Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets

An Erratum to this article was published on 03 May 2011

Abstract

Object

To propose the determination of the macromolecular baseline (MMBL) in clinical 1H MR spectra based on T1 and T2 differentiation using 2D fitting in FiTAID, a general Fitting Tool for Arrays of Interrelated Datasets.

Materials and methods

Series of localized inversion-recovery (IR) and 2DJ separation spectra of the brain were recorded at 3T. The MMBL was determined by three 2D evaluation methods based on (1) IR spectra only, (2) 2DJ spectra only, (3) both IR and 2DJ spectra (2DJ-IR). Their performance was compared using synthetic spectra and based on variability and reproducibility as obtained in vivo from 12 subjects in 20 examinations.

Results

All methods performed well for synthetic data. In vivo, 2DJ-only yielded larger variations than the other methods. IR-only and 2DJ-IR yielded similar performance. FiTAID is illustrated with further applications where linear-combination model fitting of interrelated arrays of spectra is advantageous.

Conclusion

2D-Fitting offers the possibility to determine the MMBL based on a range of complementary experimental spectra not relying on smoothness criteria or global assumptions on T1. Since 2DJ-IR includes information from spectra with different inversion and echo times, it is expected to be more robust in cases with more variable data quality and overlap with lipid resonances.

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Correspondence to Roland Kreis.

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An erratum to this article can be found at http://dx.doi.org/10.1007/s10334-011-0253-z

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Chong, D.G.Q., Kreis, R., Bolliger, C.S. et al. Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets. Magn Reson Mater Phy 24, 147–164 (2011). https://doi.org/10.1007/s10334-011-0246-y

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Keywords

  • Magnetic resonance spectroscopy
  • Signal processing
  • Computer-Assisted
  • Quantification
  • Spectral fitting
  • Spectral analysis