  Quantitative susceptibility mapping (QSM) is an MRI phase-based
post-processing method that quantifies tissue magnetic susceptibility
distributions. However, QSM acquisitions are relatively slow, even with
parallel imaging. Incoherent undersampling and compressed sensing
reconstruction techniques have been used to accelerate traditional
magnitude-based MRI acquisitions; however, most do not recover the full phase
signal due to its non-convex nature. In this study, a learning-based Deep
Complex Residual Network (DCRNet) is proposed to recover both the magnitude and
phase images from incoherently undersampled data, enabling high acceleration of
QSM acquisition. Magnitude, phase, and QSM results from DCRNet were compared
with two iterative and one deep learning methods on retrospectively
undersampled acquisitions from six healthy volunteers, one intracranial
hemorrhage and one multiple sclerosis patients, as well as one prospectively
undersampled healthy subject using a 7T scanner. Peak signal to noise ratio
(PSNR), structural similarity (SSIM) and region-of-interest susceptibility
measurements are reported for numerical comparisons. The proposed DCRNet method
substantially reduced artifacts and blurring compared to the other methods and
resulted in the highest PSNR and SSIM on the magnitude, phase, local field, and
susceptibility maps. It led to 4.0% to 8.8% accuracy improvements in deep grey
matter susceptibility than some existing methods, when the acquisition was
accelerated four times. The proposed DCRNet also dramatically shortened the
reconstruction time by nearly 10 thousand times for each scan, from around 80
hours using conventional approaches to only 30 seconds.
