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Journal Articles Journal of Neuroimaging Year : 2018

Use of the NESMA filter to improve myelin water fractionm Mapping with brain MRI

Abstract

BACKGROUND AND PURPOSE Myelin water fraction (MWF) mapping permits direct visualization of myelination patterns in the developing brain and in pathology. MWF is conventionally measured through multiexponential T-2 analysis which is very sensitive to noise, leading to inaccuracies in derived MWF estimates. Although noise reduction filters may be applied during postprocessing, conventional filtering can introduce bias and obscure small structures and edges. Advanced nonblurring filters, while effective, exhibit a high level of complexity and the requirement for supervised implementation for optimal performance. The purpose of this paper is to demonstrate the ability of the recently introduced nonlocal estimation of multispectral magnitudes (NESMA) filter to greatly improve the determination of MWF parameter estimates from gradient and spin echo (GRASE) imaging data. METHODS RESULTS We evaluated the performance of the NESMA filter for MWF mapping from clinical GRASE imaging data of the human brain, and compared the results to those calculated from unfiltered images. Numerical and in vivo analyses of the brains of three subjects, representing different ages, were conducted. Our results demonstrated the potential of the NESMA filter to permit high-quality in vivo MWF mapping. Indeed, NESMA permits substantial reduction of random variation in derived MWF estimates while preserving accuracy and detail. CONCLUSIONS In vivo estimation of MWF in the human brain from GRASE imaging data was markedly improved through use of the NESMA filter. The use of NESMA may contribute to the goal of high-quality MWF mapping in clinically feasible imaging times.

Dates and versions

hal-02620898 , version 1 (26-05-2020)

Identifiers

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Mustapha Bouhrara, David A. Reiter, Michael C. Maring, J.-M. Bonny, Richard G. Spencer. Use of the NESMA filter to improve myelin water fractionm Mapping with brain MRI. Journal of Neuroimaging, 2018, 28 (6), pp.640-649. ⟨10.1111/jon.12537⟩. ⟨hal-02620898⟩

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