Diffusion-weighted MR spectroscopy: consensus, recommendations and resources from acquisition to modelling
Abstract
Brain cell structure and function reflect neurodevelopment, plasticity and ageing, and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to non-invasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MR spectroscopy (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, modelling and interpretation of results. These challenges were the motivation behind the organisation of the Lorentz Workshop on 'Best Practices and Tools for Diffusion MR Spectroscopy' held in Leiden 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 modelling dMRS data and provides links to useful resources.
Cite
@article{arxiv.2305.10829,
title = {Diffusion-weighted MR spectroscopy: consensus, recommendations and resources from acquisition to modelling},
author = {Clémence Ligneul and Chloé Najac and André Döring and Christian Beaulieu and Francesca Branzoli and William T Clarke and Cristina Cudalbu and Guglielmo Genovese and Saad Jbabdi and Ileana Jelescu and Dimitrios Karampinos and Roland Kreis and Henrik Lundell and Małgorzata Marjańska and Harald E. Möller and Jessie Mosso and Eloïse Mougel and Stefan Posse and Stefan Ruschke and Kadir Simsek and Filip Szczepankiewicz and Assaf Tal and Chantal Tax and Georg Oeltzschner and Marco Palombo and Itamar Ronen and Julien Valette},
journal= {arXiv preprint arXiv:2305.10829},
year = {2023}
}
Comments
49 pages, 5 figures, 5 tables, supplementary material