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 Hangel1,3, Eva Niess1, Maxim Zaitsev4, Simon Robinson1, Georg Langs2, Siegfried Trattning1,5, 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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