• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Spectral data de-noising using semi-classical signal analysis: application to localized MRS

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Article
    Authors
    Laleg-Kirati, Taous-Meriem cc
    Zhang, Jiayu
    Achten, Eric
    Serrai, Hacene
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Computational Bioscience Research Center (CBRC)
    Date
    2016-09-05
    Online Publication Date
    2016-09-05
    Print Publication Date
    2016-10
    Permanent link to this record
    http://hdl.handle.net/10754/622154
    
    Metadata
    Show full item record
    Abstract
    In this paper, we propose a new post-processing technique called semi-classical signal analysis (SCSA) for MRS data de-noising. Similar to Fourier transformation, SCSA decomposes the input real positive MR spectrum into a set of linear combinations of squared eigenfunctions equivalently represented by localized functions with shape derived from the potential function of the Schrodinger operator. In this manner, the MRS spectral peaks represented as a sum of these 'shaped like' functions are efficiently separated from noise and accurately analyzed. The performance of the method is tested by analyzing simulated and real MRS data. The results obtained demonstrate that the SCSA method is highly efficient in localized MRS data de-noising and allows for an accurate data quantification.
    Citation
    Laleg-Kirati T-M, Zhang J, Achten E, Serrai H (2016) Spectral data de-noising using semi-classical signal analysis: application to localized MRS. NMR in Biomedicine 29: 1477–1485. Available: http://dx.doi.org/10.1002/nbm.3590.
    Sponsors
    The first and second authors would like to thank King Abdullah University of Science and Technology (KAUST) for its financial support and Dr S. Van Huell from University of Leuven for the use of the SVD software.
    Publisher
    Wiley
    Journal
    NMR in Biomedicine
    DOI
    10.1002/nbm.3590
    PubMed ID
    27593698
    Additional Links
    http://onlinelibrary.wiley.com/doi/10.1002/nbm.3590/abstract
    ae974a485f413a2113503eed53cd6c53
    10.1002/nbm.3590
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

    Related articles

    • A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator.
    • Authors: Chahid A, Serrai H, Achten E, Laleg-Kirati TM
    • Issue date: 2018 Jul
    • Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain.
    • Authors: Near J, Edden R, Evans CJ, Paquin R, Harris A, Jezzard P
    • Issue date: 2015 Jan
    • 3D localized 2D ultrafast J-resolved magnetic resonance spectroscopy: in vitro study on a 7 T imaging system.
    • Authors: Roussel T, Giraudeau P, Ratiney H, Akoka S, Cavassila S
    • Issue date: 2012 Feb
    • [A new de-noising technique for spectra based on Mexican hat wavelet].
    • Authors: Wang Y, Mo JY
    • Issue date: 2005 Jan
    • Robust estimation of ultrasound pulses using outlier-resistant de-noising.
    • Authors: Michailovich O, Adam D
    • Issue date: 2003 Mar
    DSpace software copyright © 2002-2022  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.