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Journal Article Review Ppublish 21 citations

PubMed: 38594906 DOI: 10.1002/mrm.30091

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications.

Magn Reson Med 92(2):469-495

Authors: Joseph G Woods, Eric Achten, Iris Asllani, Divya S Bolar, Weiying Dai, John A Detre, Audrey P Fan, María A Fernández-Seara, Xavier Golay, Matthias Günther, Jia Guo, Luis Hernandez-Garcia, Mai-Lan Ho, Meher R Juttukonda, Hanzhang Lu, Bradley J MacIntosh, Ananth J Madhuranthakam, Henk-Jan Mutsaerts, Thomas W Okell, Laura M Parkes, Nandor Pinter, Joana Pinto, Qin Qin, Marion Smits, Yuriko Suzuki, David L Thomas, Matthias J P Van Osch, Danny J J Wang, Esther A H Warnert, Greg Zaharchuk, Fernando Zelaya, Moss Zhao, Michael A Chappell

Affiliations

  • Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
  • Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California San Diego, La Jolla, California, USA.
  • Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium.
  • Department of Neuroscience, University of Sussex, Brighton, UK.
  • Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, New York, USA.
  • Department of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA.
  • Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Department of Biomedical Engineering, University of California Davis, Davis, California, USA.
  • Department of Neurology, University of California Davis, Davis, California, USA.
  • Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.
  • IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain.
  • UCL Queen Square Institute of Neurology, University College London, London, UK.
  • Gold Standard Phantoms, Sheffield, UK.
  • Imaging Physics, Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
  • Department of Physics and Electrical Engineering, University of Bremen, Bremen, Germany.
  • Department of Bioengineering, University of California Riverside, Riverside, California, USA.
  • Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.
  • Department of Radiology, University of Missouri, Columbia, Missouri, USA.
  • Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.
  • Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
  • The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Hurvitz Brain Sciences Program, Centre for Brain Resilience & Recovery, Sunnybrook Research Institute, Toronto, Ontario, Canada.
  • Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
  • Computational Radiology & Artificial Intelligence unit, Oslo University Hospital, Oslo, Norway.
  • Department of Radiology and Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA.
  • Department of Radiology and Nuclear Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
  • Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands.
  • School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
  • Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Manchester, UK.
  • Dent Neurologic Institute, Buffalo, New York, USA.
  • University at Buffalo Neurosurgery, Buffalo, New York, USA.
  • Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
  • Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
  • Medical Delta, Delft, The Netherlands.
  • Erasmus MC Cancer Institute, Erasmus MC, Rotterdam, The Netherlands.
  • Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK.
  • C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
  • Laboratory of FMRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California, USA.
  • Department of Radiology, Stanford University, Stanford, California, USA.
  • Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.
  • Maternal & Child Health Research Institute, Stanford University, Stanford, California, USA.
  • Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.

Abstract

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.

MeSH

Multiple Chronic Conditions, Perfusion Magnetic Resonance Imaging

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