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Abstract #4028

Deep Quantitative Susceptibility Mapping by combined Background Field Removal and Dipole Inversion

Stefan Heber1, Christian Tinauer1, Steffen Bollmann2, Stefan Ropele1, and Christian Langkammer1

1Department of Neurology, Medical University of Graz, Graz, Austria, 2Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia

Deep learning based on u-shaped architectures has been successfully used as a means for the dipole inversion crucial to Quantitative Susceptibility Mapping (QSM). In the present work we propose a novel deep regression network by stacking two u-shaped networks and consequently both, the background field removal and the dipole inversion can be performed in a single feed forward network architecture. Based on learning the theoretical forward model using synthetic data examples, we show a proof-of-concept for solving the background field problem and dipole inversion in a single end-to-end trained network using in vivo Magnetic Resonance Imaging (MRI) data.

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