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Song J, Khanduja S, Rando H, Shi W, Hazel K, Pottanat GP, Jones E, Xu C, Hu Z, Lin D, Yasar S, Lu H, Cho SM, Jiang D. Brain Frontal-Lobe Misery Perfusion in COVID-19 ICU Survivors: An MRI Pilot Study. Brain Sci 2024; 14:94. [PMID: 38248309 PMCID: PMC10813864 DOI: 10.3390/brainsci14010094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024] Open
Abstract
Post-acute COVID-19 syndrome (PCS) is highly prevalent. Critically ill patients requiring intensive care unit (ICU) admission are at a higher risk of developing PCS. The mechanisms underlying PCS are still under investigation and may involve microvascular damage in the brain. Cerebral misery perfusion, characterized by reduced cerebral blood flow (CBF) and elevated oxygen extraction fraction (OEF) in affected brain areas, has been demonstrated in cerebrovascular diseases such as carotid occlusion and stroke. This pilot study aimed to examine whether COVID-19 ICU survivors exhibited regional misery perfusion, indicating cerebral microvascular damage. In total, 7 COVID-19 ICU survivors (4 female, 20-77 years old) and 19 age- and sex-matched healthy controls (12 female, 22-77 years old) were studied. The average interval between ICU admission and the MRI scan was 118.6 ± 30.3 days. The regional OEF was measured using a recently developed technique, accelerated T2-relaxation-under-phase-contrast MRI, while the regional CBF was assessed using pseudo-continuous arterial spin labeling. COVID-19 ICU survivors exhibited elevated OEF (β = 5.21 ± 2.48%, p = 0.047) and reduced relative CBF (β = -0.083 ± 0.025, p = 0.003) in the frontal lobe compared to healthy controls. In conclusion, misery perfusion was observed in the frontal lobe of COVID-19 ICU survivors, suggesting microvascular damage in this critical brain area for high-level cognitive functions that are known to manifest deficits in PCS. Physiological biomarkers such as OEF and CBF may provide new tools to improve the understanding and treatment of PCS.
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Affiliation(s)
- Jie Song
- Department of Biomedical Engineering, Johns Hopkins University School of Engineering, Baltimore, MD 21218, USA
| | - Shivalika Khanduja
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Hannah Rando
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Wen Shi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Park 324, Baltimore, MD 21287, USA
| | - Kaisha Hazel
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Park 324, Baltimore, MD 21287, USA
| | - George Paul Pottanat
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Park 324, Baltimore, MD 21287, USA
| | - Ebony Jones
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Park 324, Baltimore, MD 21287, USA
| | - Cuimei Xu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Park 324, Baltimore, MD 21287, USA
| | - Zhiyi Hu
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Park 324, Baltimore, MD 21287, USA
| | - Doris Lin
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Park 324, Baltimore, MD 21287, USA
| | - Sevil Yasar
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Hanzhang Lu
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Park 324, Baltimore, MD 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD 21205, USA
| | - Sung-Min Cho
- Department of Neurology, Neurosurgery, Surgery, Anesthesiology, and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Park 324, Baltimore, MD 21287, USA
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Xu X, Tan Z, Fan M, Ma M, Fang W, Liang J, Xiao Z, Shi C, Luo L. Comparative Study of Multi-Delay Pseudo-Continuous Arterial Spin Labeling Perfusion MRI and CT Perfusion in Ischemic Stroke Disease. Front Neuroinform 2021; 15:719719. [PMID: 34456703 PMCID: PMC8386683 DOI: 10.3389/fninf.2021.719719] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 06/28/2021] [Indexed: 11/13/2022] Open
Abstract
With the aging population, stroke has gradually become the leading cause of death and disability among adults. It is necessary to verify whether multi-delay pseudo-continuous arterial spin labeling (pCASL) MRI can be used as a standard neuroimaging protocol in the patients with ischemic stroke. We aimed to investigate the clinical utility of multi-delay pCASL for evaluating cerebral perfusion in ischemic stroke disease. Twenty-one ischemic stroke patients [18 men and 3 women; median age, 62 years (age range, 37-84 years)] were enrolled in this study. All patients underwent examinations, including the multi-delay pCASL protocol (using 6 PLDs between 1,000 and 3,500 ms) and computed tomography perfusion (CTP). The cerebral blood flow (CBF) and arterial transit time (ATT) maps were obtained by the multi-delay pCASL protocol, while CBF and mean transit time (MTT) maps were derived by CTP measurements. Based on the voxel level analysis, Pearson correlation coefficients were used to estimate the associations between the two modalities in the gray matter, white matter, and whole brain of each subject. Moderate to high positive associations between ASL-CBF and CTP-CBF were acquired by voxel-level-wise analysis in the gray matter, white matter, and whole brain of the enrolled patients (all P < 0.005), and the average Pearson correlation coefficients were 0.647, 0.585, and 0.646, respectively. Highly significant positive correlations between ASL-ATT and CTP-MTT were obtained by voxel-level-wise associations in the gray matter, white matter, and whole brain (all P < 0.005), and the average Pearson correlation coefficients were 0.787, 0.707, and 0.799, respectively. In addition, significant associations between ASL and CT perfusion were obtained in the gray, white matter and whole brain, according to the subgroup analyses of patient's age and disease stage. There is a correlation between perfusion parameters from multi-delay pCASL and CT perfusion imaging in patients with ischemic stroke. Multi-delay pCASL is radiation-free and non-invasive, and could be an alternative method to CT scans for assessing perfusion in ischemic stroke disease.
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Affiliation(s)
- Xi Xu
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Zefeng Tan
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Department of Neurology, Shun De Hospital of Jinan University, Foshan, China
| | - Meng Fan
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Mengjie Ma
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Weimin Fang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jianye Liang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Department of Medical Imaging, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zeyu Xiao
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Changzheng Shi
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Engineering Research Center of Medical Imaging Artificial Intelligence for Precision Diagnosis and Treatment, Guangzhou, China
| | - Liangping Luo
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Engineering Research Center of Medical Imaging Artificial Intelligence for Precision Diagnosis and Treatment, Guangzhou, China
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4
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van Harten TW, Dzyubachyk O, Bokkers RPH, Wermer MJH, van Osch MJP. On the ability to exploit signal fluctuations in pseudocontinuous arterial spin labeling for inferring the major flow territories from a traditional perfusion scan. Neuroimage 2021; 230:117813. [PMID: 33524582 DOI: 10.1016/j.neuroimage.2021.117813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 01/14/2021] [Accepted: 01/20/2021] [Indexed: 10/22/2022] Open
Abstract
In arterial spin labeling (ASL) a magnetic label is applied to the flowing blood in feeding arteries allowing depiction of cerebral perfusion maps. The labeling efficiency depends, however, on blood velocity and local field inhomogeneities and is, therefore, not constant over time. In this work, we investigate the ability of statistical methods used in functional connectivity research to infer flow territory information from traditional pseudo-continuous ASL (pCASL) scans by exploiting artery-specific signal fluctuations. By applying an additional gradient during labeling the minimum amount of signal fluctuation that allows discrimination of the main flow territories is determined. The following three approaches were tested for their performance on inferring the large vessel flow territories of the brain: a general linear model (GLM), an independent component analysis (ICA) and t-stochastic neighbor embedding. Furthermore, to investigate the effect of large vessel pathology, standard ASL scans of three patients with a unilateral stenosis (>70%) of one of the internal carotid arteries were retrospectively analyzed using ICA and t-SNE. Our results suggest that the amount of natural-occurring variation in labeling efficiency is insufficient to determine large vessel flow territories. When applying additional vessel-encoded gradients these methods are able to distinguish flow territories from one another, but this would result in approximately 8.5% lower perfusion signal and thus also a reduction in SNR of the same magnitude.
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Affiliation(s)
- T W van Harten
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Postbus 9600, 2300 RC Leiden, the Netherlands.
| | - O Dzyubachyk
- Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center, Postbus 9600, 2300 RC Leiden, the Netherlands
| | - R P H Bokkers
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Postbus 30.001, 3700 RB Groningen, the Netherlands
| | - M J H Wermer
- Department of Neurology, Leiden University Medical Center, Postbus 9600, 2300 RC Leiden, the Netherlands
| | - M J P van Osch
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Postbus 9600, 2300 RC Leiden, the Netherlands
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Mutsaerts HJMM, Petr J, Groot P, Vandemaele P, Ingala S, Robertson AD, Václavů L, Groote I, Kuijf H, Zelaya F, O'Daly O, Hilal S, Wink AM, Kant I, Caan MWA, Morgan C, de Bresser J, Lysvik E, Schrantee A, Bjørnebekk A, Clement P, Shirzadi Z, Kuijer JPA, Wottschel V, Anazodo UC, Pajkrt D, Richard E, Bokkers RPH, Reneman L, Masellis M, Günther M, MacIntosh BJ, Achten E, Chappell MA, van Osch MJP, Golay X, Thomas DL, De Vita E, Bjørnerud A, Nederveen A, Hendrikse J, Asllani I, Barkhof F. ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies. Neuroimage 2020; 219:117031. [PMID: 32526385 DOI: 10.1016/j.neuroimage.2020.117031] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 05/29/2020] [Accepted: 06/04/2020] [Indexed: 01/01/2023] Open
Abstract
Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.
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Affiliation(s)
- Henk J M M Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands; Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands; Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Kate Gleason College of Engineering, Rochester Institute of Technology, NY, USA; Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium.
| | - Jan Petr
- Kate Gleason College of Engineering, Rochester Institute of Technology, NY, USA; Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | - Paul Groot
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Pieter Vandemaele
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Andrew D Robertson
- Schlegel-UW Research Institute for Aging, University of Waterloo, Waterloo, Ontario, Canada
| | - Lena Václavů
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Inge Groote
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Hugo Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Fernando Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Saima Hilal
- Department of Pharmacology, National University of Singapore, Singapore; Memory Aging and Cognition Center, National University Health System, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Ilse Kant
- Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Intensive Care, University Medical Centre, Utrecht, the Netherlands
| | - Matthan W A Caan
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Location Academic Medical Center, Amsterdam, the Netherlands
| | - Catherine Morgan
- School of Psychology and Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Jeroen de Bresser
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Elisabeth Lysvik
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Anouk Schrantee
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Astrid Bjørnebekk
- The Anabolic Androgenic Steroid Research Group, National Advisory Unit on Substance Use Disorder Treatment, Oslo University Hospital, Oslo, Norway
| | - Patricia Clement
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Zahra Shirzadi
- Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Joost P A Kuijer
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Udunna C Anazodo
- Department of Medical Biophysics, University of Western Ontario, London, Canada; Imaging Division, Lawson Health Research Institute, London, Canada
| | - Dasja Pajkrt
- Department of Pediatric Infectious Diseases, Emma Children's Hospital, Amsterdam University Medical Centre, Location Academic Medical Center, Amsterdam, the Netherlands
| | - Edo Richard
- Department of Neurology, Donders Institute for Brain, Behavior and Cognition, Radboud University Medical Centre, Nijmegen, the Netherlands; Neurology, Amsterdam University Medical Center, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Reinoud P H Bokkers
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Liesbeth Reneman
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Mario Masellis
- Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Matthias Günther
- Fraunhofer MEVIS, Bremen, Germany; University of Bremen, Bremen, Germany; Mediri GmbH, Heidelberg, Germany
| | | | - Eric Achten
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Michael A Chappell
- Institute of Biomedical Engineering, Department of Engineering Science & Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Matthias J P van Osch
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Xavier Golay
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David L Thomas
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK
| | - Atle Bjørnerud
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Aart Nederveen
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Jeroen Hendrikse
- Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Iris Asllani
- Kate Gleason College of Engineering, Rochester Institute of Technology, NY, USA; Clinical Imaging Sciences Centre, Department of Neuroscience, Brighton and Sussex Medical School, Brighton, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands; UCL Queen Square Institute of Neurology, University College London, London, UK; Centre for Medical Image Computing (CMIC), Faculty of Engineering Science, University College London, London, UK
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Harteveld AA, de Boer A, Franklin SL, Leiner T, van Stralen M, Bos C. Comparison of multi-delay FAIR and pCASL labeling approaches for renal perfusion quantification at 3T MRI. MAGMA (NEW YORK, N.Y.) 2020; 33:81-94. [PMID: 31811490 PMCID: PMC7021666 DOI: 10.1007/s10334-019-00806-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 10/29/2019] [Accepted: 11/19/2019] [Indexed: 12/18/2022]
Abstract
OBJECTIVE To compare the most commonly used labeling approaches, flow-sensitive alternating inversion recovery (FAIR) and pseudocontinuous arterial spin labeling (pCASL), for renal perfusion measurement using arterial spin labeling (ASL) MRI. METHODS Multi-delay FAIR and pCASL were performed in 16 middle-aged healthy volunteers on two different occasions at 3T. Relative perfusion-weighted signal (PWS), temporal SNR (tSNR), renal blood flow (RBF), and arterial transit time (ATT) were calculated for the cortex and medulla in both kidneys. Bland-Altman plots, intra-class correlation coefficient, and within-subject coefficient of variation were used to assess reliability and agreement between measurements. RESULTS For the first visit, RBF was 362 ± 57 and 140 ± 47 mL/min/100 g, and ATT was 0.47 ± 0.13 and 0.70 ± 0.10 s in cortex and medulla, respectively, using FAIR; RBF was 201 ± 72 and 84 ± 27 mL/min/100 g, and ATT was 0.71 ± 0.25 and 0.86 ± 0.12 s in cortex and medulla, respectively, using pCASL. For both labeling approaches, RBF and ATT values were not significantly different between visits. Overall, FAIR showed higher PWS and tSNR. Moreover, repeatability of perfusion parameters was better using FAIR. DISCUSSION This study showed that compared to (balanced) pCASL, FAIR perfusion values were significantly higher and more comparable between visits.
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Affiliation(s)
- Anita A Harteveld
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Postbox 85500, 3508 GA, Utrecht, The Netherlands.
| | - Anneloes de Boer
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Postbox 85500, 3508 GA, Utrecht, The Netherlands
| | - Suzanne Lisa Franklin
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Postbox 85500, 3508 GA, Utrecht, The Netherlands
- Department of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, The Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Postbox 85500, 3508 GA, Utrecht, The Netherlands
| | - Marijn van Stralen
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Postbox 85500, 3508 GA, Utrecht, The Netherlands
| | - Clemens Bos
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Postbox 85500, 3508 GA, Utrecht, The Netherlands
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