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Abstract #2464

Chi-sepnet: Susceptibility source separation using deep neural network

Minjun Kim1, Hyeong-Geol Shin1, Chungseok Oh1, Hwihun Jeong1, Sooyeon Ji1, Hongjun An1, Jiye Kim1, Jinhee Jang2, Berkin Bilgic3, and Jongho Lee1
1Seoul National University, Seoul, Korea, Republic of, 2Seoul St Mary’s Hospital, Seoul, Korea, Republic of, 3Harvard Medical School, Boston, MA, United States


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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