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Assessment of clinical data of nonlinear stochastic deconvolution versus block-circulant singular value decomposition for quantitative dynamic susceptibility contrast magnetic resonance imaging

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Title: Assessment of clinical data of nonlinear stochastic deconvolution versus block-circulant singular value decomposition for quantitative dynamic susceptibility contrast magnetic resonance imaging
Authors: Peruzzo, Denis1, Zanderigo, Francesca1,2, Bertoldo, Alessandra1, Pillonetto, Gianluigi1, Cosottini, Mirco3, Cobelli, Claudio1 cobelli@dei.unipd.it
Source: Magnetic Resonance Imaging (0730725X). Sep2011, Vol. 29 Issue 7, p927-936. 10p.
Subjects: Magnetic resonance imaging, Hemodynamics, Blood flow, Singular value decomposition, Carotid artery stenosis, Tissues, Therapeutics
Abstract: Abstract: Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) allows the noninvasive assessment of brain hemodynamics alterations by quantifying, via deconvolution, the cerebral blood flow (CBF) and mean transit time (MTT). Singular value decomposition (SVD) and block-circulant SVD (cSVD) are the most widely adopted deconvolution method, although they bear some limitations, including unphysiological oscillations in the residue function and bias in the presence of delay and dispersion between the tissue and the arterial input function. A nonlinear stochastic regularization (NSR) has been proposed, which performs better than SVD and cSVD on simulated data both in the presence and absence of dispersion. Moreover, NSR allows to quantify the dispersion level. Here, cSVD and NSR are compared for the first time on a group of nine patients with severe atherosclerotic unilateral stenosis of internal carotid artery before and after carotid stenting to investigate the effect of arterial dispersion. According to region of interest-based analysis, NSR characterizes the pathologic tissue more accurately than cSVD, thus improving the quality of the information provided to physicians for diagnosis. In fact, in 7 (78%) of the 9 subjects, CBF and MTT maps provided by NSR allow to correctly identify the pathologic hemisphere to the physician. Moreover, by emphasizing the difference between pathologic and healthy tissues, NSR may be successfully used to monitor the subject''s recovery after the treatment and/or surgery. NSR also generates dispersion level and non-dispersed CBF and MTT maps. The dispersion level provides information on CBF and MTT estimates reliability and may also be used as a clinical indicator of pathological tissue state complementary to CBF and MTT, thus increasing the clinical information provided by DSC-MRI analysis. [Copyright &y& Elsevier]
Copyright of Magnetic Resonance Imaging (0730725X) is the property of Elsevier B.V. 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.)
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  Data: Assessment of clinical data of nonlinear stochastic deconvolution versus block-circulant singular value decomposition for quantitative dynamic susceptibility contrast magnetic resonance imaging
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  Data: <searchLink fieldCode="AR" term="%22Peruzzo%2C+Denis%22">Peruzzo, Denis</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zanderigo%2C+Francesca%22">Zanderigo, Francesca</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Bertoldo%2C+Alessandra%22">Bertoldo, Alessandra</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Pillonetto%2C+Gianluigi%22">Pillonetto, Gianluigi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Cosottini%2C+Mirco%22">Cosottini, Mirco</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Cobelli%2C+Claudio%22">Cobelli, Claudio</searchLink><relatesTo>1</relatesTo><i> cobelli@dei.unipd.it</i>
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  Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+Imaging+%280730725X%29%22">Magnetic Resonance Imaging (0730725X)</searchLink>. Sep2011, Vol. 29 Issue 7, p927-936. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Hemodynamics%22">Hemodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Blood+flow%22">Blood flow</searchLink><br /><searchLink fieldCode="DE" term="%22Singular+value+decomposition%22">Singular value decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22Carotid+artery+stenosis%22">Carotid artery stenosis</searchLink><br /><searchLink fieldCode="DE" term="%22Tissues%22">Tissues</searchLink><br /><searchLink fieldCode="DE" term="%22Therapeutics%22">Therapeutics</searchLink>
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  Data: Abstract: Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) allows the noninvasive assessment of brain hemodynamics alterations by quantifying, via deconvolution, the cerebral blood flow (CBF) and mean transit time (MTT). Singular value decomposition (SVD) and block-circulant SVD (cSVD) are the most widely adopted deconvolution method, although they bear some limitations, including unphysiological oscillations in the residue function and bias in the presence of delay and dispersion between the tissue and the arterial input function. A nonlinear stochastic regularization (NSR) has been proposed, which performs better than SVD and cSVD on simulated data both in the presence and absence of dispersion. Moreover, NSR allows to quantify the dispersion level. Here, cSVD and NSR are compared for the first time on a group of nine patients with severe atherosclerotic unilateral stenosis of internal carotid artery before and after carotid stenting to investigate the effect of arterial dispersion. According to region of interest-based analysis, NSR characterizes the pathologic tissue more accurately than cSVD, thus improving the quality of the information provided to physicians for diagnosis. In fact, in 7 (78%) of the 9 subjects, CBF and MTT maps provided by NSR allow to correctly identify the pathologic hemisphere to the physician. Moreover, by emphasizing the difference between pathologic and healthy tissues, NSR may be successfully used to monitor the subject''s recovery after the treatment and/or surgery. NSR also generates dispersion level and non-dispersed CBF and MTT maps. The dispersion level provides information on CBF and MTT estimates reliability and may also be used as a clinical indicator of pathological tissue state complementary to CBF and MTT, thus increasing the clinical information provided by DSC-MRI analysis. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Magnetic Resonance Imaging (0730725X) is the property of Elsevier B.V. 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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        Value: 10.1016/j.mri.2011.02.006
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        Type: general
      – SubjectFull: Hemodynamics
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      – SubjectFull: Blood flow
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      – TitleFull: Assessment of clinical data of nonlinear stochastic deconvolution versus block-circulant singular value decomposition for quantitative dynamic susceptibility contrast magnetic resonance imaging
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              Text: Sep2011
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