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Simultaneous mapping of metabolites and individual macromolecular components via ultra-short acquisition delay 1 H MRSI in the brain at 7T.
Povazan, Michal; Strasser, Bernhard; Hangel, Gilbert; Heckova, Eva; Gruber, Stephan; Trattnig, Siegfried; Bogner, Wolfgang.
Afiliação
  • Povazan M; High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
  • Strasser B; Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria.
  • Hangel G; High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
  • Heckova E; High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
  • Gruber S; High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
  • Trattnig S; High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
  • Bogner W; High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria.
Magn Reson Med ; 79(3): 1231-1240, 2018 03.
Article em En | MEDLINE | ID: mdl-28643447
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 791231-1240, 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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Encéfalo / Mapeamento Encefálico / Imageamento por Ressonância Magnética / Imagem Molecular Limite: Adult / Female / Humans / Male Idioma: En Revista: Magn Reson Med Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Áustria

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Encéfalo / Mapeamento Encefálico / Imageamento por Ressonância Magnética / Imagem Molecular Limite: Adult / Female / Humans / Male Idioma: En Revista: Magn Reson Med Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Áustria