A comparison between simulated and experimental basis sets for assessing short‐TE in vivo 1 H MRS data at 1.5 T

Magnetic Resonance in Med

Reynolds²,

Kauppinen³

et al. 2010

Self Cite

306

283

Totally Automatic Robust Quantitation in NMR (TARQUIN), a new method for the fully automatic analysis of short echo time in vivo 1 H Magnetic resonance spectroscopy is presented. Analysis is performed in the time domain using non-negative least squares, and a new method for applying soft constraints to signal amplitudes is used to improve fitting stability. Initial point truncation and Hankel singular value decomposition water removal are used to reduce baseline interference. Three methods were used to test performance. First, metabolite concentrations from six healthy volunteers at 3 T were compared with LCModel™. Second, a Monte-Carlo simulation was performed and results were compared with LCModel™ to test the accuracy of the new method. Finally, the new algorithm was applied to 1956 spectra, acquired clinically at 1.5 T, to test robustness to noisy, abnormal, artifactual, and poorly shimmed spectra. computationally efficient (HLSVD; Ref. 4) for in vivo data, and are effective at extracting peak parameters from simple spectra. One drawback of black-box methods is that additional knowledge of spectral features cannot be incorporated into the algorithm allowing infeasible results to be possible for more complex data. For example, an incorrect ratio between peaks originating from the same molecule is possible. The AMARES (5) algorithm was developed to address this issue by extending the VARPRO (6) peak-fitting method to allow a greater level of prior knowledge to be incorporated into the fitting model.Black-box and peak-fitting methods have been shown to be highly effective for sparse spectra such as long echo time (TE) 1 H or 31 P MRS; however, the complex patterns of some metabolites seen in short TE 1 H MRS data are cumbersome to model as a series of single peaks. Although long TE 1 H MRS is still popular, there is a growing trend to shorter TE (7) because of the increase in metabolic information. Therefore, analysis methods that are suited to this data type are becoming increasingly important. For complex data, methods that incorporate a metabolite basis set have been shown to be more effective than peak-fitting methods (8).LCModel™ (9) was one of the first algorithms to incorporate a metabolite basis set into the fitting model and is widely used for the analysis of short TE 1 H MRS data. The algorithm models data in the frequency domain using a linear combination of metabolite, lipid, and macromolecule signals combined with a smoothing splines to account for baseline signals. More recently, the Quantitation Based on Quantum Estimation (QUEST) (10) algorithm has been developed that uses a combination of time-domain fitting and HSVD to model background signals. An alternative approach is taken by Automated Quantitation of Short Echo time MRS Spectra (AQSES) (11) that uses a combination of time-domain fitting and penalized splines to model the baseline. AQSES also differs from LCModel™ and QUEST as it uses the variable projection method to estimate the amplitudes of the metabolite basis set resulting in a reduc...

Section: Methodsmentioning

confidence: 94%

A constrained least‐squares approach to the automated quantitation of in vivo ¹H magnetic resonance spectroscopy data

Magnetic Resonance in Med

Reynolds²,

Kauppinen³

et al. 2010

Self Cite

306

283

“…Metabolite basis sets can be acquired experimentally or simulated from known parameters, [36][37][38] and either approach is effective. 39,40 The addition of known MM and lipid signals to the basis set results in improved analysis, particularly for short-TE data sets or tumor spectral analyses. 41 B 0 inhomogeneity and artifacts originating from rapidly changing gradients, known as eddy currents, broaden and distort the MRS line shape from its ideal form (e.g., Lorentzian for singlet peaks).…”

Section: Discussionmentioning

confidence: 99%

“…Time‐domain analysis may also be used to reduce errors from baseline signals by omitting initial data points from the fit, exploiting the rapid temporal decay of baseline distortions (QUEST, TARQUIN). Metabolite basis sets can be acquired experimentally or simulated from known parameters, and either approach is effective . The addition of known MM and lipid signals to the basis set results in improved analysis, particularly for short‐TE data sets or tumor spectral analyses .…”

Section: Standard Mrs Methodologymentioning

confidence: 99%

Methodological consensus on clinical proton MRS of the brain: Review and recommendations

Magnetic Resonance in Med

Andronesi

Barker

et al. 2019

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348

450

Proton MRS (1H MRS) provides noninvasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Although most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges toward obtaining reliable good‐quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the PRESS localization sequence was found to be unacceptably high at 3 T, and use of the semi‐adiabatic localization by adiabatic selective refocusing sequence is a recommended solution. Incorporation of simulated metabolite basis sets into analysis routines is recommended for reliably capturing the full spectral detail available from short TE acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B0) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. Implementation of these recommendations should strengthen current clinical applications and advance progress toward developing and validating new MRS biomarkers for clinical use.

“…Such evidence includes Montecarlo simulations of normal brain spectra fitted with LCModel [12], comparisons of experimental and simulated basis sets [13], and the finding of a significant correlation between in vivo and in vitro results for both glutamate and glutamine detected in brain tumours [14,15]. …”

Section: Discussionmentioning

confidence: 99%

MR spectroscopy-based brain metabolite profiling in propionic acidaemia: metabolic changes in the basal ganglia during acute decompensation and effect of liver transplantation

Davison

Davies

et al. 2011

Orphanet J Rare Dis

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BackgroundPropionic acidaemia (PA) results from deficiency of Propionyl CoA carboxylase, the commonest form presenting in the neonatal period. Despite best current management, PA is associated with severe neurological sequelae, in particular movement disorders resulting from basal ganglia infarction, although the pathogenesis remains poorly understood. The role of liver transplantation remains controversial but may confer some neuro-protection. The present study utilises quantitative magnetic resonance spectroscopy (MRS) to investigate brain metabolite alterations in propionic acidaemia during metabolic stability and acute encephalopathic episodes.MethodsQuantitative MRS was used to evaluate brain metabolites in eight children with neonatal onset propionic acidaemia, with six elective studies acquired during metabolic stability and five studies during acute encephalopathic episodes. MRS studies were acquired concurrently with clinically indicated MR imaging studies at 1.5 Tesla. LCModel software was used to provide metabolite quantification. Comparison was made with a dataset of MRS metabolite concentrations from a cohort of children with normal appearing MR imaging.ResultsMRI findings confirm the vulnerability of basal ganglia to infarction during acute encephalopathy. We identified statistically significant decreases in basal ganglia glutamate+glutamine and N-Acetylaspartate, and increase in lactate, during encephalopathic episodes. In white matter lactate was significantly elevated but other metabolites not significantly altered. Metabolite data from two children who had received liver transplantation were not significantly different from the comparator group.ConclusionsThe metabolite alterations seen in propionic acidaemia in the basal ganglia during acute encephalopathy reflect loss of viable neurons, and a switch to anaerobic respiration. The decrease in glutamine + glutamate supports the hypothesis that they are consumed to replenish a compromised Krebs cycle and that this is a marker of compromised aerobic respiration within brain tissue. Thus there is a need for improved brain protective strategies during acute metabolic decompensations. MRS provides a non-invasive tool for which could be employed to evaluate novel treatments aimed at restoring basal ganglia homeostasis. The results from the liver transplantation sub-group supports the hypothesis that liver transplantation provides systemic metabolic stability by providing a hepatic pool of functional propionyl CoA carboxylase, thus preventing further acute decompensations which are associated with the risk of brain infarction.