The separation of positive and negative susceptibility source distributions (e.g., iron and myelin distributions) has important meanings in neuroscience and clinic. In this study, a deep learning-based χ-separation method is proposed to generate high-quality susceptibility source maps. For network training, multi-orientation head data are utilized, providing artifact-free label data. For the input data, either R2’ or R2* maps are utilized in addition to local field and QSM maps, producing two neural networks, χ-sepnet-R2’ and χ-sepnet-R2* (the latter requires no T2). The results of χ-sepnets outperformed the conventional method, revealing details of brain structures both in healthy volunteers and patients.
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