From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods

Hagberg, Gisela E.

doi:10.1002/(sici)1099-1492(199806/08)11:4/5<148::aid-nbm511>3.0.co;2-4

Cited by 97 publications

(62 citation statements)

References 43 publications

(29 reference statements)

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“…The -errors are between 8 and 25%. There is virtually no difference between nonnormalized (method 1-4) and normalized methods (5)(6)(7)(8). When information from the images is also used during classification, the -error ranges between 6 and 12% and the -error between 4 and 8%.…”

Section: Resultsmentioning

confidence: 99%

“…The interpretation of this data is difficult and subjective and therefore classification based on pattern recognition is under development. [5][6][7][8][9][10] At the moment, most MR-related strategies to obtain information about brain tumors use either MRI or MRSI. A combination of both data sources has not been extensively investigated yet.…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

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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“…Height of real part = a α [6] The spectral data with peaks, NAA, Cr and Cho were simulated using Eqs. [5] and [6].…”

Section: Data Processingmentioning

confidence: 99%

“…[5] and [6]. These simulations were only meant to compare the Wiener filtering approach with inverse filtering with a fixed threshold.…”

Section: Data Processingmentioning

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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Towards a method for automated classification of1H MRS spectra from brain tumours

et al. 1998

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From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods

Cited by 97 publications

References 43 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

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

Towards a method for automated classification of1H MRS spectra from brain tumours

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