  Quantitative susceptibility mapping (QSM) is a post-processing technique for
deriving tissue magnetic susceptibility distribution from MRI phase
measurements. Deep learning (DL) algorithms hold great potential for solving
the ill-posed QSM reconstruction problem. However, a significant challenge
facing current DL-QSM approaches is their limited adaptability to magnetic
dipole field orientation variations during training and testing. In this work,
we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module
to learn the encoding of acquisition orientation vectors and seamlessly
integrate them into the latent features of deep networks. Importantly, it can
be directly Plug-and-Play (PnP) into various existing DL-QSM architectures,
enabling reconstructions of QSM from arbitrary magnetic dipole orientations.
Its effectiveness is demonstrated by combining the OA-LFE module into our
previously proposed phase-to-susceptibility single-step instant QSM (iQSM)
network, which was initially tailored for pure-axial acquisitions. The proposed
OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a
self-supervised manner on a specially-designed simulation brain dataset.
Comprehensive experiments are conducted on simulated and in vivo human brain
datasets, encompassing subjects ranging from healthy individuals to those with
pathological conditions. These experiments involve various MRI platforms (3T
and 7T) and aim to compare our proposed iQSM+ against several established QSM
reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM
images with significantly improved accuracies and mitigates artifacts,
surpassing other state-of-the-art DL-QSM algorithms.
