  Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance
imaging (MRI) contrast mechanism that has demonstrated broad clinical
applications. However, the image reconstruction of QSM is challenging due to
its ill-posed dipole inversion process. In this study, a new deep learning
method for QSM reconstruction, namely xQSM, was designed by introducing
modified state-of-the-art octave convolutional layers into the U-net backbone.
The xQSM method was compared with recentlyproposed U-net-based and conventional
regularizationbased methods, using peak signal to noise ratio (PSNR),
structural similarity (SSIM), and region-of-interest measurements. The results
from a numerical phantom, a simulated human brain, four in vivo healthy human
subjects, a multiple sclerosis patient, a glioblastoma patient, as well as a
healthy mouse brain showed that the xQSM led to suppressed artifacts than the
conventional methods, and enhanced susceptibility contrast, particularly in the
ironrich deep grey matter region, than the original U-net, consistently. The
xQSM method also substantially shortened the reconstruction time from minutes
using conventional iterative methods to only a few seconds.
