*Result*: Influence of Basis Set Composition on Metabolite Quantification of 1H-MRS at 3 T: Combining In Silico, In Vivo and In Vitro Evidence.
Title:
Influence of Basis Set Composition on Metabolite Quantification of 1H-MRS at 3 T: Combining In Silico, In Vivo and In Vitro Evidence.
Authors:
Emeliyanova P; School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.; Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Manchester, UK., Parkes LM; School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.; Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Manchester, UK., Lea-Carnall C; School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.; Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Manchester, UK., Williams SR; School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
Source:
NMR in biomedicine [NMR Biomed] 2026 Mar; Vol. 39 (3), pp. e70230.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: England NLM ID: 8915233 Publication Model: Print Cited Medium: Internet ISSN: 1099-1492 (Electronic) Linking ISSN: 09523480 NLM ISO Abbreviation: NMR Biomed Subsets: MEDLINE
Imprint Name(s):
Publication: Chichester : Wiley
Original Publication: London : Heyden & Son, 1988-
Original Publication: London : Heyden & Son, 1988-
MeSH Terms:
Brain*/metabolism , Brain*/diagnostic imaging , Proton Magnetic Resonance Spectroscopy*/methods , Computer Simulation* , Metabolome*, Creatine/metabolism ; Glutamic Acid/metabolism ; Aspartic Acid/analogs & derivatives ; Aspartic Acid/metabolism ; Choline/metabolism ; Humans ; Phantoms, Imaging ; Reproducibility of Results ; Adult ; Signal-To-Noise Ratio ; Male
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Grant Information:
United Kingdom MRC_ Medical Research Council; School of Biological Sciences, University of Manchester, UK
Contributed Indexing:
Keywords: basis set models; bias–variance trade‐off; glutamate; in vivo data; linear combination of basis spectra; phantom; synthetic data
Substance Nomenclature:
MU72812GK0 (Creatine)
3KX376GY7L (Glutamic Acid)
30KYC7MIAI (Aspartic Acid)
997-55-7 (N-acetylaspartate)
N91BDP6H0X (Choline)
3KX376GY7L (Glutamic Acid)
30KYC7MIAI (Aspartic Acid)
997-55-7 (N-acetylaspartate)
N91BDP6H0X (Choline)
Entry Date(s):
Date Created: 20260212 Date Completed: 20260212 Latest Revision: 20260224
Update Code:
20260224
PubMed Central ID:
PMC12894809
DOI:
10.1002/nbm.70230
PMID:
41674026
Database:
MEDLINE
*Further Information*
*Magnetic resonance spectra are quantified by model fitting in the time or frequency domain to a basis set consisting of metabolites present in the measured sample. Despite some work on basis set composition, it remains unclear which metabolite components should be included in the basis set models and how these impact the quantification of key metabolites such as glutamate (Glu). This lack of consensus contributes to reproducibility issues across research groups. Here, we use synthetic, human brain and phantom data to assess how basis set choice impacts quantification, focusing on the bias-variance trade-off under different SNR conditions. Simulated 1D spectra mimicking in vivo human brain data at 3 T were used to identify basis models that minimise bias and variance for our four key metabolites of interest: Glu, creatine (tCr), choline-containing compounds (tCho) and N-acetylaspartate (tNAA) under increasing noise levels. This informed analyses of human brain and phantom data collected at 3 T over 81 and 120 min, respectively, using PRESS. We find that basis set composition significantly affected Glu, tCr, tCho and tNAA concentrations. Specifically, the inclusion of γ-aminobutyric acid, glutathione, N-acetylaspartylglutamate and glucose improved Glu quantification, achieving bias and variance below 10%. Including partner metabolites for tCr and tNAA (phosphocreatine and N-acetylaspartylglutamate) offered no significant benefit. In contrast, tCho quantification remained inconsistent likely due to spectral overlap. We show that minimal basis set models provide accurate quantification while reducing variance. However, the number of metabolites accurately modelled depends on data quality and SNR. High-SNR spectra enable the inclusion of additional metabolites, while low-SNR data risk overfitting. Differences in metabolite concentrations reported in the literature may partly reflect variations in prior knowledge models, emphasising the need for clear descriptions of analysis methods in MRS research.
(© 2026 The Author(s). NMR in Biomedicine published by John Wiley & Sons Ltd.)*