1H-MRS VERI
1H-MRS VERI

1H-MRS VERI (the Proton MRS Validation Effort Resource Initiative) was initiated from within the 2021/22 MRS Code and Data Sharing Committee and MRSHub to provide a centralized means for sharing spectral datasets that can be used to quantitatively validate proton magnetic resonance spectral processing and quantification tools. The tools to be validated include not only those that have already persisted in the literature absent centralized validation efforts but also as-yet agreed upon novel software solutions, open-source and otherwise, that might underlie future consensus recommendations regarding precise and accurate spectral processing and quantification pipelines.
Objectives
Data sets sought for fieldwide use via 1H-MRS VERI include:
- 1H-MRS acquisitions from phantoms, commercial or in-house, of premeasured metabolite concentrations;
- Well defined “gold standard” simulations of metabolite 1H-MR spectra including simulated and/or measured in vivo-like contributions to the data (e.g., macromolecules, extravoxel lipids, residual water, lineshape distortions from static field inhomogeneity, and other features that might be exhibited by in vivo spectral datasets);
- In vivo 1H-MR spectral acquisitions from human or other tissues within the context of experiments that also contain supporting measurements of metabolite concentration from non-1H-MRS techniques, including X-nuclear MRS and/or non-MRS methods like chromatography;
Not sure if your data set belongs here? Want to contribute something more? Email Kelley Swanberg at kelley.swanberg@med.lu.se.
How to get involved
We are looking for our inaugural 1H-MRS VERI data curation and approval team! Please find out more below:
- Website
- OSF project
- ISMRM Virtual Meeting 2023
- ISMRM 2024 Abstract
- Or email kelley.swanberg@med.lu.se
Primary Team (2021-22 MRS Code and Data Sharing Committee Excutive Board)
- Kelley M. Swanberg, Medicinska fakulteten vid Lunds universitet
- Helge Zöllner, Johns Hopkins University
- Candace Fleischer, Emory University School of Medicine
- Jamie Near, University of Toronto
- John LaMaster, Technische Universität München
- Antonia Kaiser, École polytechnique fédérale de Lausanne
- Georg Oeltzschner, Johns Hopkins University