Myelin water imaging using multi-compartment relaxometry (MCR-MWI) improves the GRE-MWI robustness and accuracy but suffered from slow processing speed. In this study, we incorporate both supervised and self-supervised machine learning for fast MCR-MWI that is generalisable to a wide range of acquisition parameters without the need to re-train the network. We demonstrate its application on single compartment fitting and MCR-MWI. Results show that the proposed method can produce comparable high SNR results with a 62-fold shorter processing time.
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