Literature DB >> 10441012

Measurement of cerebral perfusion with dual-echo multi-slice quantitative dynamic susceptibility contrast MRI.

E J Vonken1, M J van Osch, C J Bakker, M A Viergever.   

Abstract

Quantitative cerebral perfusion was measured in vivo using dynamic susceptibility contrast magnetic resonance imaging. A dual-echo acquisition was used to eliminate T(1)-enhancement. The arterial input curve was measured in a separate slice in the neck to minimize partial volume effects. Data analysis was performed using a maximum likelihood expectation maximization method to be less sensitive to noise or contrast arrival time differences. From the contrast response curves obtained, the cerebral blood volume (CBV) and flow (CBF) and the timing parameters mean transit time (MTT), time of appearance (TA), and time-to-bolus peak (TBP) were obtained. Adjacent slices were measured to permit discrimination between intra- and inter-subject variance. The group investigated consisted of 41 subjects without cerebral pathology on anatomical MRI. Perfusion parameters for gray (GM) and white matter (WM) were obtained: CBV (GM) = 6.78 +/- 0.99 ml/100 ml, CBV (WM) = 3.78 +/- 0. 96 ml/100 ml, CBF (GM) = 68.7 +/- 21.2 ml/100 ml/min, CBF (WM) = 35. 8 +/- 12.7 ml/100 ml/min, and average GM/WM ratio for CBV (GM/WM) = 1.87 +/- 0.42 and CBF (GM/WM) = 1.99 +/- 0.48. Measured temporal aspects of perfusion were: mean transit time (MTT) (GM) = 6.4 +/- 1. 8 seconds, MTT (WM) = 6.9 +/- 2.3 seconds, time of appearance (TA) (GM) = 1.4 +/- 0.9 seconds, TA (WM) = 2.0 +/- 1.0 seconds, and time-to-bolus peak (TBP) (GM) = 2.4 +/- 1.4 seconds, TBP (WM) = 3.0 +/- 1.5 seconds. The average values were in agreement with those from the literature. Inter- and intra-person variances were estimated using an ANOVA test, and the sources of variance in the parameters, such as image noise, biological variability, and measurement errors of the arterial input curve were found to be of the same order of magnitude. J. Magn. Reson. Imaging 1999;10:109-117. Copyright 1999 Wiley-Liss, Inc.

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Year:  1999        PMID: 10441012     DOI: 10.1002/(sici)1522-2586(199908)10:2<109::aid-jmri1>3.0.co;2-#

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  43 in total

1.  Absolute cerebral blood flow measured by dynamic susceptibility contrast MRI: a direct comparison with Xe-133 SPECT.

Authors:  R Wirestam; E Ryding; A Lindgren; B Geijer; S Holtås; F Ståhlberg
Journal:  MAGMA       Date:  2000-12       Impact factor: 2.310

2.  Blood volume of gliomas determined by double-echo dynamic perfusion-weighted MR imaging: a preliminary study.

Authors:  H Uematsu; M Maeda; N Sadato; T Matsuda; Y Ishimori; Y Koshimoto; H Kimura; H Yamada; Y Kawamura; Y Yonekura; H Itoh
Journal:  AJNR Am J Neuroradiol       Date:  2001 Nov-Dec       Impact factor: 3.825

3.  Quantitative cerebral blood flow measurement with dynamic perfusion CT using the vascular-pixel elimination method: comparison with H2(15)O positron emission tomography.

Authors:  Kohsuke Kudo; Satoshi Terae; Chietsugu Katoh; Masaki Oka; Tohru Shiga; Nagara Tamaki; Kazuo Miyasaka
Journal:  AJNR Am J Neuroradiol       Date:  2003-03       Impact factor: 3.825

4.  Wavelet-based noise reduction for improved deconvolution of time-series data in dynamic susceptibility-contrast MRI.

Authors:  R Wirestam; F Ståhlberg
Journal:  MAGMA       Date:  2005-05-10       Impact factor: 2.310

5.  Identifying systematic errors in quantitative dynamic-susceptibility contrast perfusion imaging by high-resolution multi-echo parallel EPI.

Authors:  Thies H Jochimsen; Rexford D Newbould; Stefan T Skare; David B Clayton; Gregory W Albers; Michael E Moseley; Roland Bammer
Journal:  NMR Biomed       Date:  2007-06       Impact factor: 4.044

6.  ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain.

Authors:  K Welker; J Boxerman; A Kalnin; T Kaufmann; M Shiroishi; M Wintermark
Journal:  AJNR Am J Neuroradiol       Date:  2015-04-23       Impact factor: 3.825

7.  Influence of partial volume on venous output and arterial input function.

Authors:  I van der Schaaf; E-J Vonken; A Waaijer; B Velthuis; M Quist; T van Osch
Journal:  AJNR Am J Neuroradiol       Date:  2006-01       Impact factor: 3.825

8.  A fully automated method for quantitative cerebral hemodynamic analysis using DSC-MRI.

Authors:  Atle Bjørnerud; Kyrre E Emblem
Journal:  J Cereb Blood Flow Metab       Date:  2010-01-20       Impact factor: 6.200

Review 9.  Absolute quantification of perfusion using dynamic susceptibility contrast MRI: pitfalls and possibilities.

Authors:  Linda Knutsson; Freddy Ståhlberg; Ronnie Wirestam
Journal:  MAGMA       Date:  2009-12-04       Impact factor: 2.310

10.  Utilization of MR angiography in perfusion imaging for identifying arterial input function.

Authors:  Bora Buyuksarac; Mehmed Ozkan
Journal:  MAGMA       Date:  2017-07-25       Impact factor: 2.310

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