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Cited by 3,655

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Estimation of metabolite concentrations from localized in vivo proton NMR spectra

Provencher, Stephen W.·1993·Magnetic Resonance in Medicine

Abstract

The LCModel method analyzes an in vivo spectrum as a Linear Combination of Model spectra of metabolite solutions in vitro. By using complete model spectra, rather than just individual resonances, maximum information and uniqueness are incorporated into the analysis. A constrained regularization method accounts for differences in phase, baseline, and lineshapes between the in vitro and in vivo spectra, and estimates the metabolite concentrations and their uncertainties. LCModel is fully automatic in that the only input is the time‐domain in vivo data. The lack of subjective interaction should help the exchange and comparison of results. More than 3000 human brain STEAM spectra from patients and healthy volunteers have been analyzed with LCModel. N‐acetylaspartate, cholines, creatines, myo‐inositol, and glutamate can be reliably determined, and abnormal levels of these or elevated levels of lactate, alanine, scyllo‐inositol, glutamine, or glucose clearly indicate numerous pathologies. A computer program will be available.

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Metadata

Authors
Provencher, S. W.
Venue
Magnetic Resonance in Medicine
Publisher
Wiley
Published
1993-12
Volume
30
Issue
6
Pages
672-679
ISSN
  • 0740-3194 (ISSN-L)
  • 1522-2594
OpenAlex
W2038979894
Cited by
3,655
References
1
FWCI
8.70
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Estimation of metabolite concentrations from localized in vivo proton NMR spectra. https://app.cochat.ai/papers/10.1002/mrm.1910300604
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Provencher, S. W. (1993). Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.1910300604

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.