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Abstract #1639

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

Kwok-Shing Chan1, Tae Hyung Kim2,3, Berkin Bilgic2,3, 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


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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