ArticleMagnetic resonance in medicine2022
Results and interpretation of a fitting challenge for MR spectroscopy set up by the MRS study group of ISMRM.
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 59 citations in OpenAlex.
- Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease.Analytical biochemistry · 2023Pooled it
- Functional MRS studies of GABA and glutamate/Glx - A systematic review and meta-analysis.Neuroscience and biobehavioral reviews · 2023Pooled it
- Altered hypothalamic metabolism in chronic cluster headache patients measured withThe journal of headache and pain · 2026Article
- Comparative evaluation of a deep learning method QNet and LCModel for MRS quantification on the cloud computing platform CloudBrain-MRS.BMC medical imaging · 2026Article
- Parsing Neurometabolic Signatures of Multiple Sclerosis with MRSI and cPCA.medRxiv : the preprint server for health sciences · 2026Article
- A data-driven algorithm to determineMagnetic resonance in medicine · 2026Article
- MRS-Sim: Open-Source Framework for Simulating In Vivo-Like Magnetic Resonance Spectra.NMR in biomedicine · 2025Article
- Chemical shift and relaxation regularization improve the accuracy ofMagnetic resonance in medicine · 2025Article
- WAND: Wavelet Analysis-Based Neural Decomposition of MRS Signals for Artifact Removal.NMR in biomedicine · 2025Article
- Detection of elevated succinate in brain during circulatory arrest in a piglet model: A 3TMagnetic resonance in medicine · 2025Article
- Metabolite TMagnetic resonance in medicine · 2025Article
- NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets.Radiology. Artificial intelligence · 2025Article
- Functional magnetic resonance spectroscopy of prolonged motor activation using conventional and spectral GLM analyses.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A graphical pipeline platform for MRS data processing and analysis: MRSpecLAB.Frontiers in neuroimaging · 2025Article
- Model-based frequency-and-phase correction ofMagnetic resonance in medicine · 2024Article
- GABA-edited MEGA-PRESS at 3 T: Does a measured macromolecule background improve linear combination modeling?Magnetic resonance in medicine · 2024Article
- Reproducibility of arterial spin labeling cerebral blood flow image processing: A report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge.Magnetic resonance in medicine · 2024Article
- Model-based frequency-and-phase correction ofbioRxiv : the preprint server for biology · 2024Article
- Improved reproducibility of γ-aminobutyric acid measurement from short-echo-time proton MR spectroscopy by linewidth-matched basis sets in LCModel.NMR in biomedicine · 2024Article
- Event-related functional magnetic resonance spectroscopy.NeuroImage · 2023Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeFitting of MRS data plays an important role in the quantification of metabolite concentrations. Many different spectral fitting packages are used by the MRS community. A fitting challenge was set up to allow comparison of fitting methods on the basis of performance and robustness.
methodsSynthetic data were generated for 28 datasets. Short-echo time PRESS spectra were simulated using ideal pulses for the common metabolites at mostly near-normal brain concentrations. Macromolecular contributions were also included. Modulations of signal-to-noise ratio (SNR); lineshape type and width; concentrations of γ-aminobutyric acid, glutathione, and macromolecules; and inclusion of artifacts and lipid signals to mimic tumor spectra were included as challenges to be coped with.
resultsTwenty-six submissions were evaluated. Visually, most fit packages performed well with mostly noise-like residuals. However, striking differences in fit performance were found with bias problems also evident for well-known packages. In addition, often error bounds were not appropriately estimated and deduced confidence limits misleading. Soft constraints as used in LCModel were found to substantially influence the fitting results and their dependence on SNR.
conclusionsSubstantial differences were found for accuracy and precision of fit results obtained by the multiple fit packages.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.