Abstract #1639

Semi-supervised learning for fast multi-compartment relaxometry myelin water imaging (MCR-MWI)

Kwok-Shing Chan¹, Tae Hyung Kim^2,3, Berkin Bilgic^2,3, and José P Marques¹

¹Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands, ²Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ³Department of Radiology, Harvard Medical School, Boston, MA, United States

Synopsis

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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