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

  • 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
*Corresponding author for this work
  • University of Oxford
  • University of California at San Diego
  • Ghent University
  • University of Sussex
  • Kate Gleason College of Engineering
  • State University of New York Binghamton University
  • University of Pennsylvania
  • University of California at Davis
  • University of Navarra
  • Navarra's Health Research Institute (IdiSNA)
  • University College London
  • Gold Standard Phantoms
  • Fraunhofer Institute for Digital Medicine
  • University of Bremen
  • Marlan and Rosemary Bourns College of Engineering
  • University of Michigan Medical School
  • University of Missouri
  • Brigham and Women’s Hospital
  • Harvard Medical School
  • Johns Hopkins University School of Medicine
  • Sunnybrook Research Institute
  • University of Toronto
  • Oslo University Hospital
  • University of Texas Southwestern Medical Center
  • Amsterdam UMC
  • University of Manchester
  • Health Innovation Manchester
  • Dent Neurologic Institute, Amherst, NY, USA
  • SUNY Buffalo
  • Erasmus University Rotterdam
  • Medical Delta
  • Leiden University
  • University of Southern California
  • Stanford University
  • King's College London
  • University of Nottingham

Research output: Contribution to journalArticleAcademicpeer-review

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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.
Original languageEnglish
Pages (from-to)469-495
Number of pages27
JournalMagnetic resonance in medicine
Volume92
Issue number2
Early online date2024
DOIs
Publication statusPublished - Aug 2024

Keywords

  • arterial spin labeling
  • arterial transit time
  • cerebral blood flow
  • multi-timepoint
  • perfusion

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