In this study we aim to accelerate the acquisition time of myelin-water imaging by acquiring fewer slices and applying machine learning to extract myelin-specific information from anatomical (T1w and T2w) and diffusion-weighted imaging (DWI), which are commonly available in many clinical research studies. It is shown that with a 6-fold acceleration (from 7:30min to 1:15min) the myelin content can be reconstructed using neural networks with an agreement to the ground-truth that is comparable to the reproducibility of the scan itself.
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