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Woods JG, Achten E, Asllani I, Bolar DS, Dai W, Detre JA, Fan AP, Fernández-Seara M, Golay X, Günther M, Guo J, Hernandez-Garcia L, Ho ML, Juttukonda MR, Lu H, MacIntosh BJ, Madhuranthakam AJ, Mutsaerts HJ, Okell TW, Parkes LM, Pinter N, Pinto J, Qin Q, Smits M, Suzuki Y, Thomas DL, Van Osch MJ, Wang DJJ, Warnert EA, Zaharchuk G, Zelaya F, Zhao M, Chappell MA. Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications. Magn Reson Med 2024; 92:469-495. [PMID: 38594906 PMCID: PMC11142882 DOI: 10.1002/mrm.30091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 02/09/2024] [Accepted: 03/07/2024] [Indexed: 04/11/2024]
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.
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Affiliation(s)
- Joseph G. Woods
- 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
| | - Eric Achten
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Iris Asllani
- Department of Neuroscience, University of Sussex, UK and Department of Biomedical Engineering, Rochester Institute of Technology, USA
| | - Divya S. Bolar
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Weiying Dai
- Department of Computer Science, State University of New York at Binghamton, Binghamton, NY, USA, 13902
| | - John A. Detre
- Department of Neurology, University of Pennsylvania, 3 Dulles Building, 3400 Spruce Street, Philadelphia, PA 19104 USA
| | - Audrey P. Fan
- Department of Biomedical Engineering, Department of Neurology, University of California Davis, Davis, CA, USA
| | - Maria Fernández-Seara
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain; IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | - Xavier Golay
- UCL Queen Square Institute of Neurology, University College London, London, UK; Gold Standard Phantoms, UK
| | - Matthias Günther
- Imaging Physics, Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- Departments of Physics and Electrical Engineering, University of Bremen, Bremen, Germany
| | - Jia Guo
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | | | - Mai-Lan Ho
- Department of Radiology, University of Missouri, Columbia, MO, USA. ORCID: 0000-0002-9455-1350
| | - Meher R. Juttukonda
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Bradley J. MacIntosh
- Hurvitz Brain Sciences Program, Centre for Brain Resilience & Recovery, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Computational Radiology & Artificial Intelligence unit, Oslo University Hospital, Oslo, Norway
| | - Ananth J. Madhuranthakam
- Department of Radiology and Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Henk-Jan Mutsaerts
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Thomas W. Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Laura M. Parkes
- School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, UK
| | - Nandor Pinter
- Dent Neurologic Institute, Buffalo, New York, USA; University at Buffalo Neurosurgery, Buffalo, New York, USA
| | - Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Medical Delta, Delft, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Rotterdam, NL
| | - Yuriko Suzuki
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - David L. Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Matthias J.P. Van Osch
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Danny JJ Wang
- Laboratory of FMRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Esther A.H. Warnert
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Rotterdam, NL
| | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Fernando Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Moss Zhao
- Department of Radiology, Stanford University, Stanford, CA, USA
- Maternal & Child Health Research Institute, Stanford University, Stanford, CA, USA
| | - Michael A. Chappell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
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Dolui S, Tisdall D, Vidorreta M, Jacobs DR, Nasrallah IM, Bryan RN, Wolk DA, Detre JA. Characterizing a perfusion-based periventricular small vessel region of interest. NEUROIMAGE-CLINICAL 2019; 23:101897. [PMID: 31233954 PMCID: PMC6595083 DOI: 10.1016/j.nicl.2019.101897] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 06/04/2019] [Accepted: 06/11/2019] [Indexed: 10/27/2022]
Abstract
The periventricular white matter (PVWM) is supplied by terminal distributions of small vessels and is particularly susceptible to developing white matter lesions (WML) associated with cerebral small vessel disease (CSVD). We obtained group-averaged cerebral blood flow (CBF) maps from Arterial Spin Labeled (ASL) perfusion MRI data obtained in 436 middle-aged (50.4 ± 3.5 years) subjects in the NHLBI CARDIA study and in 61 elderly (73.3 ± 6.9 years) cognitively normal subjects recruited from the Penn Alzheimer's Disease Center (ADC) and found that the lowest perfused brain voxels are located within the PVWM. We constructed a white matter periventricular small vessel (PSV) region of interest (ROI) by empirically thresholding the group averaged CARDIA CBF map at CBF < 15 ml/100 g/min. Thereafter we compared CBF in the PSV ROI and in the remaining white matter (RWM) with the location and volume of WML measured with Fluid Attenuated Inversion Recovery (FLAIR) MRI. WM CBF was lower within WML than outside WML voxels (p < <0.0001) in both the PSV and RWM ROIs, however this difference was much smaller (p < <0.0001) in the PSV ROI than in the RWM suggesting a more homogenous reduction of CBF in the PSV region. Normalized WML volumes were significantly higher in the PSV ROI than in the RWM and in the elderly cohort as compared to the middle-aged cohort (p < <0.0001). Additionally, the PSV ROI showed a significantly (p = .001) greater increase in lesion volume than the RWM in the elderly ADC cohort than the younger CARDIA cohort. Considerable intersubject variability in PSV CBF observed in both study cohorts likely represents biological variability that may be predictive of future WML and/or cognitive decline. In conclusion, a data-driven PSV ROI defined by voxels with low perfusion in middle age defines a region with homogeneously reduced CBF that is particularly susceptible to progressive ischemic injury in elderly controls. PSV CBF may provide a mechanistically specific biomarker of CSVD.
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Affiliation(s)
- Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Marta Vidorreta
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Siemens Healthcare S.L.U., Madrid, Spain
| | - David R Jacobs
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Ilya M Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - R Nick Bryan
- Department of Diagnostic Medicine, University of Texas, Austin, Austin, TX, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - John A Detre
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
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Yang FN, Xu S, Spaeth A, Galli O, Zhao K, Fang Z, Basner M, Dinges DF, Detre JA, Rao H. Test-retest reliability of cerebral blood flow for assessing brain function at rest and during a vigilance task. Neuroimage 2019; 193:157-166. [PMID: 30894335 DOI: 10.1016/j.neuroimage.2019.03.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 03/06/2019] [Accepted: 03/07/2019] [Indexed: 12/22/2022] Open
Abstract
Arterial spin labeled (ASL) perfusion magnetic resonance imaging (MRI) is increasingly used to assess regional brain activity and cerebrovascular function in both healthy and clinical populations. ASL perfusion imaging provides a quantitative measure of regional brain activity by determining absolute cerebral blood flow (CBF) values at a resting state or during task performance. However, the comparative reliability of these ASL measures is not well characterized. It is also unclear whether the test-retest reliability of absolute CBF or task-induced CBF change measures would be comparable to the reliability of task performance. In this study, fifteen healthy participants were scanned three times in a strictly controlled in-laboratory study while at rest and during performing a simple and reliable psychomotor vigilance test (PVT). The reliability of absolute CBF and task-induced CBF changes was evaluated using the intraclass correlation coefficient (ICC) and compared to that of task performance. Absolute CBF showed excellent test-retest reliability across the three scans for both resting and PVT scans. The reliability of regional absolute CBF was comparable to that of behavioral measures of PVT performance, and was slightly higher during PVT scans as compared with resting scans. Task-induced regional CBF changes demonstrated only poor to moderate reliability across three scans. These findings suggest that absolute CBF measures are more reliable than task-induced CBF changes for characterizing regional brain function, especially for longitudinal and clinical studies.
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Affiliation(s)
- Fan Nils Yang
- Department of Psychology, Sun Yat-sen University, Guangzhou, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sihua Xu
- Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Andrea Spaeth
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Olga Galli
- Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ke Zhao
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Zhuo Fang
- Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mathias Basner
- Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - David F Dinges
- Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - John A Detre
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hengyi Rao
- Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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