Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling

Clémence Ligneul, Chloé Najac, André Döring, Christian Beaulieu, Francesca Branzoli, William T. Clarke, Cristina Cudalbu, Guglielmo Genovese, Saad Jbabdi, Ileana Jelescu, Dimitrios Karampinos, Roland Kreis, Henrik Lundell, Małgorzata Marjańska, Harald E. Möller, Jessie Mosso, Eloïse Mougel, Stefan Posse, Stefan Ruschke, Kadir SimsekFilip Szczepankiewicz, Assaf Tal, Chantal Tax, Georg Oeltzschner, Marco Palombo, Itamar Ronen, Julien Valette

Research output: Contribution to journalArticlepeer-review

Abstract

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on “Best Practices & Tools for Diffusion MR Spectroscopy” held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.

Original languageEnglish (US)
Pages (from-to)860-885
Number of pages26
JournalMagnetic resonance in medicine
Volume91
Issue number3
DOIs
StatePublished - Mar 2024

Keywords

  • acquisition
  • dMRS
  • fitting
  • modelling
  • processing

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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