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Modeling and correction of bolus dispersion effects in dynamic susceptibility contrast MRI

To purchase or authenticate to the full-text of this article, please visit this link: http://onlinelibrary.wiley.com/doi/10.1002/mrm.25077/abstract Byline: Amit Mehndiratta, Fernando Calamante, Bradley J. MacIntosh, David E. Crane, Stephen J. Payne, Michael A. Chappell Keywords: deconvolution; contr... Full description

Journal Title: Magnetic Resonance in Medicine December 2014, Vol.72(6), pp.1762-1774
Main Author: Mehndiratta, Amit
Other Authors: Calamante, Fernando , Macintosh, Bradley J. , Crane, David E. , Payne, Stephen J. , Chappell, Michael A.
Format: Electronic Article Electronic Article
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ID: ISSN: 0740-3194 ; E-ISSN: 1522-2594 ; DOI: 10.1002/mrm.25077
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recordid: wj10.1002/mrm.25077
title: Modeling and correction of bolus dispersion effects in dynamic susceptibility contrast MRI
format: Article
creator:
  • Mehndiratta, Amit
  • Calamante, Fernando
  • Macintosh, Bradley J.
  • Crane, David E.
  • Payne, Stephen J.
  • Chappell, Michael A.
subjects:
  • Deconvolution
  • Control Point Interpolation Method
  • Residue Function
  • Arterial Input Function
  • Dispersion
  • Bayesian Analysis
ispartof: Magnetic Resonance in Medicine, December 2014, Vol.72(6), pp.1762-1774
description: To purchase or authenticate to the full-text of this article, please visit this link: http://onlinelibrary.wiley.com/doi/10.1002/mrm.25077/abstract Byline: Amit Mehndiratta, Fernando Calamante, Bradley J. MacIntosh, David E. Crane, Stephen J. Payne, Michael A. Chappell Keywords: deconvolution; control point interpolation method; residue function; arterial input function; dispersion; Bayesian Analysis Purpose Bolus dispersion in DSC-MRI can lead to errors in cerebral blood flow (CBF) estimation by up to 70% when using singular value decomposition analysis. However, it might be possible to correct for dispersion using two alternative methods: the vascular model (VM) and control point interpolation (CPI). Additionally, these approaches potentially provide a means to quantify the microvascular residue function. Methods VM and CPI were extended to correct for dispersion by means of a vascular transport function. Simulations were performed at multiple dispersion levels and an in vivo analysis was performed on a healthy subject and two patients with carotid atherosclerotic disease. Results Simulations showed that methods that could not address dispersion tended to underestimate CBF (ratio in CBF estimation, CBFratio=0.57-0.77) in the presence of dispersion; whereas modified CPI showed the best performance at low-to-medium dispersion; CBFratio=0.99 and 0.81, respectively. The in vivo data showed trends in CBF estimation and residue function that were consistent with the predictions from simulations. Conclusion In patients with atherosclerotic disease the estimated residue function showed considerable differences in the ipsilateral hemisphere. These differences could partly be attributed to dispersive effects arising from the stenosis when dispersion corrected CPI was used. It is thus beneficial to correct for dispersion in perfusion analysis using this method. Magn Reson Med 72:1762-1774, 2014. [c] 2014 Wiley Periodicals, Inc. Supporting information: Additional Supporting Information may be found in the online version of this article Additional Supporting Information may be found in the online version of this article. CAPTION(S): Supplementary Information Tables.
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identifier: ISSN: 0740-3194 ; E-ISSN: 1522-2594 ; DOI: 10.1002/mrm.25077
fulltext: fulltext
issn:
  • 0740-3194
  • 07403194
  • 1522-2594
  • 15222594
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titleModeling and correction of bolus dispersion effects in dynamic susceptibility contrast MRI
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ispartofMagnetic Resonance in Medicine, December 2014, Vol.72(6), pp.1762-1774
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descriptionTo purchase or authenticate to the full-text of this article, please visit this link: http://onlinelibrary.wiley.com/doi/10.1002/mrm.25077/abstract Byline: Amit Mehndiratta, Fernando Calamante, Bradley J. MacIntosh, David E. Crane, Stephen J. Payne, Michael A. Chappell Keywords: deconvolution; control point interpolation method; residue function; arterial input function; dispersion; Bayesian Analysis Purpose Bolus dispersion in DSC-MRI can lead to errors in cerebral blood flow (CBF) estimation by up to 70% when using singular value decomposition analysis. However, it might be possible to correct for dispersion using two alternative methods: the vascular model (VM) and control point interpolation (CPI). Additionally, these approaches potentially provide a means to quantify the microvascular residue function. Methods VM and CPI were extended to correct for dispersion by means of a vascular transport function. Simulations were performed at multiple dispersion levels and an in vivo analysis was performed on a healthy subject and two patients with carotid atherosclerotic disease. Results Simulations showed that methods that could not address dispersion tended to underestimate CBF (ratio in CBF estimation, CBFratio=0.57-0.77) in the presence of dispersion; whereas modified CPI showed the best performance at low-to-medium dispersion; CBFratio=0.99 and 0.81, respectively. The in vivo data showed trends in CBF estimation and residue function that were consistent with the predictions from simulations. Conclusion In patients with atherosclerotic disease the estimated residue function showed considerable differences in the ipsilateral hemisphere. These differences could partly be attributed to dispersive effects arising from the stenosis when dispersion corrected CPI was used. It is thus beneficial to correct for dispersion in perfusion analysis using this method. Magn Reson Med 72:1762-1774, 2014. [c] 2014 Wiley Periodicals, Inc. Supporting information: Additional Supporting Information may be found in the online version of this article Additional Supporting Information may be found in the online version of this article. CAPTION(S): Supplementary Information Tables.
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