  Deep neural networks have demonstrated promising potential for the field of
medical image reconstruction. In this work, an MRI reconstruction algorithm,
which is referred to as quantitative susceptibility mapping (QSM), has been
developed using a deep neural network in order to perform dipole deconvolution,
which restores magnetic susceptibility source from an MRI field map. Previous
approaches of QSM require multiple orientation data (e.g. Calculation of
Susceptibility through Multiple Orientation Sampling or COSMOS) or
regularization terms (e.g. Truncated K-space Division or TKD; Morphology
Enabled Dipole Inversion or MEDI) to solve the ill-conditioned deconvolution
problem. Unfortunately, they either require long multiple orientation scans or
suffer from artifacts. To overcome these shortcomings, a deep neural network,
QSMnet, is constructed to generate a high quality susceptibility map from
single orientation data. The network has a modified U-net structure and is
trained using gold-standard COSMOS QSM maps. 25 datasets from 5 subjects (5
orientation each) were applied for patch-wise training after doubling the data
using augmentation. Two additional datasets of 5 orientation data were used for
validation and test (one dataset each). The QSMnet maps of the test dataset
were compared with those from TKD and MEDI for image quality and consistency in
multiple head orientations. Quantitative and qualitative image quality
comparisons demonstrate that the QSMnet results have superior image quality to
those of TKD or MEDI and have comparable image quality to those of COSMOS.
Additionally, QSMnet maps reveal substantially better consistency across the
multiple orientations than those from TKD or MEDI. As a preliminary
application, the network was tested for two patients. The QSMnet maps showed
similar lesion contrasts with those from MEDI, demonstrating potential for
future applications.
