  Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging
inverse problem driven by ill conditioning of its field-to -susceptibility
transformation. State-of-art QSM reconstruction methods either suffer from
image artifacts or long computation times, which limits QSM clinical
translation efforts. To overcome these limitations, a non-locally
encoder-decoder gated convolutional neural network is trained to infer whole
brain susceptibility map, using the local field and brain mask as the inputs.
The performance of the proposed method is evaluated relative to synthetic data,
a publicly available challenge dataset, and clinical datasets. The proposed
approach can outperform existing methods on quantitative metrics and visual
assessment of image sharpness and streaking artifacts. The estimated
susceptibility maps can preserve conspicuity of fine features and suppress
streaking artifacts. The demonstrated methods have potential value in advancing
QSM clinical research and aiding in the translation of QSM to clinical
operations.
