$$$T_2$$$ distributions are typically computed using point estimates such as nonnegative least-squares (NNLS). This characterizes the most likely $$$T_2$$$-distribution arising from the data, but disregards other plausible solutions - of which there are many, due to the ill-posed nature of the inverse problem. Here, we instead propose to use Bayesian posterior sampling methods. To guide the difficult high-dimensional sampling problem, a data-driven domain transformation is learned alongside a deep generative prior. The resulting posterior samples produce more spatially consistent myelin water fraction (MWF) maps compared to NNLS, despite the purely voxelwise analysis, and additionally yields novel MWF uncertainty estimates.
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