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Qin Q, Alsop DC, Bolar DS, Hernandez-Garcia L, Meakin J, Liu D, Nayak KS, Schmid S, van Osch MJP, Wong EC, Woods JG, Zaharchuk G, Zhao MY, Zun Z, Guo J. Erratum to: Velocity-selective arterial spin labeling perfusion MRI: A review of the state of the art and recommendations for clinical implementation (Magn Reson Med. 2022; 88:1528-1547). Magn Reson Med 2024. [PMID: 38659147 DOI: 10.1002/mrm.30099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 03/15/2024] [Indexed: 04/26/2024]
Affiliation(s)
- Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - David C Alsop
- Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Divya S Bolar
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California, San Diego La Jolla, California, USA
| | | | - James Meakin
- Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dapeng Liu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Krishna S Nayak
- Magnetic Resonance Engineering Laboratory, Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, California, USA
| | - Sophie Schmid
- C.J. Gorter Center for high field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Matthias J P van Osch
- C.J. Gorter Center for high field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Eric C Wong
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California, San Diego La Jolla, California, USA
| | - Joseph G Woods
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California, San Diego La Jolla, California, USA
| | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Moss Y Zhao
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Jia Guo
- Department of Bioengineering, University of California Riverside, Riverside, California, USA
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2
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Woods JG, Achten E, Asllani I, Bolar DS, Dai W, Detre JA, Fan AP, Fernández-Seara MA, 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 MJP, Wang DJJ, Warnert EAH, 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. [PMID: 38594906 DOI: 10.1002/mrm.30091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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, Brighton, UK
- Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, New York, 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, New York, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Audrey P Fan
- Department of Biomedical Engineering, University of California Davis, Davis, California, USA
- Department of Neurology, University of California Davis, Davis, California, USA
| | - María A 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, Sheffield, UK
| | - Matthias Günther
- Imaging Physics, Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- Department of Physics and Electrical Engineering, University of Bremen, Bremen, Germany
| | - Jia Guo
- Department of Bioengineering, University of California Riverside, Riverside, California, USA
| | | | - Mai-Lan Ho
- Department of Radiology, University of Missouri, Columbia, Missouri, USA
| | - 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, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, 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, Texas, USA
| | - Henk-Jan Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, 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, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Manchester, 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, The Netherlands
| | - 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 J J Wang
- Laboratory of FMRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California, USA
| | - Esther A H Warnert
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Rotterdam, The Netherlands
| | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, California, 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, California, USA
- Maternal & Child Health Research Institute, Stanford University, Stanford, California, 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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Paschoal AM, Woods JG, Pinto J, Bron EE, Petr J, Kennedy McConnell FA, Bell L, Dounavi ME, van Praag CG, Mutsaerts HJMM, Taylor AO, Zhao MY, Brumer I, Chan WSM, Toner J, Hu J, Zhang LX, Domingos C, Monteiro SP, Figueiredo P, Harms AGJ, Padrela BE, Tham C, Abdalle A, Croal PL, Anazodo U. Reproducibility of arterial spin labeling cerebral blood flow image processing: A report of the ISMRM open science initiative for perfusion imaging (OSIPI)_and the ASL MRI challenge. Magn Reson Med 2024. [PMID: 38502108 DOI: 10.1002/mrm.30081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/20/2024]
Abstract
PURPOSE Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis. METHODS Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches. RESULTS Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results. CONCLUSIONS Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization.
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Affiliation(s)
- Andre M Paschoal
- Institute of Physics, University of Campinas, Campinas, Brazil
- LIM44, Institute of Radiology, Department of Radiology and Oncology of Clinical Hospital, University of Sao Paulo, Sao Paulo, Brazil
| | - Joseph G Woods
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Radiology, Center for Functional Magnetic Resonance Imaging, University of California, San Diego, La Jolla, California, USA
| | - Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Esther E Bron
- Department of Radiology & Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Jan Petr
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | - Flora A Kennedy McConnell
- Radiological Sciences, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK
| | - Laura Bell
- Clinical Imaging Group, Genentech, Inc., South San Francisco, California, USA
| | | | - Cassandra Gould van Praag
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Henk J M M Mutsaerts
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
| | | | - Moss Y Zhao
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Irène Brumer
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Wei Siang Marcus Chan
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Jack Toner
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Jian Hu
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Logan X Zhang
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Catarina Domingos
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico-Universidade de Lisboa, Lisbon, Portugal
| | - Sara P Monteiro
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico-Universidade de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico-Universidade de Lisboa, Lisbon, Portugal
| | - Alexander G J Harms
- Department of Radiology & Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Beatriz E Padrela
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
| | - Channelle Tham
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ahmed Abdalle
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Paula L Croal
- Radiological Sciences, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Udunna Anazodo
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
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4
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Woods JG, Schauman SS, Chiew M, Chappell MA, Okell TW. Time-encoded pseudo-continuous arterial spin labeling: Increasing SNR in ASL dynamic angiography. Magn Reson Med 2023; 89:1323-1341. [PMID: 36255158 PMCID: PMC10091734 DOI: 10.1002/mrm.29491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 08/28/2022] [Accepted: 09/23/2022] [Indexed: 02/01/2023]
Abstract
PURPOSE Dynamic angiography using arterial spin labeling (ASL) can provide detailed hemodynamic information. However, the long time-resolved readouts require small flip angles to preserve ASL signal for later timepoints, limiting SNR. By using time-encoded ASL to generate temporal information, the readout can be shortened. Here, the SNR improvements from using larger flip angles, made possible by the shorter readout, are quantitatively investigated. METHODS The SNR of a conventional protocol with nine Look-Locker readouts and a 4 × $$ \times $$ 3 time-encoded protocol with three Look-Locker readouts (giving nine matched timepoints) were compared using simulations and in vivo data. Both protocols were compared using readouts with constant flip angles (CFAs) and variable flip angles (VFAs), where the VFA scheme was designed to produce a consistent ASL signal across readouts. Optimization of the background suppression to minimize physiological noise across readouts was also explored. RESULTS The time-encoded protocol increased in vivo SNR by 103% and 96% when using CFAs or VFAs, respectively. Use of VFAs improved SNR compared with CFAs by 25% and 21% for the conventional and time-encoded protocols, respectively. The VFA scheme also removed signal discontinuities in the time-encoded data. Preliminary data suggest that optimizing the background suppression could improve in vivo SNR by a further 16%. CONCLUSIONS Time encoding can be used to generate additional temporal information in ASL angiography. This enables the use of larger flip angles, which can double the SNR compared with a non-time-encoded protocol. The shortened time-encoded readout can also lead to improved background suppression, reducing physiological noise and further improving SNR.
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Affiliation(s)
- Joseph G Woods
- Wellcome Centre for Integrated Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - S Sophie Schauman
- Wellcome Centre for Integrated Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Mark Chiew
- Wellcome Centre for Integrated Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Michael A Chappell
- Wellcome Centre for Integrated Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom.,Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom.,Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - Thomas W Okell
- Wellcome Centre for Integrated Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
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5
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Qin Q, Alsop DC, Bolar DS, Hernandez-Garcia L, Meakin J, Liu D, Nayak KS, Schmid S, van Osch MJP, Wong EC, Woods JG, Zaharchuk G, Zhao MY, Zun Z, Guo J. Erratum to: Velocity-selective arterial spin labeling perfusion MRI: A review of the state of the art and recommendations for clinical implementation (Magn Reson Med. 2022; 88:1528-1547). Magn Reson Med 2023; 89:1278-1279. [PMID: 36420917 PMCID: PMC10117579 DOI: 10.1002/mrm.29504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - David C Alsop
- Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Divya S Bolar
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California, San Diego La Jolla, California, USA
| | | | - James Meakin
- Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dapeng Liu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Krishna S Nayak
- Magnetic Resonance Engineering Laboratory, Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, California, USA
| | - Sophie Schmid
- C.J. Gorter Center for high field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Matthias J P van Osch
- C.J. Gorter Center for high field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Eric C Wong
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California, San Diego La Jolla, California, USA
| | - Joseph G Woods
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California, San Diego La Jolla, California, USA
| | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Moss Y Zhao
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Jia Guo
- Department of Bioengineering, University of California Riverside, Riverside, California, USA
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6
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Qin Q, Alsop DC, Bolar DS, Hernandez‐Garcia L, Meakin J, Liu D, Nayak KS, Schmid S, van Osch MJP, Wong EC, Woods JG, Zaharchuk G, Zhao MY, Zun Z, Guo J. Velocity-selective arterial spin labeling perfusion MRI: A review of the state of the art and recommendations for clinical implementation. Magn Reson Med 2022; 88:1528-1547. [PMID: 35819184 PMCID: PMC9543181 DOI: 10.1002/mrm.29371] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 05/16/2022] [Accepted: 06/08/2022] [Indexed: 12/11/2022]
Abstract
This review article provides an overview of the current status of velocity-selective arterial spin labeling (VSASL) perfusion MRI and is part of a wider effort arising from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group. Since publication of the 2015 consensus paper on arterial spin labeling (ASL) for cerebral perfusion imaging, important advancements have been made in the field. The ASL community has, therefore, decided to provide an extended perspective on various aspects of technical development and application. Because VSASL has the potential to become a principal ASL method because of its unique advantages over traditional approaches, an in-depth discussion was warranted. VSASL labels blood based on its velocity and creates a magnetic bolus immediately proximal to the microvasculature within the imaging volume. VSASL is, therefore, insensitive to transit delay effects, in contrast to spatially selective pulsed and (pseudo-) continuous ASL approaches. Recent technical developments have improved the robustness and the labeling efficiency of VSASL, making it a potentially more favorable ASL approach in a wide range of applications where transit delay effects are of concern. In this review article, we (1) describe the concepts and theoretical basis of VSASL; (2) describe different variants of VSASL and their implementation; (3) provide recommended parameters and practices for clinical adoption; (4) describe challenges in developing and implementing VSASL; and (5) describe its current applications. As VSASL continues to undergo rapid development, the focus of this review is to summarize the fundamental concepts of VSASL, describe existing VSASL techniques and applications, and provide recommendations to help the clinical community adopt VSASL.
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Affiliation(s)
- Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - David C. Alsop
- Department of RadiologyBeth Israel Deaconess Medical Center and Harvard Medical SchoolBostonMassachusettsUSA
| | - Divya S. Bolar
- Center for Functional Magnetic Resonance Imaging, Department of RadiologyUniversity of CaliforniaSan Diego La JollaCaliforniaUSA
| | | | - James Meakin
- Department of Radiology, Nuclear Medicine and AnatomyRadboud University Medical CenterNijmegenThe Netherlands
| | - Dapeng Liu
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Krishna S. Nayak
- Magnetic Resonance Engineering Laboratory, Ming Hsieh Department of Electrical EngineeringUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Sophie Schmid
- C.J. Gorter Center for high field MRI, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Matthias J. P. van Osch
- C.J. Gorter Center for high field MRI, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Eric C. Wong
- Center for Functional Magnetic Resonance Imaging, Department of RadiologyUniversity of CaliforniaSan Diego La JollaCaliforniaUSA
| | - Joseph G. Woods
- Center for Functional Magnetic Resonance Imaging, Department of RadiologyUniversity of CaliforniaSan Diego La JollaCaliforniaUSA
| | - Greg Zaharchuk
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Moss Y. Zhao
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Zungho Zun
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
| | - Jia Guo
- Department of BioengineeringUniversity of California RiversideRiversideCaliforniaUSA
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7
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Clement P, Castellaro M, Okell TW, Thomas DL, Vandemaele P, Elgayar S, Oliver-Taylor A, Kirk T, Woods JG, Vos SB, Kuijer JPA, Achten E, van Osch MJP, Detre JA, Lu H, Alsop DC, Chappell MA, Hernandez-Garcia L, Petr J, Mutsaerts HJMM. ASL-BIDS, the brain imaging data structure extension for arterial spin labeling. Sci Data 2022; 9:543. [PMID: 36068231 PMCID: PMC9448788 DOI: 10.1038/s41597-022-01615-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 08/05/2022] [Indexed: 11/29/2022] Open
Abstract
Arterial spin labeling (ASL) is a non-invasive MRI technique that allows for quantitative measurement of cerebral perfusion. Incomplete or inaccurate reporting of acquisition parameters complicates quantification, analysis, and sharing of ASL data, particularly for studies across multiple sites, platforms, and ASL methods. There is a strong need for standardization of ASL data storage, including acquisition metadata. Recently, ASL-BIDS, the BIDS extension for ASL, was developed and released in BIDS 1.5.0. This manuscript provides an overview of the development and design choices of this first ASL-BIDS extension, which is mainly aimed at clinical ASL applications. Discussed are the structure of the ASL data, focussing on storage order of the ASL time series and implementation of calibration approaches, unit scaling, ASL-related BIDS fields, and storage of the labeling plane information. Additionally, an overview of ASL-BIDS compatible conversion and ASL analysis software and ASL example datasets in BIDS format is provided. We anticipate that large-scale adoption of ASL-BIDS will improve the reproducibility of ASL research.
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Affiliation(s)
- Patricia Clement
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium.
| | - Marco Castellaro
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Thomas W Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - David L Thomas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, UK.,Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, UK
| | | | - Sara Elgayar
- Faculty of computers and information science, Ain Shams University, Cairo, Egypt
| | | | - Thomas Kirk
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.,Sir Peter Mansfield Imaging Center, School of Medicine, University of Nottingham, Nottingham, UK
| | - Joseph G Woods
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, 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
| | - Sjoerd B Vos
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, UK.,Centre for Medical Image Computing, University College London, London, UK
| | - Joost P A Kuijer
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Eric Achten
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Matthias J P van Osch
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - John A Detre
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - David C Alsop
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Michael A Chappell
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Sir Peter Mansfield Imaging Center, School of Medicine, University of Nottingham, Nottingham, UK.,Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK.,Nottingham Biomedical Research Centre, Queens Medical Centre, University of Nottingham, Nottingham, UK
| | | | - Jan Petr
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands.,Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | - Henk J M M Mutsaerts
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium.,Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
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Woods JG, Wong EC, Boyd EC, Bolar DS. VESPA ASL: VElocity and SPAtially Selective Arterial Spin Labeling. Magn Reson Med 2022; 87:2667-2684. [PMID: 35061920 DOI: 10.1002/mrm.29159] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/25/2021] [Accepted: 12/22/2021] [Indexed: 01/22/2023]
Abstract
PURPOSE Spatially selective arterial spin labeling (ASL) perfusion MRI is sensitive to arterial transit times (ATT) that can result in inaccurate perfusion quantification when ATTs are long. Velocity-selective ASL is robust to this effect because blood is labeled within the imaging region, allowing immediate label delivery. However, velocity-selective ASL cannot characterize ATTs, which can provide important clinical information. Here, we introduce a novel pulse sequence, called VESPA ASL, that combines velocity-selective and pseudo-continuous ASL to simultaneously label different pools of arterial blood for robust cerebral blood flow (CBF) and ATT measurement. METHODS The VESPA ASL sequence is similar to velocity-selective ASL, but the velocity-selective labeling is made spatially selective, and pseudo-continuous ASL is added to fill the inflow time. The choice of inflow time and other sequence settings were explored. VESPA ASL was compared to multi-delay pseudo-continuous ASL and velocity-selective ASL through simulations and test-retest experiments in healthy volunteers. RESULTS VESPA ASL is shown to accurately measure CBF in the presence of long ATTs, and ATTs < TI can also be measured. Measurements were similar to established ASL techniques when ATT was short. When ATT was long, VESPA ASL measured CBF more accurately than multi-delay pseudo-continuous ASL, which tended to underestimate CBF. CONCLUSION VESPA ASL is a novel and robust approach to simultaneously measure CBF and ATT and offers important advantages over existing methods. It fills an important clinical need for noninvasive perfusion and transit time imaging in vascular diseases with delayed arterial transit.
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Affiliation(s)
- Joseph G Woods
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Eric C Wong
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California San Diego, La Jolla, California, USA.,Department of Psychiatry, University of California San Diego, La Jolla, California, USA
| | - Emma C Boyd
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California San Diego, La Jolla, California, USA.,Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Divya S Bolar
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California San Diego, La Jolla, California, USA
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Zhang LX, Woods JG, Okell TW, Chappell MA. Examination of optimized protocols for pCASL: Sensitivity to macrovascular contamination, flow dispersion, and prolonged arterial transit time. Magn Reson Med 2021; 86:2208-2219. [PMID: 34009682 PMCID: PMC8581991 DOI: 10.1002/mrm.28839] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/19/2021] [Accepted: 04/23/2021] [Indexed: 01/01/2023]
Abstract
Purpose Previously, multi‐ post‐labeling delays (PLD) pseudo‐continuous arterial spin labeling (pCASL) protocols have been optimized for the estimation accuracy of the cerebral blood flow (CBF) with/without the arterial transit time (ATT) under a standard kinetic model and a normal ATT range. This study aims to examine the estimation errors of these protocols under the effects of macrovascular contamination, flow dispersion, and prolonged arrival times, all of which might differ substantially in elderly or pathological groups. Methods Simulated data for four protocols with varying degrees of arterial blood volume (aBV), flow dispersion, and ATTs were fitted with different kinetic models, both with and without explicit correction for macrovascular signal contamination (MVC), to obtain CBF and ATT estimates. Sensitivity to MVC was defined and calculated when aBV > 0.5%. A previously acquired dataset was retrospectively analyzed to compare with simulation. Results All protocols showed underestimation of CBF and ATT in the prolonged ATT range. With MVC, the protocol optimized for CBF only (CBFopt) had the lowest sensitivity value to MVC, 33.47% and 60.21% error per 1% aBV in simulation and in vivo, respectively, among multi‐PLD protocols. All multi‐PLD protocols showed a significant decrease in estimation error when an extended kinetic model was used. Increasing flow dispersion at short ATTs caused increasing CBF and ATT overestimation in all protocols. Conclusion CBFopt was the least sensitive protocol to prolonged ATT and MVC for CBF estimation while maintaining reasonably good performance in estimating ATT. Explicitly including a macrovascular component in the kinetic model was shown to be a feasible approach in controlling for MVC.
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Affiliation(s)
- Logan X Zhang
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Joseph G Woods
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom.,Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Thomas W Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Michael A Chappell
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.,Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom.,Mental Health and Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom.,Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom.,Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
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10
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Woods JG, Chappell MA, Okell TW. Designing and comparing optimized pseudo-continuous Arterial Spin Labeling protocols for measurement of cerebral blood flow. Neuroimage 2020; 223:117246. [PMID: 32853814 PMCID: PMC7762814 DOI: 10.1016/j.neuroimage.2020.117246] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 07/03/2020] [Accepted: 08/05/2020] [Indexed: 01/24/2023] Open
Abstract
Arterial Spin Labeling (ASL) is a non-invasive, non-contrast, perfusion imaging technique which is inherently SNR limited. It is, therefore, important to carefully design scan protocols to ensure accurate measurements. Many pseudo-continuous ASL (PCASL) protocol designs have been proposed for measuring cerebral blood flow (CBF), but it has not yet been demonstrated which design offers the most accurate and repeatable CBF measurements. In this study, a wide range of literature PCASL protocols were first optimized for CBF accuracy and then compared using Monte Carlo simulations and in vivo experiments. The protocols included single-delay, sequential and time-encoded multi-timepoint protocols, and several novel protocol designs, which are hybrids of time-encoded and sequential multi-timepoint protocols. It was found that several multi-timepoint protocols produced more confident, accurate, and repeatable CBF estimates than the single-delay protocol, while also generating maps of arterial transit time. Of the literature protocols, the time-encoded protocol with T1-adjusted label durations gave the most confident and accurate CBF estimates in vivo (16% and 40% better than single-delay), while the sequential multi-timepoint protocol was the most repeatable (20% more repeatable than single-delay). One of the novel hybrid protocols, HybridT1-adj, was found to produce the most confident, accurate and repeatable CBF estimates out of all the protocols tested in both simulations and in vivo (24%, 47%, and 28% more confident, accurate, and repeatable than single-delay in vivo). The HybridT1-adj protocol makes use of the best aspects of both time-encoded and sequential multi-timepoint protocols and should be a useful tool for accurately and efficiently measuring CBF.
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Affiliation(s)
- Joseph G Woods
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Michael A Chappell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom; Institute of Biomedical Engineering, Department of Engineering, University of Oxford, Oxford, United Kingdom
| | - Thomas W Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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Woods JG, Chappell MA, Okell TW. A general framework for optimizing arterial spin labeling MRI experiments. Magn Reson Med 2019; 81:2474-2488. [PMID: 30588656 PMCID: PMC6492260 DOI: 10.1002/mrm.27580] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2018] [Revised: 09/21/2018] [Accepted: 10/02/2018] [Indexed: 11/22/2022]
Abstract
PURPOSE Arterial spin labeling (ASL) MRI is a non-invasive perfusion imaging technique that is inherently SNR limited, so scan protocols ideally need to be rigorously optimized to provide the most accurate measurements. A general framework is presented for optimizing ASL experiments to achieve optimal accuracy for perfusion estimates and, if required, other hemodynamic parameters, within a fixed scan time. The effectiveness of this framework is then demonstrated by optimizing the post-labeling delays (PLDs) of a multi-PLD pseudo-continuous ASL experiment and validating the improvement using simulations and in vivo data. THEORY AND METHODS A simple framework is proposed based on the use of the Cramér-Rao lower bound to find the protocol design which minimizes the predicted parameter estimation errors. Protocols were optimized for cerebral blood flow (CBF) accuracy or both CBF and arterial transit time (ATT) accuracy and compared to a conventional multi-PLD protocol, with evenly spaced PLDs, and a single-PLD protocol, using simulations and in vivo experiments in healthy volunteers. RESULTS Simulations and in vivo data agreed extremely well with the predicted performance of all protocols. For the in vivo experiments, optimizing for just CBF resulted in a 48% and 15% decrease in CBF errors, relative to the reference multi-PLD and single-PLD protocols, respectively. Optimizing for both CBF and ATT reduced CBF errors by 37%, without a reduction in ATT accuracy, relative to the reference multi-PLD protocol. CONCLUSION The presented framework can effectively design ASL experiments to minimize measurement errors based on the requirements of the scan.
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Affiliation(s)
- Joseph G. Woods
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Michael A. Chappell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
- Institute of Biomedical Engineering, Department of EngineeringUniversity of OxfordOxfordUnited Kingdom
| | - Thomas W. Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
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Woods JG, Dillion T. The performance review approach to improving productivity. Personnel 1985; 62:20-7. [PMID: 10270456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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Woods JG. INsects parasitic on the saw-whet owl, Aegolius acadicus (Gmelin) (Aves: Strigidae). CAN J ZOOL 1971; 49:959-60. [PMID: 5165021 DOI: 10.1139/z71-144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Records of nine parasitic insects from Aegolius acadicus (Gmelin) are listed. These include three Mallophaga, Siphonaptera, and Diptera.
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