Title:Untrained Network for Super-resolution for Non-contrast-enhanced Wholeheart MRI Acquired using Cardiac-triggered REACT (SRNN-REACT)
Volume: 20
Author(s): Corbin Maciel, Tayaba Miah and Qing Zou*
Affiliation:
- Division of Pediatric Cardiology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, USA
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, USA
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, USA
Keywords:
Super-resolution, Deep neural network, Non-contrast imaging, 3D whole-heart MRI, Unsupervised learning, SRNN, Non-contrast magnetic resonance angiograpy.
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.
