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Abstract #3542

QSMResGAN - Dipole inversion for quantitative susceptibilitymapping using conditional Generative Adversarial Networks

Francesco Cognolato1,2, Steffen Bollmann1,3, and Markus Barth1,3
1The University of Queensland, Brisbane, Australia, 2Technical University of Munich, Munich, Germany, 3ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia

In our abstract we present QSMResGAN, a conditional Generative Adversarial Network (cGAN) with a novel architecture for the generator (ResUNet), trained only with simulated data of different shapes to solve the dipole inversion problem for quantitative susceptibility mapping (QSM). The network has been compared with other state-of-the-art QSM methods on the QSM challenge 2.0 and on in vivo data.

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