Magnetic resonance spectroscopic imaging reconstruction with deformable shape-intensity models

Xiao Ping Zhu; An Tao Du; Geon Ho Jahng; Brian J. Soher; Andrew A. Maudsley; Michael W. Weiner; Norbert Schuff

doi:10.1002/mrm.10572

Magnetic resonance spectroscopic imaging reconstruction with deformable shape-intensity models

Xiao Ping Zhu, An Tao Du, Geon Ho Jahng, Brian J. Soher, Andrew A. Maudsley, Michael W. Weiner, Norbert Schuff

Research output: Contribution to journal › Article › peer-review

Abstract

A new method, based on a deformable shape-intensity model (DSM), was developed to improve the signal-to-noise ratio (SNR) of multidimensional magnetic resonance spectroscopic imaging (MRSI) data sets without affecting spectral lineshapes and linewidths. Improvements with DSM, compared to digital filters using conventional signal apodization, were demonstrated on both simulated and experimental in vivo 1H MRS images from 22 cognitively normal (CN) elderly subjects and 25 patients with Alzheimer's disease (AD). Simulated MRSI data showed that DSM achieved superior noise suppression compared to a matched apodization filter. Experimental MRSI data showed that SNR could be increased 2.1-fold with DSM without distorting spectral resolution, thus maintaining all spectral features of the raw, unfiltered data. In conclusion, DSM should be used to achieve high SNR in reconstructing MRSI data. © 2003 Wiley-Liss, Inc.

Original language	English
Pages (from-to)	474-482
Number of pages	8
Journal	Magnetic Resonance in Medicine
Volume	50
Issue number	3
DOIs	https://doi.org/10.1002/mrm.10572
Publication status	Published - 1 Sept 2003

Keywords

Deformable shape-intensity model
Magnetic resonance spectroscopic imaging
Noise reduction
Spectral analysis

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10.1002/mrm.10572

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title = "Magnetic resonance spectroscopic imaging reconstruction with deformable shape-intensity models",

abstract = "A new method, based on a deformable shape-intensity model (DSM), was developed to improve the signal-to-noise ratio (SNR) of multidimensional magnetic resonance spectroscopic imaging (MRSI) data sets without affecting spectral lineshapes and linewidths. Improvements with DSM, compared to digital filters using conventional signal apodization, were demonstrated on both simulated and experimental in vivo 1H MRS images from 22 cognitively normal (CN) elderly subjects and 25 patients with Alzheimer's disease (AD). Simulated MRSI data showed that DSM achieved superior noise suppression compared to a matched apodization filter. Experimental MRSI data showed that SNR could be increased 2.1-fold with DSM without distorting spectral resolution, thus maintaining all spectral features of the raw, unfiltered data. In conclusion, DSM should be used to achieve high SNR in reconstructing MRSI data. {\textcopyright} 2003 Wiley-Liss, Inc.",

keywords = "Deformable shape-intensity model, Magnetic resonance spectroscopic imaging, Noise reduction, Spectral analysis",

author = "Zhu, {Xiao Ping} and Du, {An Tao} and Jahng, {Geon Ho} and Soher, {Brian J.} and Maudsley, {Andrew A.} and Weiner, {Michael W.} and Norbert Schuff",

year = "2003",

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doi = "10.1002/mrm.10572",

language = "English",

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T1 - Magnetic resonance spectroscopic imaging reconstruction with deformable shape-intensity models

AU - Zhu, Xiao Ping

AU - Du, An Tao

AU - Jahng, Geon Ho

AU - Soher, Brian J.

AU - Maudsley, Andrew A.

AU - Weiner, Michael W.

AU - Schuff, Norbert

PY - 2003/9/1

Y1 - 2003/9/1

N2 - A new method, based on a deformable shape-intensity model (DSM), was developed to improve the signal-to-noise ratio (SNR) of multidimensional magnetic resonance spectroscopic imaging (MRSI) data sets without affecting spectral lineshapes and linewidths. Improvements with DSM, compared to digital filters using conventional signal apodization, were demonstrated on both simulated and experimental in vivo 1H MRS images from 22 cognitively normal (CN) elderly subjects and 25 patients with Alzheimer's disease (AD). Simulated MRSI data showed that DSM achieved superior noise suppression compared to a matched apodization filter. Experimental MRSI data showed that SNR could be increased 2.1-fold with DSM without distorting spectral resolution, thus maintaining all spectral features of the raw, unfiltered data. In conclusion, DSM should be used to achieve high SNR in reconstructing MRSI data. © 2003 Wiley-Liss, Inc.

AB - A new method, based on a deformable shape-intensity model (DSM), was developed to improve the signal-to-noise ratio (SNR) of multidimensional magnetic resonance spectroscopic imaging (MRSI) data sets without affecting spectral lineshapes and linewidths. Improvements with DSM, compared to digital filters using conventional signal apodization, were demonstrated on both simulated and experimental in vivo 1H MRS images from 22 cognitively normal (CN) elderly subjects and 25 patients with Alzheimer's disease (AD). Simulated MRSI data showed that DSM achieved superior noise suppression compared to a matched apodization filter. Experimental MRSI data showed that SNR could be increased 2.1-fold with DSM without distorting spectral resolution, thus maintaining all spectral features of the raw, unfiltered data. In conclusion, DSM should be used to achieve high SNR in reconstructing MRSI data. © 2003 Wiley-Liss, Inc.

KW - Deformable shape-intensity model

KW - Magnetic resonance spectroscopic imaging

KW - Noise reduction

KW - Spectral analysis

U2 - 10.1002/mrm.10572

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SP - 474

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JO - Magnetic Resonance in Medicine

JF - Magnetic Resonance in Medicine

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ER -

Magnetic resonance spectroscopic imaging reconstruction with deformable shape-intensity models

Abstract

Keywords

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