QSM Reconstruction of Arbitrary Dipole Orientations using an End-to-end Neural Network via Latent Feature Editing

Motivation: The performances of most DL-QSM methods are limited to MRI phase data of pure-axial acquisition orientation. Goal(s): In this work, we would like to propose a novel DL-based end-to-end neural network for QSM reconstruction from phase data of arbitrary dipole orientations. Approach: A novel Latent Feature Editing (LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks to make them orientation-adaptive. Results: Both simulated and in vivo experiments demonstrate that the proposed LFE module can result in desirable QSM images at arbitrary oblique head orientations. Impact: This work introduces a new DL paradigm, allowing researchers to develop innovative QSM methods without requiring a complete overhaul of their existing architectures.