging; Non-contrast magnetic resonance angiograpy.; SRNN; Super-resolution; Unsupervised learning.