Comparing model-based and model-free analysis methods for QUASAR arterial spin labeling perfusion quantification
Journal
Magnetic Resonance in Medicine
Journal Volume
69
Journal Issue
5
Pages
1466-1475
Date Issued
2013
Author(s)
Abstract
Amongst the various implementations of arterial spin labeling MRI methods for quantifying cerebral perfusion, the QUASAR method is unique. By using a combination of labeling with and without flow suppression gradients, the QUASAR method offers the separation of macrovascular and tissue signals. This permits local arterial input functions to be defined and "model-free" analysis, using numerical deconvolution, to be used. However, it remains unclear whether arterial spin labeling data are best treated using model-free or model-based analysis. This work provides a critical comparison of these two approaches for QUASAR arterial spin labeling in the healthy brain. An existing two-component (arterial and tissue) model was extended to the mixed flow suppression scheme of QUASAR to provide an optimal model-based analysis. The model-based analysis was extended to incorporate dispersion of the labeled bolus, generally regarded as the major source of discrepancy between the two analysis approaches. Model-free and model-based analyses were compared for perfusion quantification including absolute measurements, uncertainty estimation, and spatial variation in cerebral blood flow estimates. Major sources of discrepancies between model-free and model-based analysis were attributed to the effects of dispersion and the degree to which the two methods can separate macrovascular and tissue signal. ? 2012 Wiley Periodicals, Inc.
Subjects
Dispersions
Galaxies
Image processing
Polypropylenes
Tissue
Arterial spin labeling
flow and perfusion
Technical research
Technique development
theoretical
Uncertainty analysis
arteriole
article
brain blood flow
brain perfusion
dispersion
electromagnetic field
gray matter
image analysis
mathematical model
quantitative analysis
quantitative STAR labeling of arterial region
reproducibility
spin labeling
structure analysis
tissues
vascularization
Algorithms
Blood Flow Velocity
Cerebral Arteries
Cerebrovascular Circulation
Computer Simulation
Humans
Image Enhancement
Image Interpretation, Computer-Assisted
Magnetic Resonance Angiography
Models, Cardiovascular
Reproducibility of Results
Sensitivity and Specificity
Spin Labels
SDGs
Type
journal article