Keywords: Other AI/ML, Machine Learning/Artificial Intelligence, Super-resolution, Heart
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.
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