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Updated: May 16, 2026

Pure Shift Nuclear Magnetic Resonance: a New Tool for Plant Metabolomics
Published on: July 31, 2021
Towards harmonized spectral quantification in MRSI: comparative analysis of backward-linear-predicted and original
Alessio Siviglia1,2,3, Brayan Alves4,5,6, Cristina Cudalbu4,5,6
1CIBM Center for Biomedical Imaging, Lausanne, Switzerland. alessio.siviglia@epfl.ch.
Objective:
We hypothesized that the inherent acquisition delay (AD) in 1H-FID-MRSI can introduce systematic LCModel quantification biases due to strong spectral dephasing, and that Backward-Linear-Prediction (BLP) reconstruction toward AD = 0 ms can harmonize LCModel metabolite estimates across acquisitions with various delays.
Materials And Methods:
2D 1H-FID-MRSI were acquired in rats at 14.1 T with three AD values (0.71, 0.94, 1.30 ms). Hippocampal metabolites were quantified using LCModel and AD-matched basis sets. Complementary Monte-Carlo simulations (n = 1000) replicated 1H-FID-MRSI spectra at multiple ADs under realistic SNR conditions. BLP was applied to in vivo and simulated FIDs to back-predict missing points up to AD = 0 ms, enabling quantification within a unified basis set framework.
Results:
In vivo and simulated data showed clear AD-dependent variations for several metabolites (Gln, tCho, tNAA, Ins, Tau), with discrepancies frequently > 10% despite AD-specific basis sets. Simulations confirmed metabolite-specific biases increasing with AD. BLP reconstruction preserved quantification consistency up to ~ 0.98 ms of recovered FIDs, reducing inter-AD mismatches in vivo-particularly for Tau, tNAA and tCho-lowering the mean discrepancy from 10.5% to ~ 5%.
Discussion:
This work supports BLP as a practical strategy to improve consistency and comparability in MRSI studies.
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