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Quantitative susceptibility mapping (QSM) is a powerful MRI technique thathas shown great potential in quantifying tissue susceptibility in numerousneurological disorders. However, the intrinsic ill-posed dipole inversionproblem greatly affects the accuracy of the susceptibility map. We proposeQSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Netarchitecture with increased receptive field of the input phase compared to theoutput and further refined the network using the WGAN with gradient penaltytraining strategy. Our method generates accurate QSM maps from singleorientation phase maps efficiently and performs significantly better thantraditional non-learning-based dipole inversion algorithms. The generalizationcapability was verified by applying the algorithm to an unseen pathology--braintumor patients with radiation-induced cerebral microbleeds.

QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field