  Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to
estimate local tissue susceptibility, which has been shown useful to provide
novel image contrast and as biomarkers of abnormal tissue. QSM requires
addressing a challenging post-processing problem: filtering of image phase
estimates and inversion of the phase to susceptibility relationship. A wide
variety of quantification errors, robustness limitations, and artifacts
constraints QSM clinical translation. To overcome these limitations, a robust
deep-learning-based QSM reconstruction approach is proposed to perform
background field removal and susceptibility inversion simultaneously from input
MRI phase images. Synthetic training data based on in-vivo data sources and
physics simulations were used for training. The network was quantitatively
tested using gold-standard in-silico labeled dataset against established
background field removal and QSM inversion approaches. In addition, the
algorithm was applied to a QSM challenge data and clinical
susceptibility-weighted imaging (SWI) data. When quantitatively compared
against gold-standard in-silico labels, the proposed algorithm outperformed the
existing comparable background field removal approaches and QSM reconstruction
algorithms. The QSM challenge data and clinical SWI data demonstrated that the
proposed approach was able to robustly generate high quality local field and
QSM with improved accuracy.
