Quantification by the fast Padé transform of magnetic resonance spectroscopic data encoded at 1.5 T: implications for brain tumor diagnostics

Belkić, Dževad; Belkić, Karen

doi:10.1007/s10910-015-0578-3

Cited by 13 publications

(1 citation statement)

References 156 publications

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“…Clinically, MRSI can detect metabolic changes in strokes [77], autism [78], brain tumors [79], multiple sclerosis [80], seizure [81], depression [82], Parkinson's disease [83], Kimura disease [84], etc. Table 4 lists the identifiable metabolites (N-acetyl aspartate, choline, creatine, lipid, lactate, myoinositol, glutamate and glutamine), and gives their peak positions and indications of change.…”

Section: Other Modalitiesmentioning

confidence: 99%

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Wang¹,

Zhang²,

Zhan³

et al. 2016

PIER

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Abstract-(Aim) Pathological brain detection (PBD) systems aim to assist and even replace neuroradiologists to make decisions for patients. This review offers a comprehensive and quantitative comparison for PBD systems by artificial intelligence in magnetic resonance imaging (MRI) scanning. (Method) We first investigated four categories of brain diseases, including neoplastic disease, neurodegenerative disease, cerebrovascular disease, and inflammation. Next, we introduced important MRI techniques, such as the shimming, water and fat suppression, and three advanced imaging modalities (functional MRI, diffusion tensor imaging, and magnetic resonance spectroscopic imaging). Then, we discussed four image preprocessing techniques (image denoising, slice selection, brain extraction, spatial normalization, and intensity normalization), seven feature representation techniques (shape, moment, wavelet, statistics, entropy, gray level co-occurrence matrix, and Fourier transform), and two dimension reduction techniques (feature selection and feature extraction). Afterwards, we studied classification related methods: six learning models (decision tree, extreme learning machine, k-nearest neighbors, naive Bayes classifier, support vector machine, feed-forward neural network), five kernel functions (linear, homogeneous and inhomogeneous polynomial, radial basis function, and sigmoid), and three types of optimization methods (evolutionary algorithm, stochastic optimization, and swarm intelligence). (Results) We introduced three benchmark datasets and used K-fold stratified cross validation to avoid overfitting. We presented a detailed quantitative comparison among 44 state-of-theart PBD algorithms and discussed their advantages and limitations. (Discussions) Artificial intelligence is now making stride in the PBD field and enjoys a fair amount of success. In the future, semi-supervised learning and transfer learning techniques may be potential breakthroughs to develop PBD systems.

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Section: Other Modalitiesmentioning

confidence: 99%

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Wang¹,

Zhang²,

Zhan³

et al. 2016

PIER

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Improving the diagnostic yield of magnetic resonance spectroscopy for pediatric brain tumors through mathematical optimization

Belkić

2016

J Math Chem

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Iterative averaging of spectra as a powerful way of suppressing spurious resonances in signal processing

Belkić

2016

J Math Chem

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The fast Padé transform (FPT) was applied to magnetic resonance spectroscopic (MRS) time signals encoded in vivo on a 1.5 T scanner from the parietal-temporal brain region of a pediatric patient who had suffered cerebral asphyxia. An iterative averaging procedure was implemented to the 9th iteration, whereby spurious structures on the total shape spectra were effectively suppressed. The parametric and non-parametric FPT (±) were verified to reconstruct equivalent total shape spectra. Via the parametric FPT, the spectral region of interest was chosen to bypass the giant water resonance, automatically generating spectral envelopes without the need for windowing. The dense component spectra were reliably reconstructed by the FPT (+) , in the "usual" mode (mixture of absorption and dispersion components) and "ersatz" mode (reconstructed phases set to zero). Via the latter, interference effects were well-visualized for closely-overlapping and hidden resonances. The most stringent test was performed for the complex frequencies and associated complex amplitudes reconstructed by the FPT (+) . Exceedingly small variances were obtained for all four Padé-reconstructed parameters per genuine resonance, once convergence was achieved at the 7th to 9th iterated averages. This now fully-validated methodology can generate denoised spectra and accurate spectral parameters for in vivo MRS data encoded within neurodiagnostics. Such a multi-faceted Padé-based strategy for processing the dense spectra of the brain could vitally improve pediatric neurodiagnostics. A wider range of clinical applications becomes within reach, including areas of cancer diagnostics where the added value of in vivo MRS is urgently needed. The broad theoretical underpinnings of incorporating quantum mechanics into signal processing provide the basis for these innovative advances.

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Quantification by the fast Padé transform of magnetic resonance spectroscopic data encoded at 1.5 T: implications for brain tumor diagnostics

Cited by 13 publications

References 156 publications

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Improving the diagnostic yield of magnetic resonance spectroscopy for pediatric brain tumors through mathematical optimization

Iterative averaging of spectra as a powerful way of suppressing spurious resonances in signal processing

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