Full Paper

Optimal design of pulsed arterial spin labeling MRI experiments

  1. Jingyi Xie1,
  2. Daniel Gallichan1,
  3. Roger N. Gunn2,3,
  4. Peter Jezzard1,*

Article first published online: 26 FEB 2008

DOI: 10.1002/mrm.21549

Issue

Magnetic Resonance in Medicine

Magnetic Resonance in Medicine

Volume 59, Issue 4, pages 826–834, April 2008

Additional Information

How to Cite

Xie, J., Gallichan, D., Gunn, R. N. and Jezzard, P. (2008), Optimal design of pulsed arterial spin labeling MRI experiments. Magnetic Resonance in Medicine, 59: 826–834. doi: 10.1002/mrm.21549

Author Information

  1. 1

    Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Department of Clinical Neurology, University of Oxford, Oxford, UK

  2. 2

    GlaxoSmithKline Clinical Imaging Centre, Hammersmith Hospital, London, UK

  3. 3

    Department of Engineering Science, University of Oxford, UK

*FMRIB Centre, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, United Kingdom

Publication History

  1. Issue published online: 26 MAR 2008
  2. Article first published online: 26 FEB 2008
  3. Manuscript Accepted: 12 DEC 2007
  4. Manuscript Revised: 4 DEC 2007
  5. Manuscript Received: 16 JUL 2007

Funded by

  • GlaxoSmithKline
  • Medical Research Council

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Keywords:

  • arterial spin labeling;
  • optimal design;
  • optimal sampling schedule;
  • perfusion;
  • PASL;
  • CBF

Abstract

Quantitative measurement of cerebral blood flow (CBF) using arterial spin labeling (ASL) MRI requires the acquisition of multiple inversion times (TIs) and the application of an appropriate kinetic model. The choice of these sampling times will have an impact on the precision of the estimated parameters. Here, optimal sampling schedule (OSS) design techniques, based on the Fisher Information approach, are applied in order to derive an optimal sampling scheme for pulsed arterial spin labeling (PASL) experiments. Such an approach should improve the precision of parameter estimation from experimental data, and provide a formal framework for optimally selecting a limited number of samples. In this study, we aimed to optimize the estimation precision of CBF and bolus arrival time from the PASL data. The performance of OSS was compared to a more standard evenly distributed sampling schedule (EDS) using both simulated and measured experimental data sets. It was found that OSS was able to significantly improve the precision of parameter estimation in PASL studies that sought to estimate either both CBF and bolus arrival time, or CBF alone. Magn Reson Med, 2008. © 2008 Wiley-Liss, Inc.

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