In vivo brain macromolecule signals in healthy and glioblastoma mouse models: 1H magnetic resonance spectroscopy, post-processing and metabolite quantification at 14.1 T

Authors

  • Mélanie Craveiro,

    1. Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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  • Virginie Clément-Schatlo,

    1. Department of Clinical Neurosciences, University of Geneva, Geneva, Switzerland
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  • Denis Marino,

    1. Department of Clinical Neurosciences, University of Geneva, Geneva, Switzerland
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  • Rolf Gruetter,

    1. Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    2. Department of Radiology, University of Lausanne, Lausanne, Switzerland
    3. Department of Radiology, University of Geneva, Geneva, Switzerland
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  • Cristina Cudalbu

    Corresponding author
    1. Centre d'Imagerie Biomédicale, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    • Address correspondence and reprint requests to Cristina Cudalbu, Centre d'Imagerie Biomedicale (CIBM), Ecole Polytechnique Fédérale de Lausanne (EPFL), Station 6, 1015 Lausanne, Switzerland. E-mail: cristina.cudalbu@epfl.ch

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Abstract

In 1H magnetic resonance spectroscopy, macromolecule signals underlay metabolite signals, and knowing their contribution is necessary for reliable metabolite quantification. When macromolecule signals are measured using an inversion-recovery pulse sequence, special care needs to be taken to correctly remove residual metabolite signals to obtain a pure macromolecule spectrum. Furthermore, since a single spectrum is commonly used for quantification in multiple experiments, the impact of potential macromolecule signal variability, because of regional differences or pathologies, on metabolite quantification has to be assessed. In this study, we introduced a novel method to post-process measured macromolecule signals that offers a flexible and robust way of removing residual metabolite signals. This method was applied to investigate regional differences in the mouse brain macromolecule signals that may affect metabolite quantification when not taken into account. However, since no significant differences in metabolite quantification were detected, it was concluded that a single macromolecule spectrum can be generally used for the quantification of healthy mouse brain spectra. Alternatively, the study of a mouse model of human glioma showed several alterations of the macromolecule spectrum, including, but not limited to, increased mobile lipid signals, which had to be taken into account to avoid significant metabolite quantification errors.

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