  Deep neural networks have demonstrated great potential in solving dipole
inversion for Quantitative Susceptibility Mapping (QSM). However, the
performances of most existing deep learning methods drastically degrade with
mismatched sequence parameters such as acquisition orientation and spatial
resolution. We propose an end-to-end AFfine Transformation Edited and Refined
(AFTER) deep neural network for QSM, which is robust against arbitrary
acquisition orientation and spatial resolution up to 0.6 mm isotropic at the
finest. The AFTER-QSM neural network starts with a forward affine
transformation layer, followed by an Unet for dipole inversion, then an inverse
affine transformation layer, followed by a Residual Dense Network (RDN) for QSM
refinement. Simulation and in-vivo experiments demonstrated that the proposed
AFTER-QSM network architecture had excellent generalizability. It can
successfully reconstruct susceptibility maps from highly oblique and
anisotropic scans, leading to the best image quality assessments in simulation
tests and suppressed streaking artifacts and noise levels for in-vivo
experiments compared with other methods. Furthermore, ablation studies showed
that the RDN refinement network significantly reduced image blurring and
susceptibility underestimation due to affine transformations. In addition, the
AFTER-QSM network substantially shortened the reconstruction time from minutes
using conventional methods to only a few seconds.
