Abstract #3975

Deep learning based quantitative susceptibility mapping (QSM) in the presence of fat by using synthetically generated multi-echo phase data

Jannis Hanspach¹, Aurel Jolla¹, Michael Uder¹, Bernhard Hensel², Steffen Bollmann³, and Frederik Bernd Laun¹

¹Institute of Radiology, University Hospital Erlangen, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU), Erlangen, Germany, ²Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Center for Medical Physics and Engineering, Erlangen, Germany, ³University of Queensland, Brisbane, Australia, School of Information Technology and Electrical Engineering, Brisbane, Australia

Deep Learning reconstruction methods are increasingly investigated in Quantitative Susceptibility Mapping (QSM). In this work, we applied a UNET to reconstruct susceptibility maps in the presence of fat from unwrapped phase maps. The network was trained using synthetically generated multi-echo phase data and does not require explicit masking for the background field correction. Our results show that the proposed approach is well-suited to rapidly reconstruct high quality susceptibility maps in the presence of fat (e.g., outside the central nervous system) in in vivo data.

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