MR spectroscopy quantitation: a review of time‐domain methods

Vanhamme, Leentje; Sundin, Tomas; Hecke, Paul Van; Huffel, Sabine Van

doi:10.1002/nbm.695

Cited by 198 publications

(171 citation statements)

References 84 publications

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“…A number of approaches have been suggested for performing this step in the brain, and these various approaches applied to processing and fitting spectral data were recently reviewed in a series of articles devoted to spectral quantitation. [117][118][119][120] One tool that appears to be gaining wide acceptance is LC-Model. We routinely use LC-Model to automatically fit CSI data sets obtained from the brain, using a version similar to that described by McLean et al 121,122 An example of the results of a fit of LC-Model to a brain spectrum obtained at 3 T from a normal volunteer is shown in Figure 4.…”

Section: Quantitationmentioning

confidence: 99%

Recent Advances in Magnetic Resonance Neurospectroscopy

Rosen

Lenkinski

2007

Neurotherapeutics

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Summary:Over the past two decades, proton magnetic resonance spectroscopy (proton MRS) of the brain has made the transition from research tool to a clinically useful modality. In this review, we first describe the localization methods currently used in MRS studies of the brain and discuss the technical and practical factors that determine the applicability of the methods to particular clinical studies. We also describe each of the resonances detected by localized solvent-suppressed proton MRS of the brain and discuss the metabolic and biochemical information that can be derived from an analysis of their concentrations. We discuss spectral quantitation and summarize the reproducibility of both single-voxel and multivoxel methods at 1.5 and 3-4 T. We have selected three clinical neurologic applications in which there has been a consensus as to the diagnostic value of MRS and summarize the information relevant to clinical applications. Finally, we speculate about some of the potential technical developments, either in progress or in the future, that may lead to improvements in the performance of proton MRS.

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Section: Quantitationmentioning

confidence: 99%

Recent Advances in Magnetic Resonance Neurospectroscopy

Rosen

Lenkinski

2007

Neurotherapeutics

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“…The first echo of each acquisition was removed because it disrupted the fit. Subsequently, the complex signal of the echoes of each voxel was fit in a least-squares manner to a two Gaussian peak model (24):…”

Section: Methodsmentioning

confidence: 99%

Compensating for magnetic field inhomogeneity in multigradient-echo-based MR thermometry

et al. 2014

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Purpose: MR thermometry (MRT) is a noninvasive method for measuring temperature that can potentially be used for radio frequency (RF) safety monitoring. This application requires measuring absolute temperature. In this study, a multigradientecho (mGE) MRT sequence was used for that purpose. A drawback of this sequence, however, is that its accuracy is affected by background gradients. In this article, we present a method to minimize this effect and to improve absolute temperature measurements using MRI. Theory: By determining background gradients using a B 0 map or by combining data acquired with two opposing readout directions, the error can be removed in a homogenous phantom, thus improving temperature maps. Methods: All scans were performed on a 3T system using ethylene glycol-filled phantoms. Background gradients were varied, and one phantom was uniformly heated to validate both compensation approaches. Independent temperature recordings were made with optical probes. Results: Errors correlated closely to the background gradients in all experiments. Temperature distributions showed a much smaller standard deviation when the corrections were applied (0.21 C vs. 0.45 C) and correlated well with thermo-optical probes. Conclusion: The corrections offer the possibility to measure RF heating in phantoms more precisely. This allows mGE MRT to become a valuable tool in RF safety assessment. Magn

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“…As can be seen from Eq. [3], 2 3 ϱ corresponds to the case where the estimated baseline vanishes. This means that baseline features tend to be negligible compared with the noise.…”

Section: Determination Of Uncertaintiesmentioning

confidence: 99%

Quantitative magnetic resonance spectroscopy: Semi‐parametric modeling and determination of uncertainties

Elster

Schubert

Link

et al. 2005

Magnetic Resonance in Med

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A semi-parametric approach for the quantitative analysis of magnetic resonance (MR) spectra is proposed and an uncertainty analysis is given. Single resonances are described by parametric models or by parametrized in vitro spectra and the baseline is determined nonparametrically by regularization. By viewing baseline estimation in a reproducing kernel Hilbert space, an explicit parametric solution for the baseline is derived. A Bayesian point of view is adopted to derive uncertainties, and the many parameters associated with the baseline solution are treated as nuisance parameters. The derived uncertainties formally reduce to Cramé r-Rao lower bounds for the parametric part of the model in the case of a vanishing baseline.The proposed uncertainty calculation was applied to simulated and measured MR spectra and the results were compared to Cramé r-Rao lower bounds derived after the nonparametrically estimated baselines were subtracted from the spectra. In particular, for high SNR and strong baseline contributions the proposed procedure yields a more appropriate characterization of the accuracy of parameter estimates than Cramé r-Rao lower bounds, which tend to overestimate accuracy. Accurate quantitative analysis of in vivo 1 H magnetic resonance spectra is a challenging task and has been the subject of research for a long time; see (1-3) for reviews. In addition to technical advances, such as higher field strength (4) and novel pulse sequences (5), the development of sophisticated quantification approaches (6 -16) has greatly contributed to establishing magnetic resonance spectroscopy (MRS) as a noninvasive tool for medical diagnosis and biochemical analysis. Further improvement of quantification methods is still a topic of interest, and to date a wide variety of methods are in use. Consequently, considerable effort has been undertaken in comparing different approaches of spectrum analysis (17)(18)(19)(20).Typically, parametric models including (parametrized) in vitro spectra are utilized for quantitative analysis of magnetic resonance (MR) spectra. Determination of the unknown MRS parameters is usually done by (nonlinear) least-squares fitting, either in the time or in the frequency domain. A serious difficulty arises-in particular when analyzing spectra acquired at short echo times (TE)-due to the presence of baseline contributions caused by residual water and macromolecules. Generally, no parametric model is available for these baseline contributions, but they can be assumed to be smooth in the frequency domain. Several methods have been proposed for dealing with this task; e.g., successful baseline treatment has been carried out by spline approximation (7) and application of wavelets (12), but incorporation of measured baselines has also been suggested (4,13,14).Characterizing the accuracies of calculated MRS parameter estimates is an important part of quantitative analysis, and reliable uncertainty calculation is mandatory for assessing or comparing individual results. Usually, accuracies of MRS parameter e...

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MR spectroscopy quantitation: a review of time‐domain methods

Cited by 198 publications

References 84 publications

Recent Advances in Magnetic Resonance Neurospectroscopy

Recent Advances in Magnetic Resonance Neurospectroscopy

Compensating for magnetic field inhomogeneity in multigradient-echo-based MR thermometry

Quantitative magnetic resonance spectroscopy: Semi‐parametric modeling and determination of uncertainties

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