Demyelinating diseases such as multiple sclerosis cause changes in the brain white matter microstructure. Multi-exponential T2 relaxometry is a powerful technology for detecting these changes by generating a myelin water fraction (MWF) map. However, conventional approaches are subject to noise and spatial in-consistence. We proposed a novel approach by imposing spatial consistency and smoothness constraints. We first introduce a two-Gaussian model to approximate the T2 distribution. Then an expectation-maximization framework is introduced with an edge-preserving prior incorporated. Three-dimensional multi-echo MRI data sets were collected from three patients and three healthy volunteers. MWF maps obtained using the conventional, Spatially Regularized Non-negative Least Squares (srNNLS) algorithm as well as the proposed algorithm are compared. The proposed method provides MWF maps with improved depiction of brain structures and significantly lower coefficients of variance in various brain regions,