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Abstract #1971

Residual U-net for denoising 3D low field MRI

Tobias Senft1, Reina Ayde1, Marco Fiorito1, Najat Salameh1, and Mathieu Sarracanie1
1Department 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 challenges its relevance for clinical applications. Recently, denoising of low SNR images using deep learning techniques has shown promising results for MRI applications. In this study, we assess the denoising performance of residual U-net architecture on different SNR levels of LF MRI data (0.1 T). The model performance has been evaluated on both simulated and acquired LF MRI datasets.

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