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

Taous-Meriem Laleg-Kirati; Jiayu Zhang; Eric Achten; Hacene Serrai

doi:10.1002/nbm.3590

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

Taous-Meriem Laleg-Kirati, Jiayu Zhang, Eric Achten, Hacene Serrai

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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.

Original language	English (US)
Pages (from-to)	1477-1485
Number of pages	9
Journal	NMR in Biomedicine
Volume	29
Issue number	10
DOIs	https://doi.org/10.1002/nbm.3590
State	Published - Sep 5 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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.

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10.1002/nbm.3590

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title = "Spectral data de-noising using semi-classical signal analysis: application to localized MRS",

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.",

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note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: 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.",

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AU - Laleg-Kirati, Taous-Meriem

AU - Zhang, Jiayu

AU - Achten, Eric

AU - Serrai, Hacene

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: 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.

PY - 2016/9/5

Y1 - 2016/9/5

N2 - 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.

AB - 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.

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JO - NMR in Biomedicine

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

Abstract

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