Abstract #3542

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

Francesco Cognolato^1,2, Steffen Bollmann^1,3, and Markus Barth^1,3

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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