Deep Learning reconstruction methods are increasingly investigated in Quantitative Susceptibility Mapping (QSM). In this work, we applied a UNET to reconstruct susceptibility maps in the presence of fat from unwrapped phase maps. The network was trained using synthetically generated multi-echo phase data and does not require explicit masking for the background field correction. Our results show that the proposed approach is well-suited to rapidly reconstruct high quality susceptibility maps in the presence of fat (e.g., outside the central nervous system) in in vivo data.
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