Simultaneous mapping of metabolites and individual macromolecular components via ultra‐short acquisition delay 1H MRSI in the brain at 7T

Purpose Short‐echo‐time proton MR spectra at 7T feature nine to 10 distinct macromolecule (MM) resonances that overlap with the signals of metabolites. Typically, a metabolite‐nulled in vivo MM spectrum is included in the quantification`s prior knowledge to provide unbiased metabolite quantification...

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Bibliographic Details
Published in:Magnetic resonance in medicine Vol. 79; no. 3; pp. 1231 - 1240
Main Authors: Považan, Michal, Strasser, Bernhard, Hangel, Gilbert, Heckova, Eva, Gruber, Stephan, Trattnig, Siegfried, Bogner, Wolfgang
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc 01.03.2018
John Wiley and Sons Inc
Subjects:
Brain
Computer simulation
Constraint modelling
Full Papers—Spectroscopic Methodology
human brain
In vivo methods and tests
Macromolecules
Magnetic induction
Magnetic resonance imaging
Measurement methods
Medicine
Metabolites
MR spectroscopic imaging
Neuroimaging
Parameterization
Singular value decomposition
Spectra
ISSN:0740-3194, 1522-2594
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Summary:Purpose Short‐echo‐time proton MR spectra at 7T feature nine to 10 distinct macromolecule (MM) resonances that overlap with the signals of metabolites. Typically, a metabolite‐nulled in vivo MM spectrum is included in the quantification`s prior knowledge to provide unbiased metabolite quantification. However, this MM model may fail if MMs are pathologically altered. In addition, information about the individual MM peaks is lost. In this study, we aimed to create an improved MM model by parameterization of the in vivo MM spectrum into individual components, and to use this new model to quantify free induction decay MR spectroscopic imaging (FID‐MRSI) data. Methods The measured in vivo MM spectrum was parameterized using advanced method for accurate, robust, and efficient spectral fitting (AMARES) and Hankel‐Lanczos singular value decomposition algorithms from which six different MM models were derived. Soft constraints were applied to avoid over‐parameterization. All MM models were combined with simulated metabolite spectra to form complete basis sets. FID‐MRSI data from 14 healthy volunteers were quantified via LCModel, and the results were compared between all basis sets. Results The MM model using nine individual AMARES‐parameterized MM components with additional soft constraints achieved the most reliable results. Nine MMs and seven metabolites were mapped simultaneously over the whole slice. Conclusion The proposed MM model may facilitate studies that involve patients with pathologically altered MMs. Magn Reson Med 79:1231–1240, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Bibliography:Parts of this study were presented at ISMRM 24th Annual Meeting and Exhibition, Singapore, May 7–13, 2016.
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ISSN:0740-3194
1522-2594
DOI:10.1002/mrm.26778