Abstract #0966

Predicting changes in B0 field due to patient movement dynamically for MRI via Deep Learning

Stanislav Motyka¹, Paul Weiser², Beata Bachrata¹, Lukas Hingerl¹, Bernhard Strasser¹, Dario Goranovic¹, Gilbert Hangel^1,3, Eva Niess¹, Maxim Zaitsev⁴, Simon Robinson¹, Georg Langs², Siegfried Trattning^1,5, and Wolfgang Bogner¹

¹High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ²Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ³Department of Neurosurgery, Medical University of Vienna, Vienna, Austria, ⁴High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria, ⁵Christian Doppler Laboratory for Clinical Molecular MR Imaging, Medical University of Vienna, Vienna, Austria

Synopsis

We proposed a U-net-based neural network to predict changes in B0 field due to patient movement. The network was trained and tested on 7T in vivo volunteer data. The method was compared against two other approaches. The estimated B0 maps were compared against the ground truth. The results suggest that prediction of B0 maps is feasible and by combination with external tracking a considerable improvement in data quality of B0-sensitive MRI methods could be achieved.

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