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Objective <p>We hypothesized that the inherent acquisition delay (AD) in <sup>1</sup>H-FID-MRSI can introduce systematic LCModel quantification biases due to strong spectral dephasing, and that Backward-Linear-Prediction (BLP) reconstruction toward AD = 0&#xa0;ms can harmonize LCModel metabolite estimates across acquisitions with various delays.</p> Materials and methods <p>2D <sup>1</sup>H-FID-MRSI were acquired in rats at 14.1&#xa0;T with three AD values (0.71, 0.94, 1.30&#xa0;ms). Hippocampal metabolites were quantified using LCModel and AD-matched basis sets. Complementary Monte-Carlo simulations (n = 1000) replicated <sup>1</sup>H-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&#xa0;ms, enabling quantification within a unified basis set framework.</p> Results <p>In vivo and simulated data showed clear AD-dependent variations for several metabolites (Gln, tCho, tNAA, Ins, Tau), with discrepancies frequently &gt; 10% despite AD-specific basis sets. Simulations confirmed metabolite-specific biases increasing with AD. BLP reconstruction preserved quantification consistency up to ~ 0.98&#xa0;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%.</p> Discussion <p>This work supports BLP as a practical strategy to improve consistency and comparability in MRSI studies.</p>

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Towards harmonized spectral quantification in MRSI: comparative analysis of backward-linear-predicted and original 1H-FID-MRSI dephased data

  • 期刊论文

  • 发表日期：

    2026年5月14日

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  • Alessio Siviglia,
  • Brayan Alves,
  • Cristina Cudalbu,
  • Bernard Lanz

摘要

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.