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    Semi-Classical Signal Analysis Method with Soft-Thresholding for MRS denoising

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    18-11-06-Abstract2-Complex_MRS_Denoising_Submitted.pdf
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    ISMRM2019_poster_SCSA_MRS_Denoising (3).pdf
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    Type
    Poster
    Authors
    Laleg-Kirati, Taous-Meriem cc
    Chahid, Abderrazak cc
    Bhaduri, Sourav
    Wali, Malik
    Achten, Eric
    Serrai, Hacene
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-11-06
    Permanent link to this record
    http://hdl.handle.net/10754/655506
    
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    Abstract
    A Semi-Classical Signal Analysis (SCSA) method with soft thresholding is proposed for MRSI denoising. The SCSA takes advantage of the pulseshaped MRS spectrum to decompose both real and imaginary parts, into localized basis given by squared eigenfunctions of the Schrödinger operator. An optimization-based soft-threshold is provided to find optimal semi-classical parameters, for both the real and imaginary parts of the MRS signal. The optimal SCSA parameters discard the eigenfunctions representing noise from the noisy spectrum, and conserve the eigenfunctions representing the useful information. The obtained in-vivo results show the efficiency of the SCSA with soft thresholding in removing noise and conserving metabolite signals.
    Conference/Event name
    ISMRM 27th Annual Meeting & Exhibition
    Collections
    Posters; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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