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A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
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A data-driven algorithm to determine 1H-MRS basis set composition.

Christopher W Davies-Jenkins1, Helge J Zöllner1, Dunja Simicic1

  • 1The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Magnetic Resonance in Medicine
|August 16, 2025
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Summary
This summary is machine-generated.

This study introduces a data-driven method using Akaike information criteria (AIC) to select basis sets for magnetic resonance spectroscopy (MRS). This approach improves the accuracy and reproducibility of metabolite quantification in clinical applications.

Keywords:
2HGbasis setcystathionineinformation criteriamagnetic resonance spectroscopymodel selection

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Area of Science:

  • Magnetic Resonance Spectroscopy (MRS)
  • Metabolomics
  • Quantitative Analysis

Background:

  • Quantitative analysis in MRS relies on basis sets, which are lists of metabolite basis functions.
  • Inconsistent basis set selection leads to poor reproducibility in MRS studies.
  • Lack of objective criteria for basis set suitability hinders standardization.

Purpose of the Study:

  • To develop and validate a novel, data-driven approach for determining optimal basis set composition in MRS.
  • To utilize Akaike information criteria (AIC) for objective basis set selection.
  • To enhance the reproducibility and objectivity of quantitative MRS analyses.

Main Methods:

  • An iterative algorithm was developed to build basis sets using AIC scores.
  • Two stopping conditions (max-AIC and zero-amplitude) were investigated.
  • Basis set optimization was evaluated at both single-spectrum and group levels using synthetic brain and tumor spectra.

Main Results:

  • Derived basis sets accurately identified high-concentration metabolites and fitted spectra well.
  • Single-spectrum basis set determination achieved 84%-88% accuracy.
  • Group-level optimization improved basis set determination accuracy to 89%-92%.

Conclusions:

  • Data-driven determination of basis set composition is feasible and effective.
  • This method has the potential to reduce operator bias in MRS.
  • Refinement of this approach can enhance the clinical viability of MRS through improved objectivity.