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NMF in MR Spectroscopy

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Non-negative Matrix Factorization Techniques

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

Nowadays, magnetic resonance spectroscopy (MRS) represents a powerful nuclear magnetic resonance (NMR) technique in oncology since it provides information on the biochemical profile of tissues, thereby allowing clinicians and radiologists to identify in a non-invasive way the different tissue types characterising the sample under investigation. The main purpose of the present chapter is to provide a review of the most recent and significant applications of non-negative matrix factorisation (NMF) to MRS data in the field of tissue typing methods for tumour diagnosis. Specifically, NMF-based methods for the recovery of constituent spectra in ex vivo and in vivo brain MRS data, for brain tissue pattern differentiation using magnetic resonance spectroscopic imaging (MRSI) data and for automatic detection and visualisation of prostate tumours, will be described. Furthermore, since several NMF implementations are available in the literature, a comparison in terms of pattern detection accuracy of some NMF algorithms will be reported and discussed, and the NMF performance for MRS data analysis will be compared with that of other blind source separation (BSS) techniques.

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Acknowledgments

The work of Teresa Laudadio is partly supported by the following projects: PRIN 2012 entitled SMaSIP (Structured matrices in Signal and Image Processing) and Progetto Premiale MIUR 2012 entitled MATHTECH.

The work of Yuqian Li is supported by Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 61401068) and by the China Postdoctoral Science Foundation (Grant No. 2014M552341).

The work of Anca Croitor, Nicolas Sauwen, Diana Sima and Sabine Van Huffel is supported by Research Council KUL: GOA/10/09 MaNet, CoE PFV/10/002 (OPTEC); PhD/Postdoc grants. Flemish Government: FWO: projects: G.0427.10N (Integrated EEG-fMRI), G.0108.11 (Compressed Sensing) G.0869.12N (Tumor imaging) G.0A5513N (Deep brain stimulation); PhD/Postdoc grants; IWT: projects: TBM 080658-MRI (EEG-fMRI), TBM 110697-NeoGuard; PhD/Postdoc grants; iMinds Medical Information Technologies SBO 2014, ICON: NXT_Sleep; Flanders Care: Demonstratieproject Tele-Rehab III (2012–2014). Belgian Federal Science Policy Office: IUAP P7/19/(DYSCO, ‘Dynamical systems, control and optimization’, 2012–2017). Belgian Foreign Affairs-Development Cooperation: VLIR UOS programmes. EU: The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC Advanced Grant: BIOTENSORS (no. 339804). This paper reflects only the authors’ views and the Union is not liable for any use that may be made of the contained information. Other EU funding: RECAP 209G within INTERREG IVB NWE programme, EU MC ITN TRANSACT 2012 (no. 316679), ERASMUS EQR: Community service engineer (no. 539642-LLP-1-2013).

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Authors and Affiliations

  1. Istituto per le Applicazioni del Calcolo “M. Picone” (IAC), Consiglio Nazionale delle Ricerche, Bari, Italy

    T. Laudadio

  2. Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium

    A. R. Croitor Sava, N. Sauwen, D. M. Sima & S. Van Huffel

  3. iMinds Medical Information Technologies, Leuven, Belgium

    A. R. Croitor Sava, N. Sauwen, D. M. Sima & S. Van Huffel

  4. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Y. Li

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  1. T. Laudadio
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Correspondence to T. Laudadio .

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  1. University of Technology Sydney, Sydney, New South Wales, Australia

    Ganesh R. Naik

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Laudadio, T., Sava, A.R.C., Li, Y., Sauwen, N., Sima, D.M., Van Huffel, S. (2016). NMF in MR Spectroscopy. In: Naik, G. (eds) Non-negative Matrix Factorization Techniques. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48331-2_7

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  • DOI: https://doi.org/10.1007/978-3-662-48331-2_7

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