A general linear relaxometry model of R1 using imaging data.

Détails

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Etat: Serval
Version: Final published version
ID Serval
serval:BIB_C7F6F59D8C30
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
A general linear relaxometry model of R1 using imaging data.
Périodique
Magnetic Resonance In Medicine : Official Journal of the Society of Magnetic Resonance In Medicine / Society of Magnetic Resonance In Medicine
Auteur(s)
Callaghan M.F., Helms G., Lutti A., Mohammadi S., Weiskopf N.
ISSN
1522-2594 (Electronic)
ISSN-L
0740-3194
Statut éditorial
Publié
Date de publication
2015
Volume
73
Numéro
3
Pages
1309-1314
Langue
anglais
Notes
Publication types: Journal ArticlePublication Status: ppublish
Résumé
PURPOSE: The longitudinal relaxation rate (R1 ) measured in vivo depends on the local microstructural properties of the tissue, such as macromolecular, iron, and water content. Here, we use whole brain multiparametric in vivo data and a general linear relaxometry model to describe the dependence of R1 on these components. We explore a) the validity of having a single fixed set of model coefficients for the whole brain and b) the stability of the model coefficients in a large cohort.
METHODS: Maps of magnetization transfer (MT) and effective transverse relaxation rate (R2 *) were used as surrogates for macromolecular and iron content, respectively. Spatial variations in these parameters reflected variations in underlying tissue microstructure. A linear model was applied to the whole brain, including gray/white matter and deep brain structures, to determine the global model coefficients. Synthetic R1 values were then calculated using these coefficients and compared with the measured R1 maps.
RESULTS: The model's validity was demonstrated by correspondence between the synthetic and measured R1 values and by high stability of the model coefficients across a large cohort.
CONCLUSION: A single set of global coefficients can be used to relate R1 , MT, and R2 * across the whole brain. Our population study demonstrates the robustness and stability of the model. Magn Reson Med, 2014. © 2014 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. Magn Reson Med 73:1309-1314, 2015. © 2014 Wiley Periodicals, Inc.
Pubmed
Web of science
Création de la notice
02/04/2015 20:30
Dernière modification de la notice
15/03/2019 14:04
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