Improved myelin water fraction mapping with deep neural networks using synthetically generated 3D data
Description
We introduce a generative model for synthesis of large scale 3D datasets for quantitative parameter mapping of myelin water fraction (MWF). Our model combines a MR physics signal decay model with an accurate probabilistic multi-component parametric
