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Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling
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Comparing model-based and model-free analysis methods for QUASAR arterial spin labeling perfusion quantification.

Michael A Chappell1, Mark W Woolrich, Esben T Petersen

  • 1Institute of Biomedical Engineering, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom. michael.chappell@eng.ox.ac.uk

Magnetic Resonance in Medicine
|June 20, 2012
PubMed
Summary

QUASAR arterial spin labeling MRI offers unique signal separation. This study compares model-free and model-based analyses for cerebral perfusion quantification, finding dispersion and signal separation are key discrepancies.

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Physiology

Background:

  • Arterial spin labeling (ASL) MRI is used for cerebral perfusion quantification.
  • The QUASAR (QUantitative ASL) method uniquely separates macrovascular and tissue signals using combined labeling and flow suppression gradients.
  • Model-free and model-based analyses are common for ASL data, but their comparative performance with QUASAR is unclear.

Purpose of the Study:

  • To critically compare model-free and model-based analysis approaches for QUASAR ASL in healthy brains.
  • To extend an existing two-component model for QUASAR's mixed flow suppression scheme.
  • To incorporate bolus dispersion into the model-based analysis to investigate discrepancies.

Main Methods:

  • Extended a two-component (arterial and tissue) model to accommodate QUASAR's specific labeling scheme.
  • Incorporated bolus dispersion into the model-based analysis.
  • Compared model-free (numerical deconvolution) and model-based analyses for perfusion quantification, including absolute measurements, uncertainty, and spatial variation of cerebral blood flow.

Main Results:

  • Discrepancies between model-free and model-based analyses were primarily attributed to bolus dispersion effects.
  • The ability of each method to separate macrovascular and tissue signals significantly influenced the results.
  • Both methods provided estimates for cerebral blood flow, but differed in accuracy and uncertainty.

Conclusions:

  • Bolus dispersion and macrovascular/tissue signal separation are critical factors influencing QUASAR ASL analysis outcomes.
  • Model-based analysis, when extended to include dispersion, offers a more comprehensive approach for QUASAR ASL.
  • Further refinement of analysis methods is needed for accurate and reliable cerebral perfusion quantification using QUASAR ASL.