  Quantitative susceptibility mapping (QSM) is a MRI technique that estimates
tissue magnetic susceptibility. The generation of QSM requires solving a
challenging ill-posed field-to-source inversion problem. Recently, several deep
learning (DL) QSM techniques have been proposed and demonstrated impressive
performance. Due to the inherent non-existent ground-truth QSM references,
these techniques used either COSMOS maps or synthetic data for network
training. Synthetic data is easy to generate but often adapt poorly to in-vivo
data due to domain shifts. Here, we introduce an easy domain adaptation
technique using domain-specific batch normalization to address this problem.
