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dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.contributor.authorChahid, Abderrazak
dc.contributor.authorBhaduri, Sourav
dc.contributor.authorWali, Malik
dc.contributor.authorAchten, Eric
dc.contributor.authorSerrai, Hacene
dc.date.accessioned2019-06-09T10:58:50Z
dc.date.available2019-06-09T10:58:50Z
dc.date.issued2018-11-06
dc.identifier.urihttp://hdl.handle.net/10754/655506
dc.description.abstractA 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.
dc.titleSemi-Classical Signal Analysis Method with Soft-Thresholding for MRS denoising
dc.typePoster
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.dateMay 11, 2019 - May 16, 2019
dc.conference.nameISMRM 27th Annual Meeting & Exhibition
dc.conference.locationMontréal, QC, Canada
dc.contributor.institutionUniversity of Western Ontario
dc.contributor.institutionUniversity of Ghent
refterms.dateFOA2019-06-09T10:58:50Z


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