BACKGROUND: Quantitative susceptibility mapping (QSM) is a post-processing magnetic resonance imaging (MRI) technique that extracts the distribution of tissue susceptibilities and holds significant promise in the study of neurological diseases. However, the ill-conditioned nature of dipole inversion often results in noise and artifacts during QSM reconstruction from the tissue field. Deep learning methods have shown great potential in addressing these issues
however, most existing approaches rely on basic U-net structures, leading to limited performances and reconstruction artifacts sometimes. PURPOSE: This study aims to develop a novel deep learning-based method, IR METHODS: IR RESULTS: In this work, IR CONCLUSION: Overall, the proposed IR