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Abstract #3151

Multi-seed myelin water imaging using gradient echo: beyond the initial guess in exponential-sum fit

Hyeong-Geol Shin1, Se-Hong Oh2, Sooyeon Ji1, Jieun Lee1, Woojin Jung1, Eun-Jung Choi1, and Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea, Republic of

In this work, we investigated the effects of initial parameter guess on non-linear least square (NLLS) method for myelin water imaging (MWI). We demonstrated that an inappropriate initial guess induces error in MWI and proposed a multi-seed algorithm to reduce the initial guess-dependent error. To do so, we applied the multi-seed MWI to synthetic and in-vivo data and compared the outcomes with the conventional algorithm (i.e., single-seed MWI). MWI results estimated by the multi-seed algorithm showed better agreement with the model than the single-seed algorithm, suggesting a potential solution to mitigate the ill-posed condition of MWI.

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