ArticleMagnetic resonance in medicine2022
Macromolecular background signal and non-Gaussian metabolite diffusion determined in human brain using ultra-high diffusion weighting.
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 4 papers.
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4 citing papers in PubMed.
- Article
- Universal dynamic fitting of magnetic resonance spectroscopy.Magnetic resonance in medicine · 2024Article
- Feasibility and implications of using subject-specific macromolecular spectra to model short echo time magnetic resonance spectroscopy data.NMR in biomedicine · 2023Article
- Macromolecular background signal and non-Gaussian metabolite diffusion determined in human brain using ultra-high diffusion weighting.Magnetic resonance in medicine · 2022Article
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5 authors.
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Abstract
purposeDefinition of a macromolecular MR spectrum based on diffusion properties rather than relaxation time differences and characterization of non-Gaussian diffusion of brain metabolites with strongly diffusion-weighted MR spectroscopy.
methodsShort echo time MRS with strong diffusion-weighting with b-values up to 25 ms/μm
resultsThe motion compensation scheme prevented spurious signal decay reflected in very small apparent diffusion constants for macromolecular signal. Macromolecular background signal patterns were determined using multiple fit strategies. Signal decay corresponding to non-Gaussian metabolite diffusion was represented by biexponential fit models yielding parameter estimates for human gray matter that are in line with published rodent data. The optimal fit strategies used constraints for the signal decay of metabolites with limited signal contributions to the overall spectrum.
conclusionThe determined macromolecular spectrum based on diffusion properties deviates from the conventional one derived from longitudinal relaxation time differences calling for further investigation before use as experimental basis spectrum when fitting clinical MR spectra. The biexponential characterization of metabolite signal decay is the basis for investigations into pathologic alterations of microstructure.
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