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A multicomponent T 2 relaxometry algorithm for myelin water imaging of the brain
Author(s) -
Björk Marcus,
Zachariah Dave,
Kullberg Joel,
Stoica Petre
Publication year - 2016
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.25583
Subject(s) - relaxometry , nuclear magnetic resonance , myelin , computer science , algorithm , magnetic resonance imaging , physics , neuroscience , psychology , medicine , radiology , spin echo , central nervous system
Purpose Models based on a sum of damped exponentials occur in many applications, particularly in multicomponent T 2 relaxometry. The problem of estimating the relaxation parameters and the corresponding amplitudes is known to be difficult, especially as the number of components increases. In this article, the commonly used non‐negative least squares spectrum approach is compared to a recently published estimation algorithm abbreviated as Exponential Analysis via System Identification using Steiglitz–McBride. Methods The two algorithms are evaluated via simulation, and their performance is compared to a statistical benchmark on precision given by the Cramér–Rao bound. By applying the algorithms to an in vivo brain multi‐echo spin‐echo dataset, containing 32 images, estimates of the myelin water fraction are computed. Results Exponential Analysis via System Identification using Steiglitz–McBride is shown to have superior performance when applied to simulated T 2 relaxation data. For the in vivo brain, Exponential Analysis via System Identification using Steiglitz–McBride gives an myelin water fraction map with a more concentrated distribution of myelin water and less noise, compared to non‐negative least squares. Conclusion The Exponential Analysis via System Identification using Steiglitz–McBride algorithm provides an efficient and user‐parameter‐free alternative to non‐negative least squares for estimating the parameters of multiple relaxation components and gives a new way of estimating the spatial variations of myelin in the brain. Magn Reson Med 75:390–402, 2016. © 2015 Wiley Periodicals, Inc.