Blog
About

  • Record: found
  • Abstract: found
  • Article: found
Is Open Access

Cerebral blood flow quantification using vessel-encoded arterial spin labeling

Read this article at

Bookmark
      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

      Abstract

      Arterial spin labeling (ASL) techniques are gaining popularity for visualizing and quantifying cerebral blood flow (CBF) in a range of patient groups. However, most ASL methods lack vessel-selective information, which is important for the assessment of collateral flow and the arterial supply to lesions. In this study, we explored the use of vessel-encoded pseudocontinuous ASL (VEPCASL) with multiple postlabeling delays to obtain individual quantitative CBF and bolus arrival time maps for each of the four main brain-feeding arteries and compared the results against those obtained with conventional pseudocontinuous ASL (PCASL) using matched scan time. Simulations showed that PCASL systematically underestimated CBF by up to 37% in voxels supplied by two arteries, whereas VEPCASL maintained CBF accuracy since each vascular component is treated separately. Experimental results in healthy volunteers showed that there is no systematic bias in the CBF estimates produced by VEPCASL and that the signal-to-noise ratio of the two techniques is comparable. Although more complex acquisition and image processing is required and the potential for motion sensitivity is increased, VEPCASL provides comparable data to PCASL but with the added benefit of vessel-selective information. This could lead to more accurate CBF estimates in patients with a significant collateral flow.

      Related collections

      Most cited references 36

      • Record: found
      • Abstract: found
      • Article: not found

      Fast robust automated brain extraction.

       Teri S Krebs (2002)
      An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Copyright 2002 Wiley-Liss, Inc.
        Bookmark
        • Record: found
        • Abstract: found
        • Article: not found

        Improved optimization for the robust and accurate linear registration and motion correction of brain images.

        Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.
          Bookmark
          • Record: found
          • Abstract: found
          • Article: not found

          FSL.

          FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
            Bookmark

            Author and article information

            Affiliations
            [1 ]Nuffield Department of Clinical Neurosciences, Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford , Oxford, UK
            [2 ]Department of Engineering, Institute of Biomedical Engineering, University of Oxford , Oxford, UK
            Author notes
            [* ]FMRIB Centre, John Radcliffe Hospital , Headley Way, Headington, Oxford OX3 9DU, UK. E-mail: tokell@ 123456fmrib.ox.ac.uk
            Journal
            J Cereb Blood Flow Metab
            J. Cereb. Blood Flow Metab
            Journal of Cerebral Blood Flow & Metabolism
            Nature Publishing Group
            0271-678X
            1559-7016
            November 2013
            07 August 2013
            1 November 2013
            : 33
            : 11
            : 1716-1724
            23921895
            3824178
            jcbfm2013129
            10.1038/jcbfm.2013.129
            Copyright © 2013 International Society for Cerebral Blood Flow & Metabolism, Inc.

            This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/

            Categories
            Original Article

            Neurosciences

            mri, cerebral hemodynamics, cerebral blood flow measurement, brain imaging, asl

            Comments

            Comment on this article