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Abstract #0349

Super-resolution algorithms for simultaneous 1H MRF/23Na MRI: Comparison between U-Net, PLS-regression, and hybrid methods

Gonzalo Gabriel Rodriguez1, Hector Lise de Moura1, Lauren O'Donnell1, Ravinder Regatte1,2, and Guillaume Madelin1,2
1Center for Biomedical Imaging, Radiology Department, NYU School of Medicine, New York, NY, United States, 2Vilcek Institute of Graduate Biomedical Sciences, NYU Langone Health, New York, NY, United States

Synopsis

Keywords: Data Processing, Non-Proton, Super-Resolution

We compared three algorithms to generate a high-resolution (HR) 23Na image from simultaneously-acquired low-resolution (HR) 23Na density-weighted MRI and HR 1H density, T1 and T2 maps from MRF in brain at 7 T: U-net, PLS-regression, and hybrid. The multi-scale structural similarity index between generated HR 23Na images and HR ground truth was higher than 0.95 for the three methods. Overall, the hybrid method showed better results, generating the sharpest HR image while keeping the highest similarity between the acquired and generated LR images.

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Abstract Presentation Video

Keywords