  Quantitative susceptibility mapping (QSM) estimates the underlying tissue
magnetic susceptibility from the MRI gradient-echo phase signal and has
demonstrated great potential in quantifying tissue susceptibility in various
brain diseases. However, the intrinsic ill-posed inverse problem relating the
tissue phase to the underlying susceptibility distribution affects the accuracy
for quantifying tissue susceptibility. The resulting susceptibility map is
known to suffer from noise amplification and streaking artifacts. To address
these challenges, we propose a model-based framework that permeates benefits
from generative adversarial networks to train a regularization term that
contains prior information to constrain the solution of the inverse problem,
referred to as MoG-QSM. A residual network leveraging a mixture of
least-squares (LS) GAN and the L1 cost was trained as the generator to learn
the prior information in susceptibility maps. A multilayer convolutional neural
network was jointly trained to discriminate the quality of output images.
MoG-QSM generates highly accurate susceptibility maps from single orientation
phase maps. Quantitative evaluation parameters were compared with recently
developed deep learning QSM methods and the results showed MoG-QSM achieves the
best performance. Furthermore, a higher intraclass correlation coefficient
(ICC) was obtained from MoG-QSM maps of the traveling subjects, demonstrating
its potential for future applications, such as large cohorts of multi-center
studies. MoG-QSM is also helpful for reliable longitudinal measurement of
susceptibility time courses, enabling more precise monitoring for metal ion
accumulation in neurodegenerative disorders.
