Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling.
Clémence LigneulChloé F NajacAndré DöringChristian BeaulieuFrancesca BranzoliWilliam T ClarkeCristina CudalbuGuglielmo GenoveseSaad JbabdiIleana O JelescuDimitrios C KarampinosPhilippe SchneiterHenrik LundellMalgorzata MarjańskaHarald E MöllerJessie MossoEloïse MougelStefan PosseStefan RuschkeKadir SimsekFilip SzczepankiewiczAssaf TalChantal M W TaxGeorg OeltzschnerMarco PalomboItamar RonenJulien ValettePublished in: Magnetic resonance in medicine (2023)
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.
Keyphrases
- diffusion weighted
- contrast enhanced
- resting state
- white matter
- high resolution
- cerebral ischemia
- healthcare
- functional connectivity
- magnetic resonance imaging
- single cell
- magnetic resonance
- clinical practice
- primary care
- mental health
- computed tomography
- single molecule
- electronic health record
- big data
- subarachnoid hemorrhage
- multiple sclerosis
- brain injury
- cognitive impairment
- risk assessment
- mesenchymal stem cells
- artificial intelligence
- data analysis
- climate change
- lipopolysaccharide induced
- solid state
- case control