Automated Quantification of Human Brain Metabolites by Artificial Neural Network Analysis fromin VivoSingle-Voxel1H NMR Spectra

Kaartinen, Jouni; Mierisová, Šárka; Oja, Joni M. E.; Usenius, J P; Kauppinen, Risto A.; Hiltunen, Yrjö

doi:10.1006/jmre.1998.1477

Cited by 25 publications

(16 citation statements)

References 15 publications

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“…The concept of using ANN for the analysis of MRSI is not new. Recently ANN based approaches for automated quantification of MRSI data have been reported [15,16]. As pointed out earlier these methods suffer from a number of limitations.…”

Section: Discussionmentioning

confidence: 99%

“…The ability of ANN for classification of spectra for various pathologies has been demonstrated [6][7][8][9][10][11][12][13][14]. However, relatively little attention has been paid to metabolite quantification using ANN [15][16]. Recently, ANN has been used to develop automated methods to quantify MRS data [15][16].…”

Section: Introductionmentioning

confidence: 99%

“…However, relatively little attention has been paid to metabolite quantification using ANN [15][16]. Recently, ANN has been used to develop automated methods to quantify MRS data [15][16]. These studies demonstrated a high correlation between the areas of metabolite peaks computed with ANN and LF methods.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bhat

Sajja

Narayana

2006

Journal of Magnetic Resonance

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“…In the first approach, tests of automatic quantification of NMR spectra using hierarchical neural networks were recently reported [3,4]. A three-layered network based on the back propagation method was employed [5] and the spectra in the frequency domain were used as the training data of the network.…”

Section: Introductionmentioning

confidence: 99%

A method of estimation of magnetic resonance spectroscopy using complex‐valued neural networks

Morita

Konishi

2004

Systems & Computers in Japan

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SUMMARYMRS (magnetic resonance spectroscopy) consists of collecting the nuclear magnetic resonance (NMR) spectrum of the metabolites in the living body and estimating it. In the field of MRS, we usually get an NMR spectrum by applying the Fourier transform to the real part of the NMR signal [free induction decay (FID) signal], which is a complex-valued signal. We must then measure the area of the spectral peak to estimate metabolites in the living body from the spectrum. A curve fitting technique is used for that measurement. However, this technique is not suitable for large quantities of data processing, because human intervention is necessary and is laborious. For this reason, an automatic method of estimating an NMR spectrum is required in the field of MRS. In this paper, we propose a method of estimation of an NMR spectrum by using a Hopfield complex-valued neural network and try to estimate the spectrum automatically. In addition, we consider damping state of the NMR signal and devise a method called sequential extension of section (SES). It is shown that the estimation precision of the spectrum improves when SES is used. Furthermore, SES is found to reduce the local minimum problem in Hopfield neural networks.

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“…Moreover, some studies [12,13,14] have shown that SVM were generally more efficient on whole data than data having undergone dimension reduction techniques. We chose to compare this method with a Multilayer Perceptron (MLP) that have already proved it qualities in medical applications [15,16].…”

Section: Introductionmentioning

confidence: 99%

Classification of prostate magnetic resonance spectra using Support Vector Machine

Parfait

Walker

Créhange

et al. 2012

Biomedical Signal Processing and Control

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Prostate cancer is the most common cancer in men over 50 years of age and it has been shown that nuclear magnetic resonance spectra are sensitive enough to distinguish normal and cancer. In this paper, we propose a classification technique of spectra from magnetic resonance spectroscopy. We studied automatic classification with and without quantification of metabolite signals. The dataset is composed of 22 patient datasets with a biopsy-proven cancer, from which we extracted 2464 spectra from the whole prostate and of which 1062 were localised in the peripheral zone. The spectra were manually classed into 3 different categories by a spectroscopist with 4 years experience in clinical spectroscopy of prostate cancer: undetermined, healthy and pathologic. We used different preprocessing methods (module, phase correction only, phase correction and baseline correction) as input for Support Vector Machine and for Multilayer Perceptron, and we compared the results with those from the expert. If we class only healthy and pathologic spectra we reach a total error rate of 4.51%. However, if we class all spectra (undetermined, healthy and pathologic) the total error rate rises to 11.49%. We have shown in this paper that the best results are obtained using the * Corresponding author * * Principal corresponding author Email addresses: sebastien.parfait@u-bourgogne.fr (S. Parfait), johel.miteran@u-bourgogne.fr (J. Mitéran) Preprint submitted to Biomedical Signal Processing and ControlDecember 12, 2011 pre-processed spectra without quantification as input for the classifiers and we confirm that Support Vector Machine are more efficient than Multilayer Perceptron in processing high dimensional data.

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Automated Quantification of Human Brain Metabolites by Artificial Neural Network Analysis fromin VivoSingle-Voxel1H NMR Spectra

Cited by 25 publications

References 15 publications

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

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

A method of estimation of magnetic resonance spectroscopy using complex‐valued neural networks

Classification of prostate magnetic resonance spectra using Support Vector Machine

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