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Abstract #2192

Deep learning for fast 3D low field MRI

Reina Ayde1, Tobias Senft1, Najat Salameh1, and Mathieu Sarracanie1
1Center for Adaptable MRI Technology (AMT Center), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland

Low magnetic field (LF) MRI is currently gaining momentum as a complementary, more flexible and cost-effective approach to MRI diagnosis. However, the impaired Signal-to-Noise Ratio, leading in turn to prolonged acquisition times, challenges its relevance at the clinical level. Recently, reconstructing an alias-free image using deep learning techniques has shown promising results. In this study, we leverage deep learning reconstruction to demonstrate the feasibility of highly undersampled (20% sampling) 3D LF MRI at 0.1 T. The model performance has been evaluated on both retrospective and acquired, prospective 3D LF data.

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