Stable spline deconvolution for dynamic susceptibility contrast MRI.
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| Title: | Stable spline deconvolution for dynamic susceptibility contrast MRI. |
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| Authors: | Peruzzo, Denis1, Castellaro, Marco2, Pillonetto, Gianluigi2, Bertoldo, Alessandra2 alessandra.bertoldo@dei.unipd.it |
| Source: | Magnetic Resonance in Medicine. Nov2017, Vol. 78 Issue 5, p1801-1811. 11p. |
| Abstract: | Purpose To present the stable spline (SS) deconvolution method for the quantification of the cerebral blood flow (CBF) from dynamic susceptibility contrast MRI. Methods The SS method was compared with both the block-circulant singular value decomposition (oSVD) and nonlinear stochastic regularization (NSR) methods. oSVD is one of the most popular deconvolution methods in dynamic susceptibility contrast MRI (DSC-MRI). NSR is an alternative approach that we proposed previously. The three methods were compared using simulated data and two clinical data sets. Results The SS method correctly reconstructed the dispersed residue function and its peak in presence of dispersion, regardless of the delay. In absence of dispersion, SS performs similarly to oSVD and does not correctly reconstruct the residue function and its peak. SS and NSR better differentiate healthy and pathologic CBF values compared with oSVD in all simulated conditions. Using acquired data, SS and NSR provide more clinically plausible and physiological estimates of the residue function and CBF maps compared with oSVD. Conclusion The SS method overcomes some of the limitations of oSVD, such as unphysiological estimates of the residue function and NSR, the latter of which is too computationally expensive to be applied to large data sets. Thus, the SS method is a valuable alternative for CBF quantification using DSC-MRI data. Magn Reson Med 78:1801-1811, 2017. © 2017 International Society for Magnetic Resonance in Medicine. [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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 125690379 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Stable spline deconvolution for dynamic susceptibility contrast MRI. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Peruzzo%2C+Denis%22">Peruzzo, Denis</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Castellaro%2C+Marco%22">Castellaro, Marco</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Pillonetto%2C+Gianluigi%22">Pillonetto, Gianluigi</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Bertoldo%2C+Alessandra%22">Bertoldo, Alessandra</searchLink><relatesTo>2</relatesTo><i> alessandra.bertoldo@dei.unipd.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. Nov2017, Vol. 78 Issue 5, p1801-1811. 11p. – Name: Abstract Label: Abstract Group: Ab Data: Purpose To present the stable spline (SS) deconvolution method for the quantification of the cerebral blood flow (CBF) from dynamic susceptibility contrast MRI. Methods The SS method was compared with both the block-circulant singular value decomposition (oSVD) and nonlinear stochastic regularization (NSR) methods. oSVD is one of the most popular deconvolution methods in dynamic susceptibility contrast MRI (DSC-MRI). NSR is an alternative approach that we proposed previously. The three methods were compared using simulated data and two clinical data sets. Results The SS method correctly reconstructed the dispersed residue function and its peak in presence of dispersion, regardless of the delay. In absence of dispersion, SS performs similarly to oSVD and does not correctly reconstruct the residue function and its peak. SS and NSR better differentiate healthy and pathologic CBF values compared with oSVD in all simulated conditions. Using acquired data, SS and NSR provide more clinically plausible and physiological estimates of the residue function and CBF maps compared with oSVD. Conclusion The SS method overcomes some of the limitations of oSVD, such as unphysiological estimates of the residue function and NSR, the latter of which is too computationally expensive to be applied to large data sets. Thus, the SS method is a valuable alternative for CBF quantification using DSC-MRI data. Magn Reson Med 78:1801-1811, 2017. © 2017 International Society for Magnetic Resonance in Medicine. [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.26582 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1801 Titles: – TitleFull: Stable spline deconvolution for dynamic susceptibility contrast MRI. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Peruzzo, Denis – PersonEntity: Name: NameFull: Castellaro, Marco – PersonEntity: Name: NameFull: Pillonetto, Gianluigi – PersonEntity: Name: NameFull: Bertoldo, Alessandra IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 07403194 Numbering: – Type: volume Value: 78 – Type: issue Value: 5 Titles: – TitleFull: Magnetic Resonance in Medicine Type: main |
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