  Quantitative susceptibility mapping (QSM) is a powerful MRI technique that
has shown great potential in quantifying tissue susceptibility in numerous
neurological disorders. However, the intrinsic ill-posed dipole inversion
problem greatly affects the accuracy of the susceptibility map. We propose
QSMGAN: a 3D deep convolutional neural network approach based on a 3D U-Net
architecture with increased receptive field of the input phase compared to the
output and further refined the network using the WGAN with gradient penalty
training strategy. Our method generates accurate QSM maps from single
orientation phase maps efficiently and performs significantly better than
traditional non-learning-based dipole inversion algorithms. The generalization
capability was verified by applying the algorithm to an unseen pathology--brain
tumor patients with radiation-induced cerebral microbleeds.
