Sparse spectral deconvolution algorithm for noncartesian MR spectroscopic imaging.
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| Title: | Sparse spectral deconvolution algorithm for noncartesian MR spectroscopic imaging. |
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| Authors: | Bhave, Sampada1, Eslami, Ramin2, Jacob, Mathews1 |
| Source: | Magnetic Resonance in Medicine. Feb2014, Vol. 71 Issue 2, p469-476. 8p. |
| Abstract: | Purpose To minimize line shape distortions and spectral leakage artifacts in MR spectroscopic imaging (MRSI). Methods A spatially and spectrally regularized non-Cartesian MRSI algorithm that uses the line shape distortion priors, estimated from water reference data, to deconvolve the spectra is introduced. Sparse spectral regularization is used to minimize noise amplification associated with deconvolution. A spiral MRSI sequence that heavily oversamples the central k-space regions is used to acquire the MRSI data. The spatial regularization term uses the spatial supports of brain and extracranial fat regions to recover the metabolite spectra and nuisance signals at two different resolutions. Specifically, the nuisance signals are recovered at the maximum resolution to minimize spectral leakage, while the point spread functions of metabolites are controlled to obtain acceptable signal-to-noise ratio. Results The comparisons of the algorithm against Tikhonov regularized reconstructions demonstrates considerably reduced line-shape distortions and improved metabolite maps. Conclusion The proposed sparsity constrained spectral deconvolution scheme is effective in minimizing the line-shape distortions. The dual resolution reconstruction scheme is capable of minimizing spectral leakage artifacts. Magn Reson Med 71:469-476, 2014. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR] |
| Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 93647393 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Sparse spectral deconvolution algorithm for noncartesian MR spectroscopic imaging. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bhave%2C+Sampada%22">Bhave, Sampada</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Eslami%2C+Ramin%22">Eslami, Ramin</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Jacob%2C+Mathews%22">Jacob, Mathews</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. Feb2014, Vol. 71 Issue 2, p469-476. 8p. – Name: Abstract Label: Abstract Group: Ab Data: Purpose To minimize line shape distortions and spectral leakage artifacts in MR spectroscopic imaging (MRSI). Methods A spatially and spectrally regularized non-Cartesian MRSI algorithm that uses the line shape distortion priors, estimated from water reference data, to deconvolve the spectra is introduced. Sparse spectral regularization is used to minimize noise amplification associated with deconvolution. A spiral MRSI sequence that heavily oversamples the central k-space regions is used to acquire the MRSI data. The spatial regularization term uses the spatial supports of brain and extracranial fat regions to recover the metabolite spectra and nuisance signals at two different resolutions. Specifically, the nuisance signals are recovered at the maximum resolution to minimize spectral leakage, while the point spread functions of metabolites are controlled to obtain acceptable signal-to-noise ratio. Results The comparisons of the algorithm against Tikhonov regularized reconstructions demonstrates considerably reduced line-shape distortions and improved metabolite maps. Conclusion The proposed sparsity constrained spectral deconvolution scheme is effective in minimizing the line-shape distortions. The dual resolution reconstruction scheme is capable of minimizing spectral leakage artifacts. Magn Reson Med 71:469-476, 2014. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mrm.24693 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 469 Titles: – TitleFull: Sparse spectral deconvolution algorithm for noncartesian MR spectroscopic imaging. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bhave, Sampada – PersonEntity: Name: NameFull: Eslami, Ramin – PersonEntity: Name: NameFull: Jacob, Mathews IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 07403194 Numbering: – Type: volume Value: 71 – Type: issue Value: 2 Titles: – TitleFull: Magnetic Resonance in Medicine Type: main |
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