Abstract #1873

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

Synopsis

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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