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Abstract #5076

Myelin Water Fraction Estimation from Optimized Steady-State Sequences using Kernel Ridge Regression

Gopal Nataraj1, Jon-Fredrik Nielsen2, and Jeffrey A. Fessler1

1Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States, 2Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States

This work introduces a new framework for myelin water fraction (MWF) estimation. We use a novel scan design approach to construct a sequence a fast steady-state sequences and optimize corresponding flip angles and repetition times for precise MWF estimation. We quantify MWF and five other parameters per voxel using a novel method based on kernel ridge regression. We obtain MWF maps in vivo that are comparable to those reported in literature, with possibly shorter overall scan time.

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