Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Cardiovascular, Super-resolution
Motivation: Three-dimensional (3D) whole-heart MRI is important in diagnosing congenital heart disease. However, it requires long acquisition times, which can lead to patient discomfort and irregular motion.
Goal(s): The goal of this study is to develop a deep learning super-resolution (SR) method to decrease acquisition time, without degrading image quality.
Approach: The proposed method implements a frequency-domain regularization to inform training, with the framework also enabling arbitrary factor SR.
Results: The image quality metrics PSNR and SSIM show that the proposed method outperforms basic and state-of-the-art methods. Qualitative comparisons demonstrate that the proposed method better maintains diagnostically important, small anatomical structures.
Impact: By implementing frequency-domain regularization inform network training and arbitrary factor super-resolution, the proposed method offers the potential to decrease acquisition time in 3D whole-heart MRI, while maintaining fine image detail important for diagnostic utility.
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