Proton NMR chemical shifts and coupling constants for brain metabolites

Govind, Varan; Young, Karl; Maudsley, Andrew A.

doi:10.1002/1099-1492(200005)13:3<129::aid-nbm619>3.0.co;2-v

Cited by 1,524 publications

(1,238 citation statements)

References 150 publications

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“…Proton MR spectra contain information from important brain metabolites like glutamate, myo-inositol, choline, creatine, N-acetyl aspartate (NAA), lactate and fatty acids. 4 Therefore MRS can be used to non-invasively investigate diseases related to cell membrane proliferation, neuronal damage and energy metabolism of brain. Proton MRS can be extended to an imaging mode called MRS imaging (MRSI), in which the acquisition of spectra from multiple voxels provides spatially encoded chemical information of the brain.…”

Section: Introductionmentioning

confidence: 99%

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

et al. 2005

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The purpose of this paper is to evaluate the effect of the combination of magnetic resonance spectroscopic imaging (MRSI) data and magnetic resonance imaging (MRI) data on the classification result of four brain tumor classes. Suppressed and unsuppressed short echo time MRSI and MRI were performed on 24 patients with a brain tumor and four volunteers. Four different feature reduction procedures were applied to the MRSI data: simple quantitation, principal component analysis, independent component analysis and LCModel. Water intensities were calculated from the unsuppressed MRSI data. Features were extracted from the MR images which were acquired with four different contrasts to comply with the spatial resolution of the MRSI. Evaluation was performed by investigating different combinations of the MRSI features, the MRI features and the water intensities. For each data set, the isolation in feature space of the tumor classes, healthy brain tissue and cerebrospinal fluid was calculated and visualized. A test set was used to calculate classification results for each data set. Finally, the effect of the selected feature reduction procedures on the MRSI data was investigated to ascertain whether it was more important than the addition of MRI information. Conclusions are that the combination of features from MRSI data and MRI data improves the classification result considerably when compared with features obtained from MRSI data alone. This effect is larger than the effect of specific feature reduction procedures on the MRSI data. The addition of water intensities to the data set also increases the classification result, although not significantly. We show that the combination of data from different MR investigations can be very important for brain tumor classification, particularly if a large number of tumors are to be classified simultaneously.

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Section: Introductionmentioning

confidence: 99%

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

et al. 2005

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“…Subsequently, the chemical shift values were determined by a C++ program using a simplex algorithm. This optimisation procedure minimised the difference between the experimental and fitted spectra calculated by the GAMMA NMR library [41] and using the J-coupling constants published in [12,13] as prior knowledge. Downfield signals were neglected because their in vivo observation is hampered for most metabolites by short T 2 , exchange processes with water, and overlapping with other resonances [12,43].…”

Section: Data Processing and Fittingmentioning

confidence: 99%

“…The basis sets of metabolite profiles were simulated for the upfield range using the chemical shifts and J-coupling constants for 37 °C [12,13] or the temperature matched chemical shift values for other temperatures. All basis sets were simulated for a constant concentration.…”

Section: Simulations and Quantificationsmentioning

confidence: 99%

“…Thus, well established quantification programs such as LCModel [8], QUEST [9,10], or AQSES [11] use a model function for each metabolite to minimise the number of variables during the fitting procedure. These model functions are either measured on phantom solutions or simulated using published values of chemical shifts and J-coupling constants as prior knowledge [12,13]. The similarities and differences between AQSES, which was used in this study, and other quantification methods have been described by Poullet et al [11].…”

Section: U N C O R R E C T E D P R O O F Introductionmentioning

confidence: 97%

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Temperature dependence of 1H NMR chemical shifts and its influence on estimated metabolite concentrations

Wermter

Mitschke

Bock

et al. 2017

Magn Reson Mater Phy

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“…The number of centers in these 2 and 3 overlapping peaks networks was 344 and 500 respectively. In the current studies, the 2.25 − 2.5 ppm spectral region that has three resonances [33][34][35][36] from glutamate and glutamine (collectively referred to as Glx) was quantified using three overlapping peaks. The myo-inositol (3.52 ppm and 3.61 ppm) peaks [33,36] were quantified by the network based on 2 overlapping peaks.…”

Section: Positions and Widths Of Basis Functions-mentioning

confidence: 99%

Fast quantification of proton magnetic resonance spectroscopic imaging with artificial neural networks

Bhat

Sajja

Narayana

2006

Journal of Magnetic Resonance

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Accurate quantification of the MRSI-observed regional distribution of metabolites involves relatively long processing times. This is particularly true in dealing with large amount of data that is typically acquired in multi-center clinical studies. To significantly shorten the processing time, an artificial neural network (ANN) based approach was explored for quantifying the phase corrected (as opposed to magnitude) spectra. Specifically, in these studies radial basis function neural network (RBFNN) was used. This method was tested on simulated and normal human brain data acquired at 3T. The N-acetyl aspartate (NAA)/ creatine (Cr), choline (Cho)/Cr, Glutamate + Glutamine (Glx)/Cr, and Myo-inositol (mI)/Cr ratios in normal subjects were compared with the line fitting (LF) technique and jMRUI-AMARES analysis, and published values. The average NAA/Cr, Cho/Cr, Glx/Cr and mI/Cr ratios in normal controls were found to be 1.58 ± 0.13, 0.9 ± 0.08, 0.7 ± 0.17 and 0.42 ± 0.07 respectively. The corresponding ratios using the LF and jMRUI-AMARES methods were 1.6 ± 0.11, 0.95 ± 0.08, 0.78 ± 0.18, 0.49 ± 0.1 and 1.61 ± 0.15, 0.78 ± 0.07, 0.61 ± 0.18, 0.42 ± 0.13 respectively. These results agree with those published in literature. Bland-Altman analysis indicated an excellent agreement and minimal bias between the results obtained with RBFNN and other methods. The computational time for the current method was 15 seconds compared to approximately 10 minutes for the LF-based analysis.

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Proton NMR chemical shifts and coupling constants for brain metabolites

Cited by 1,524 publications

References 150 publications

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

Temperature dependence of 1H NMR chemical shifts and its influence on estimated metabolite concentrations

Fast quantification of proton magnetic resonance spectroscopic imaging with artificial neural networks

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