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Abstract #4282

Arbitrary Factor Super-Resolution for 3D Whole-Heart MRI Using a Frequency-Domain Informed Neural Network

Corbin Maciel1 and Qing Zou2
1Biomedical Engineering, University of Texas Southwestern Medical Center, Fort Worth, TX, United States, 2Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, United States

Synopsis

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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Keywords