  Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging
(MRI) technique that estimates magnetic susceptibility of tissue from Larmor
frequency offset measurements. The generation of QSM requires solving a
challenging ill-posed field-to-source inversion problem. Inaccurate
field-to-source inversion often causes large susceptibility estimation errors
that appear as streaking artifacts in the QSM, especially in massive
hemorrhagic regions. 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 DL techniques used either
calculation of susceptibility through multiple orientation sampling (COSMOS)
maps or synthetic data for network training. Therefore, they were constrained
by the availability and accuracy of COSMOS maps, or suffered from performance
drop when the training and testing domains were different. To address these
limitations, we present a model-based DL method, denoted as uQSM. Without
accessing to QSM labels, uQSM is trained using the well-established physical
model. When evaluating on multi-orientation QSM datasets, uQSM achieves higher
levels of quantitative accuracy compared to TKD, TV-FANSI, MEDI, and DIP
approaches. When qualitatively evaluated on single-orientation datasets, uQSM
outperforms other methods and reconstructed high quality QSM.
