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Abstract #1054

Data scarcity mitigation approaches in deep learning reconstruction of undersampled low field MR images

Tobias Senft1, Reina Ayde1, Najat Salameh1, and Mathieu Sarracanie1
1Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland


Low magnetic field (LF) MRI is gaining popularity as a flexible and cost-effective complement to conventional MRI. However, LF-MRI suffers from a low signal-to-noise ratio per unit time which calls for signal averaging and hence prolonged acquisition times, challenging the clinical value of LF MRI.

In this study, we show that Deep Learning (DL) can reconstruct artifact-free heavily undersampled 2D LF MR images (34% sampling) with great success, both retrospectively and prospectively. Our results also highlight that a transfer learning approach combined with data augmentation improves the overall reconstruction performances, even when only small LF training datasets are available.

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