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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