Literature DB >> 11948741

Quantification of myocardial blood flow and blood flow reserve in the presence of arterial dispersion: a simulation study.

Melanie Schmitt1, Magalie Viallon, Manfred Thelen, Wolfgang G Schreiber.   

Abstract

Myocardial blood flow (MBF) can be quantified using dynamic T1-weighted MRI of diffusible tracers and a mathematical model of underlying vasculature. Quantification of MBF by means of T1- weighted MRI requires knowledge of the arterial input function (AIF). The AIF can be estimated from the left ventricular (LV) cavity. However, dispersion may occur between the LV and the tissue of interest because of the laminar blood flow profiles, branching of venules, and because of stenosis. To evaluate the influence of dispersion on the results of MBF quantification, a simulation study was performed. The dispersion was described as a convolution of the AIF with an exponential residue function. Synthetic tissue and AIF curves were analyzed and the derived parameters fit to the simulated parameters. The results show that an unaccounted dispersion may result in a systematic underestimation of MBF up to approximately 50%. Underestimation increases with increasing dispersion and with increasing MBF. Assuming equal dispersion at rest and during hyperemia, myocardial perfusion reserve (MPR) estimates are also susceptible to underestimation of approximately 20%. An unaccounted dispersion therefore can lead to systematic underestimation of both blood flow and perfusion reserve. Copyright 2002 Wiley-Liss, Inc.

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Year:  2002        PMID: 11948741     DOI: 10.1002/mrm.10115

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  6 in total

Review 1.  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

2.  Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.

Authors:  Chong Duan; Jesper F Kallehauge; Carlos J Pérez-Torres; G Larry Bretthorst; Scott C Beeman; Kari Tanderup; Joseph J H Ackerman; Joel R Garbow
Journal:  Mol Imaging Biol       Date:  2018-02       Impact factor: 3.488

3.  Model-based blind estimation of kinetic parameters in dynamic contrast enhanced (DCE)-MRI.

Authors:  Jacob U Fluckiger; Matthias C Schabel; Edward V R Dibella
Journal:  Magn Reson Med       Date:  2009-12       Impact factor: 4.668

4.  Predicting grade of cerebral glioma using vascular-space occupancy MR imaging.

Authors:  H Lu; E Pollack; R Young; J S Babb; G Johnson; D Zagzag; R Carson; J H Jensen; J A Helpern; M Law
Journal:  AJNR Am J Neuroradiol       Date:  2007-11-01       Impact factor: 3.825

5.  Perfusion phantom: An efficient and reproducible method to simulate myocardial first-pass perfusion measurements with cardiovascular magnetic resonance.

Authors:  Amedeo Chiribiri; Andreas Schuster; Masaki Ishida; Gilion Hautvast; Niloufar Zarinabad; Geraint Morton; James Otton; Sven Plein; Marcel Breeuwer; Philip Batchelor; Tobias Schaeffter; Eike Nagel
Journal:  Magn Reson Med       Date:  2012-04-24       Impact factor: 4.668

6.  Computational fluid dynamics simulations of contrast agent bolus dispersion in a coronary bifurcation: impact on MRI-based quantification of myocardial perfusion.

Authors:  Regine Schmidt; Dirk Graafen; Stefan Weber; Laura M Schreiber
Journal:  Comput Math Methods Med       Date:  2013-02-28       Impact factor: 2.238

  6 in total

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