Myelin water imaging (MWI) is a useful tool to probe and provide a quantitative measure of myelin content in the human brain in vivo. The most common MRI technique for MWI is based on multi-echo T2 measurements allowing one to estimate different T2 contributions into the signal decay. However, the conventional non-negative least squares algorithm is computationally very challenging and vulnerable to image artefacts. In the present work we developed an optimised framework for MWI enabling improved pipeline and fast metric estimations using Bayesian regression.
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