Optimization strategies for evaluation of brain hemodynamic parameters with qBOLD technique

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18 Scopus citations

Abstract

Quantitative blood oxygenation level dependent technique provides an MRI-based method to measure tissue hemodynamic parameters such as oxygen extraction fraction and deoxyhemoglobin-containing (veins and prevenous part of capillaries) cerebral blood volume fraction. It is based on a theory of MR signal dephasing in the presence of blood vessel network and experimental method - gradient echo sampling of spin echo previously proposed and validated on phantoms and animals. In vivo human studies also demonstrated feasibility of this approach but also recognized that obtaining reliable results requires high signal-to-noise ratio in the data. In this paper, we analyze in detail the uncertainties of the quantitative blood oxygenation level dependent parameter estimates in the framework of the Bayesian probability theory, namely, we examine how the estimated parameters oxygen extraction fraction and deoxygenated cerebral blood volume fraction depend on their "true values," signal-to-noise ratio, and data sampling strategies. On the basis of this analysis, we develop strategies for optimization of the quantitative blood oxygenation level dependent technique for deoxygenated cerebral blood volume and oxygen extraction fraction evaluation. In particular, it is demonstrated that the use of gradient echo sampling of spin echo sequence allows substantial decrease of measurement errors as the data are acquired on both sides of spin echo. We test our theory on phantom mimicking the structure of blood vessel network. A 3D gradient echo sampling of spin echo pulse sequence is used for the acquisition of the MRI signal that was subsequently analyzed by Bayesian Application Software. The experimental results demonstrated a good agreement with theoretical predictions.

Original languageEnglish
Pages (from-to)1034-1043
Number of pages10
JournalMagnetic resonance in medicine
Volume69
Issue number4
DOIs
StatePublished - Apr 2013

Keywords

  • BOLD contrast
  • Bayesian analysis
  • CBV
  • GESSE
  • MRI
  • OEF
  • qBOLD

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