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Abstract #3432

Improved Microstructure-Informed Myelin Mapping (iMIMM) for Myelin Mapping solely from Gradient-Echo Data

Mert Sisman1,2, Alexey V. Dimov2, Pascal Spincemaille2, Thanh D. Nguyen2, and Yi Wang2,3
1Electrical and Computer Engineering, Cornell University, New York, NY, United States, 2Department of Radiology, Weill Cornell Medicine, New York, NY, United States, 3Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States

Synopsis

Keywords: Susceptibility/QSM, Microstructure, Myelin

Motivation: Recently, microstructure-informed myelin mapping (MIMM) was proposed to estimate myelin content from multi gradient-echo (mGRE) data by modeling both magnitude and phase-derived susceptibility values. Although magnitude modeling incorporated the susceptibility anisotropy, susceptibility modeling ignored it.

Goal(s): Here, MIMoSA is proposed to improve upon MIMM by including the susceptibility anisotropy both in magnitude and phase.

Approach: Following MIMM, a dictionary of voxel signals associated with specific microstructures including myelin and iron was generated with a novel negative susceptibility estimation approach.

Results: In vivo results of healthy subjects showed that iMIMM performs similar with MIMM without the need of an additional orientation prior.

Impact: By accurate modeling of the effect of white matter anisotropic susceptibility on the full gradient-echo signal, myelin content can be mapped from routine gradient echo acquisitions.

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