Quantitative Susceptibility Mapping (QSM) is a MR post-processing technique that estimates underlying tissue magnetic susceptibilities. In QSM processing pipelines, background field removal is of vital importance to obtain local tissue field estimates for precise susceptibility quantification. Existing background field removal methods such as SHARP, RESHARP, PDF, and LBV can effectively remove the background field. However, they struggled in clinical applications with large slice thickness and resulting non-isotropic resolutions. To address the limitations of these existing pre-processing methods in clinical QSM practice, a deep-learning-based method was proposed to approximate the underlying tissue field maps from total field maps. In-vivo datasets acquired using clinical SWI protocol demonstrated the improved performance of this approach, compared to conventional existing methods.
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