Benchmarking Accelerated MRI: A Head-to-Head Comparison of Deep Learning Reconstruction and Super-Resolution Techniques
Eric K. Gibbons1, Zhongnan Fang2, Arjun D. Desai3, Christopher M. Sandino3, Garry E. Gold4, Brian A. Hargreaves4, and Akshay S. Chaudhari4
1Department of Electrical and Computer Engineering, Weber State University, Ogden, UT, United States, 2Lvis Corporation, Palo Alto, CA, United States, 3Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 4Department of Radiology, Stanford University, Stanford, CA, United States
Deep-learning (DL) can be used to extend compressed sensing (CS) to learn the regularization function in a data-driven manner. In contrast, super resolution (SR) algorithms have been used to transform rapidly-acquired low-resolution images into higher-resolution images. This work compares DL-CS with DL-SR for accelerated MRI on a test dataset of 50 patients with conventional image quality metrics and clinically-relevant quantitative T2 relaxation measurements. We demonstrate that DLCS approaches outperform DLSR approaches for accelerated MRI.
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