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

Clémence Ligneul1, Chloé Najac2, André Döring3,4

  • 1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Magnetic Resonance in Medicine
|November 10, 2023
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Summary

Diffusion-weighted magnetic resonance spectroscopy (dMRS) offers insights into brain cell structure but faces technical challenges. This paper outlines best practices and tools for robust dMRS studies, aiding neurodegenerative disease research.

Keywords:
acquisitiondMRSfittingmodellingprocessing

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Area of Science:

  • Neuroscience
  • Biophysics
  • Medical Imaging

Background:

  • Brain cell structure and function are key indicators of neurodevelopment, plasticity, aging, and pathological processes like neurodegeneration and neuroinflammation.
  • Noninvasive, quantitative methods to assess cellular structural features are crucial for advancing brain research.
  • Diffusion-weighted magnetic resonance spectroscopy (dMRS) provides access to diffusion properties of intracellular brain metabolites, offering cell-specific information.

Purpose of the Study:

  • To address the challenges in diffusion-weighted magnetic resonance spectroscopy (dMRS) data acquisition, analysis, quantification, modeling, and interpretation.
  • To establish a set of recommendations for conducting robust dMRS studies.
  • To provide a comprehensive guide and resources for the dMRS community.

Main Methods:

  • The paper consolidates recommendations from the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy."
  • It details the necessary steps for acquiring, processing, fitting, and modeling dMRS data.
  • Links to valuable resources for dMRS practitioners are provided.

Main Results:

  • The dMRS community has established consensus-based recommendations for best practices.
  • A structured approach to dMRS data handling, from acquisition to interpretation, is presented.
  • The paper serves as a practical guide for researchers aiming to improve dMRS study robustness.

Conclusions:

  • Standardized best practices are essential for overcoming the technical challenges in dMRS.
  • Implementing these recommendations will enhance the reliability and reproducibility of dMRS studies.
  • This work facilitates the broader application of dMRS in understanding brain health and disease.