[Paper Review] Diffusion-weighted MR spectroscopy: consensus, recommendations and resources from acquisition to modelling

Clémence Ligneul, Chloé Najac+25 more|arXiv (Cornell University)|May 18, 2023

Advanced Neuroimaging Techniques and Applications8 citations

TL;DR

The paper presents a community-wide consensus on best practices for diffusion-weighted MR spectroscopy (dMRS), detailing acquisition, processing, fitting, modelling, and providing resources from a workshop.

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.

Motivation & Objective

• Motivate robust dMRS studies by outlining a standardized workflow from data acquisition to modelling.
• Summarize consensus recommendations established during the Lorentz Workshop on Best Practices and Tools for Diffusion MR Spectroscopy (Leiden, 2021).
• Provide practical guidance and links to resources to support researchers in dMRS
• Address challenges in data acquisition, processing, quantification, and interpretation of dMRS results.

Proposed method

• Describe the end-to-end steps needed to acquire, processing, fitting, and modelling dMRS data.
• Consolidate community recommendations into a structured workflow from data collection to interpretation.
• Provide links to useful resources and tools for each stage of the dMRS pipeline.
• Present the consensus as a reference for robust and reproducible dMRS studies.

Experimental results

Research questions

• RQ1 What are the community-endorsed best practices for acquiring diffusion-weighted MRS data?
• RQ2 What processing and fitting strategies optimize reliability and quantification in dMRS?
• RQ3 How should diffusion modelling of intracellular metabolites be conducted and interpreted in brain studies?
• RQ4 What resources and tools are available to support researchers across the dMRS workflow.

Key findings

• A set of consensus recommendations for robust dMRS studies across acquisition, processing, fitting, and modelling.
• A structured description of steps required for acquiring, processing, fitting, and modelling dMRS data.
• Identification and curation of useful resources and tools to support each stage of the dMRS workflow.
• Recognition of challenges and limitations in dMRS and guidance to address them.
• Provision of a reference framework to standardize dMRS methods across studies.