Abstract #0347

Comparison of dynamic B0 mapping approaches at 7T – AI-based prediction via U-net vs measurement via dual-echo EPI navigator

Stanislav Motyka¹, Paul Weiser², Bernhard Strasser¹, Dario Goranovic¹, Lukas Hingerl¹, Gilbert Hangel^1,3, Eva Niess¹, Stephan Wampl⁴, Fabian Niess¹, Simon Robinson¹, Georg Langs², Siegfried Trattnig¹, and Wolfgang Bogner⁵

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, ⁵Medical University of Vienna, Vienna, Austria

Synopsis

Keywords: Motion Correction, BrainThe abstract compares two approaches to dynamically estimate B0 maps at 7T. The U-net predicts B0 maps based on the initial B0 map and the movement information. The dual-echo EPI-based navigator directly measures B0 maps. Both methods yield comparable results. However, the deep learning approach could overcome the major disadvantage of the EPI-based navigator, thus the need for the dead time in the parent sequence, if the motion can be mapped externally.

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