Literature DB >> 22711674

Comparing model-based and model-free analysis methods for QUASAR arterial spin labeling perfusion quantification.

Michael A Chappell1, Mark W Woolrich, Esben T Petersen, Xavier Golay, Stephen J Payne.   

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.
Copyright © 2012 Wiley Periodicals, Inc.

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Year:  2012        PMID: 22711674     DOI: 10.1002/mrm.24372

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


  7 in total

Review 1.  Arterial spin labeling for the measurement of cerebral perfusion and angiography.

Authors:  Peter Jezzard; Michael A Chappell; Thomas W Okell
Journal:  J Cereb Blood Flow Metab       Date:  2017-11-23       Impact factor: 6.200

2.  Effects of Acquisition Parameter Modifications and Field Strength on the Reproducibility of Brain Perfusion Measurements Using Arterial Spin-Labeling.

Authors:  K P A Baas; J Petr; J P A Kuijer; A J Nederveen; H J M M Mutsaerts; K C C van de Ven
Journal:  AJNR Am J Neuroradiol       Date:  2020-11-12       Impact factor: 3.825

3.  Cerebrovascular reactivity measurements using simultaneous 15O-water PET and ASL MRI: Impacts of arterial transit time, labeling efficiency, and hematocrit.

Authors:  Moss Y Zhao; Audrey P Fan; David Yen-Ting Chen; Magdalena J Sokolska; Jia Guo; Yosuke Ishii; David D Shin; Mohammad Mehdi Khalighi; Dawn Holley; Kim Halbert; Andrea Otte; Brittney Williams; Taghi Rostami; Jun-Hyung Park; Bin Shen; Greg Zaharchuk
Journal:  Neuroimage       Date:  2021-03-11       Impact factor: 6.556

4.  Resting brain perfusion and selected vascular risk factors in healthy elderly subjects.

Authors:  Otto M Henriksen; Lars T Jensen; Katja Krabbe; Per Guldberg; Tom Teerlink; Egill Rostrup
Journal:  PLoS One       Date:  2014-05-19       Impact factor: 3.240

5.  Sub-Clinical Cognitive Decline and Resting Cerebral Blood Flow in Middle Aged Men.

Authors:  Otto Mølby Henriksen; Naja Liv Hansen; Merete Osler; Erik Lykke Mortensen; Dorte Merete Hallam; Esben Thade Pedersen; Michael Chappell; Martin Johannes Lauritzen; Egill Rostrup
Journal:  PLoS One       Date:  2017-01-17       Impact factor: 3.240

6.  Quantification of cerebral perfusion and cerebrovascular reserve using Turbo-QUASAR arterial spin labeling MRI.

Authors:  Moss Y Zhao; Lena Václavů; Esben T Petersen; Bart J Biemond; Magdalena J Sokolska; Yuriko Suzuki; David L Thomas; Aart J Nederveen; Michael A Chappell
Journal:  Magn Reson Med       Date:  2019-09-12       Impact factor: 4.668

7.  ASL-BIDS, the brain imaging data structure extension for arterial spin labeling.

Authors:  Patricia Clement; Marco Castellaro; Thomas W Okell; David L Thomas; Pieter Vandemaele; Sara Elgayar; Aaron Oliver-Taylor; Thomas Kirk; Joseph G Woods; Sjoerd B Vos; Joost P A Kuijer; Eric Achten; Matthias J P van Osch; John A Detre; Hanzhang Lu; David C Alsop; Michael A Chappell; Luis Hernandez-Garcia; Jan Petr; Henk J M M Mutsaerts
Journal:  Sci Data       Date:  2022-09-06       Impact factor: 8.501

  7 in total

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