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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