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Bitzer F, Walger L, Bauer T, Schulte F, Gaertner FC, Schmitz M, Schidlowski M, von Wrede R, Rácz A, Baumgartner T, Gnatkovsky V, Paech D, Borger V, Vatter H, Weber B, Michels DL, Stöcker T, Essler M, Sander JW, Radbruch A, Surges R, Rüber T. Higher Validity, Lower Radiation: A New Ictal Single-Photon Emission Computed Tomography Framework. Ann Neurol 2024. [PMID: 39166769 DOI: 10.1002/ana.27061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 07/30/2024] [Accepted: 08/05/2024] [Indexed: 08/23/2024]
Abstract
OBJECTIVE To assess whether arterial spin labeling perfusion images of healthy controls can enhance ictal single-photon emission computed tomography analysis and whether the acquisition of the interictal image can be omitted. METHODS We developed 2 pipelines: The first uses ictal and interictal images and compares these to single-photon emission computed tomography and arterial spin labeling of healthy controls. The second pipeline uses only the ictal image and the analogous healthy controls. Both pipelines were compared to the gold standard analysis and evaluated on data of individuals with epilepsy who underwent ictal single-photon emission computed tomography imaging during presurgical evaluation between 2010 and 2022. Fifty healthy controls prospectively underwent arterial spin labeling imaging. The correspondence between the detected hyperperfusion and the postoperative resection cavity or the presumably affected lobe was assessed using Dice score and mean Euclidean distance. Additionally, the outcomes of the pipelines were automatically assigned to 1 of 5 concordance categories. RESULTS Inclusion criteria were met by 43 individuals who underwent epilepsy surgery and by 73 non-surgical individuals with epilepsy. Compared to the gold standard analysis, both pipelines resulted in significantly higher Dice scores and lower mean distances (p < 0.05). The combination of both provided localizing results in 85/116 cases, compared to 54/116 generated by the current gold standard analysis and the ictal image alone produced localizing results in 60/116 (52%) cases. INTERPRETATION We propose a new ictal single-photon emission computed tomography protocol; it finds relevantly more ictal hyperperfusion, and halves the radiation dose in about half of the individuals. ANN NEUROL 2024.
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Affiliation(s)
- Felix Bitzer
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Lennart Walger
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Tobias Bauer
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Freya Schulte
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | | | - Matthias Schmitz
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | | | - Randi von Wrede
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Attila Rácz
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | | | - Vadym Gnatkovsky
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Daniel Paech
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Valeri Borger
- Department of Neurosurgery, University Hospital Bonn, Bonn, Germany
| | - Hartmut Vatter
- Department of Neurosurgery, University Hospital Bonn, Bonn, Germany
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany
| | - Dominik L Michels
- Department of Computer Science, Technische Universität Darmstadt, Darmstadt, Germany
| | - Tony Stöcker
- MR Physics, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Markus Essler
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
| | - Josemir W Sander
- Department of Clinical & Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Chalfont St Peter, UK
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | | | - Rainer Surges
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Theodor Rüber
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
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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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Wang X, Wang L, Wu Y, Lv X, Xu Y, Dou W, Zhang H, Wu J, Shang S. Intracerebral hemodynamic abnormalities in patients with Parkinson's disease: Comparison between multi-delay arterial spin labelling and conventional single-delay arterial spin labelling. Diagn Interv Imaging 2024; 105:281-291. [PMID: 38310001 DOI: 10.1016/j.diii.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/05/2024]
Abstract
PURPOSE The purpose of this study was to analyze the intracerebral abnormalities of hemodynamics in patients with Parkinson's disease (PD) through arterial spin labelling (ASL) technique with multi-delay ASL (MDASL) and conventional single-delay ASL (SDASL) protocols and to verify the potential clinical application of these features for the diagnosis of PD. MATERIALS AND METHODS Perfusion data of the brain obtained using MDASL and SDASL in patients with PD were compared to those obtained in healthy control (HC) subjects. Intergroup comparisons of z-scored cerebral blood flow (zCBF), arterial transit time (zATT) and cerebral blood volume (zCBV) were performed via voxel-based analysis. Performance of these perfusion metrics were estimated using area under the receiver operating characteristic curve (AUC) and compared using Delong test. RESULTS A total of 47 patients with PD (29 men; 18 women; mean age, 69.0 ± 7.6 (standard deviation, [SD]) years; range: 50.0-84.0 years) and 50 HC subjects (28 men; 22 women; mean age, 70.1 ± 6.2 [SD] years; range: 50.0-93.0 years) were included. Relative to the uncorrected-zCBF map, the corrected-zCBF map further refined the distributed brain regions in the PD group versus the HC group, manifested as the extension of motor-related regions (PFWE < 0.001). Compared to the HC subjects, patients with PD had elevated zATT and zCBV in the right putamen, a shortened zATT in the superior frontal gyrus, and specific zCBV variations in the left precuneus and the right supplementary motor area (PFWE < 0.001). The corrected-zCBF (AUC, 0.90; 95% confidence interval [CI]: 0.84-0.96) showed better classification performance than uncorrected-zCBF (AUC, 0.84; 95% CI: 0.75-0.92) (P = 0.035). zCBV achieved an AUC of 0.89 (95% CI: 0.82-0.96) and zATT achieved an AUC of 0.66 (95% CI: 0.55-0.77). The integration model of hemodynamic features from MDASL provided improved performance (AUC, 0.97; 95% CI: 0.95-0.98) for the diagnosis of PD by comparison with each perfusion model (P < 0.001). CONCLUSION ASL identifies impaired hemodynamics in patients with PD including regional abnormalities of CBF, CBV and ATT, which can better be mapped with MDASL compared to SDASL. These findings provide complementary depictions of perfusion abnormalities in patients with PD and highlight the clinical feasibility of MDASL.
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Affiliation(s)
- Xue Wang
- Graduate school of Dalian Medical University, Dalian 116000, China; Department of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou 225009, China
| | - Lijuan Wang
- Department of Radiology, Jintang First People's Hospital, Sichuan University, Chengdu 610499, China
| | - Yating Wu
- Graduate school of Dalian Medical University, Dalian 116000, China; Department of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou 225009, China
| | - Xiang Lv
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou 225009, China
| | - Yao Xu
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou 225009, China
| | - Weiqiang Dou
- MR Research China, GE Healthcare, Beijing 100176, China
| | - Hongying Zhang
- Department of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou 225009, China
| | - Jingtao Wu
- Department of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou 225009, China
| | - Song'an Shang
- Department of Medical imaging center, Clinical Medical College, Yangzhou University, Yangzhou 225009, China.
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Fan X, Lai Z, Lin T, Li K, Hou B, You H, Wei J, Qu J, Liu B, Zuo Z, Feng F. Multidelay MR Arterial Spin Labeling Perfusion Map for the Prediction of Cerebral Hyperperfusion After Carotid Endarterectomy. J Magn Reson Imaging 2023; 58:1245-1255. [PMID: 36951494 DOI: 10.1002/jmri.28634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Multidelay arterial spin labeling (ASL) generates time-resolved perfusion maps, which may provide sufficient and accurate hemodynamic information in carotid stenosis. PURPOSE To use imaging markers derived from multidelay ASL magnetic resonance imaging (MRI) and to determine the optimal strategy for predicting cerebral hyperperfusion after carotid endarterectomy (CEA). STUDY TYPE Prospective observational cohort. SUBJECTS A total of 79 patients who underwent CEA for carotid stenosis. FIELD STRENGTH/SEQUENCE A 3.0 T/pseudo-continuous ASL with three postlabeling delays of 1.0, 1.57, and 2.46 seconds using fast-spin echo readout. ASSESSMENT Cerebral perfusion pressure, antegrade, and collateral flow were scored on a four-grade ordinal scale based on preoperative multidelay ASL perfusion maps. Simultaneously, quantitative hemodynamic parameters including cerebral blood flow (CBF), arterial transit time (ATT), relative CBF (rCBF) and relative ATT (rATT; ipsilateral/contralateral values) were calculated. On the CBF ratio map obtained through dividing postoperative by preoperative CBF map, regions of interest were placed covering ipsilateral middle cerebral artery territory. Three neuroradiologists conducted this procedure. Cerebral hyperperfusion was defined as a CBF ratio >2. STATISTICAL TESTS Weighted κ values, independent sample t test, chi-square test, Mann-Whitney U-test, multivariable logistic regression analysis, receiver-operating characteristic curve analysis, and Delong test. Significance level was P < 0.05. RESULTS Cerebral hyperperfusion was observed in 15 (19%) patients. Higher blood pressure (odd ratio [OR] = 1.08) and carotid near-occlusion (NO; OR = 7.31) were clinical risk factors for postoperative hyperperfusion. Poor ASL perfusion score (OR = 37.33), decreased CBF (OR = 0.74), prolonged ATT (OR = 1.02), lower rCBF (OR = 0.91), and higher rATT (OR = 1.12) were independent imaging predictors of hyperperfusion. ASL perfusion score exhibited the highest specificity (95.3%), while CBF exhibited the highest sensitivity (93.3%) for the prediction of hyperperfusion. When combined with ASL perfusion score, CBF and ATT, the predictive ability was significantly higher than using blood pressure and NO alone (AUC: 0.98 vs. 0.78). DATA CONCLUSIONS Multidelay ASL can accurately predict cerebral hyperperfusion after CEA with high sensitivity and specificity. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 5.
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Affiliation(s)
- Xiaoyuan Fan
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhichao Lai
- Department of Vascular Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tianye Lin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kang Li
- Department of Vascular Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui You
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Juan Wei
- GE Healthcare, MR Research China, Beijing, China
| | - Jianxun Qu
- GE Healthcare, MR Research China, Beijing, China
| | - Bao Liu
- Department of Vascular Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
- Sino-Danish college, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- State Key Laboratory of Difficult, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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