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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| Published in: | Magnetic resonance in medicine Vol. 79; no. 3; pp. 1231 - 1240 |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.03.2018
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| ISSN: | 0740-3194, 1522-2594 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. PurposeShort‐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.MethodsThe 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.ResultsThe 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.ConclusionThe 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. |
| Author | Trattnig, Siegfried Heckova, Eva Považan, Michal Strasser, Bernhard Gruber, Stephan Hangel, Gilbert Bogner, Wolfgang |
| AuthorAffiliation | 1 High Field MR Center, Department of Biomedical Imaging and Image‐guided Therapy Medical University Vienna Vienna Austria 2 Christian Doppler Laboratory for Clinical Molecular MR Imaging Vienna Austria |
| AuthorAffiliation_xml | – name: 2 Christian Doppler Laboratory for Clinical Molecular MR Imaging Vienna Austria – name: 1 High Field MR Center, Department of Biomedical Imaging and Image‐guided Therapy Medical University Vienna Vienna Austria |
| Author_xml | – sequence: 1 givenname: Michal surname: Považan fullname: Považan, Michal organization: Christian Doppler Laboratory for Clinical Molecular MR Imaging – sequence: 2 givenname: Bernhard surname: Strasser fullname: Strasser, Bernhard organization: Medical University Vienna – sequence: 3 givenname: Gilbert surname: Hangel fullname: Hangel, Gilbert organization: Medical University Vienna – sequence: 4 givenname: Eva surname: Heckova fullname: Heckova, Eva organization: Medical University Vienna – sequence: 5 givenname: Stephan surname: Gruber fullname: Gruber, Stephan organization: Medical University Vienna – sequence: 6 givenname: Siegfried surname: Trattnig fullname: Trattnig, Siegfried organization: Christian Doppler Laboratory for Clinical Molecular MR Imaging – sequence: 7 givenname: Wolfgang surname: Bogner fullname: Bogner, Wolfgang email: wolfgang.bogner@meduniwien.ac.at organization: Medical University Vienna |
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Short‐echo‐time proton MR spectra at 7T feature nine to 10 distinct macromolecule (MM) resonances that overlap with the signals of metabolites.... PurposeShort‐echo‐time proton MR spectra at 7T feature nine to 10 distinct macromolecule (MM) resonances that overlap with the signals of metabolites.... |
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| SubjectTerms | 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 |
| Title | Simultaneous mapping of metabolites and individual macromolecular components via ultra‐short acquisition delay 1H MRSI in the brain at 7T |
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