Unsupervised Neural Network for Super-Resolving Non-Contrast-Enhanced Whole-Heart MRI Using REACT

Motivation: Three-dimensional (3D) whole-heart MRI requires long scan times and the sequence used to acquire such scans is susceptible to banding artifacts. Goal(s): The goal of this study was to develop an unsupervised super-resolution neural network for 3D whole-heart MRI. Approach: The data used in this study was acquired using a modified Relaxation-Enhanced Angiography without Contrast and Triggering (REACT) sequence. A neural network referred to hereafter as the Super-resolution Neural Network (SRNN) was developed to super-resolve 3D MRI data. Results: The SRNN allows us to acquire lower-resolution scans, thus decreasing scan time, and provides improved image quality after performing super-resolution. Impact: The results of this study show that super-resolution offers a viable option to decrease scan time and improve overall image quality in 3D whole-heart MRI.