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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson SD, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group. Magn Reson Med 2024; 91:1834-1862. [PMID: 38247051 PMCID: PMC10950544 DOI: 10.1002/mrm.30006] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/31/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Daniel Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Centre of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, New York, USA
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, New York, USA
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2
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Stellingwerff MD, Al-Saady ML, Chan KS, Dvorak A, Marques JP, Kolind S, Roosendaal SD, Wolf NI, Barkhof F, van der Knaap MS, Pouwels PJW. Applicability of multiple quantitative magnetic resonance methods in genetic brain white matter disorders. J Neuroimaging 2024; 34:61-77. [PMID: 37925602 DOI: 10.1111/jon.13167] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/29/2023] [Accepted: 10/20/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND AND PURPOSE Magnetic resonance imaging (MRI) measures of tissue microstructure are important for monitoring brain white matter (WM) disorders like leukodystrophies and multiple sclerosis. They should be sensitive to underlying pathological changes. Three whole-brain isotropic quantitative methods were applied and compared within a cohort of controls and leukodystrophy patients: two novel myelin water imaging (MWI) techniques (multi-compartment relaxometry diffusion-informed MWI: MCR-DIMWI, and multi-echo T2 relaxation imaging with compressed sensing: METRICS) and neurite orientation dispersion and density imaging (NODDI). METHODS For 9 patients with different leukodystrophies (age range 0.4-62.4 years) and 15 control subjects (2.3-61.3 years), T1-weighted MRI, fluid-attenuated inversion recovery, multi-echo gradient echo with variable flip angles, METRICS, and multi-shell diffusion-weighted imaging were acquired on 3 Tesla. MCR-DIMWI, METRICS, NODDI, and quality control measures were extracted to evaluate differences between patients and controls in WM and deep gray matter (GM) regions of interest (ROIs). Pearson correlations, effect size calculations, and multi-level analyses were performed. RESULTS MCR-DIMWI and METRICS-derived myelin water fractions (MWFs) were lower and relaxation times were higher in patients than in controls. Effect sizes of MWF values and relaxation times were large for both techniques. Differences between patients and controls were more pronounced in WM ROIs than in deep GM. MCR-DIMWI-MWFs were more homogeneous within ROIs and more bilaterally symmetrical than METRICS-MWFs. The neurite density index was more sensitive in detecting differences between patients and controls than fractional anisotropy. Most measures obtained from MCR-DIMWI, METRICS, NODDI, and diffusion tensor imaging correlated strongly with each other. CONCLUSION This proof-of-concept study shows that MCR-DIMWI, METRICS, and NODDI are sensitive techniques to detect changes in tissue microstructure in WM disorders.
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Affiliation(s)
- Menno D Stellingwerff
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Murtadha L Al-Saady
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Adam Dvorak
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Shannon Kolind
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Stefan D Roosendaal
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Nicole I Wolf
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Amsterdam, Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Marjo S van der Knaap
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Petra J W Pouwels
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Amsterdam, Netherlands
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Salameh N, Weingärtner S, Hilbert T, Vilgrain V, Robson MD, Marques JP. Quantitative imaging through the production chain: from idea to application. MAGMA 2023; 36:851-855. [PMID: 37950797 DOI: 10.1007/s10334-023-01131-w] [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] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/13/2023]
Affiliation(s)
- Najat Salameh
- Center for Adaptable MRI Technology (AMT Center), Institute of Medical Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
- HollandPTC, Delft, The Netherlands
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Valérie Vilgrain
- Department of Radiology, Hôpital Beaujon, APHP.Nord, Université Paris Cité, Paris, France
| | | | - José P Marques
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN, Nijmegen, The Netherlands.
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Li H, Jacob MA, Cai M, Duering M, Chamberland M, Norris DG, Kessels RPC, de Leeuw FE, Marques JP, Tuladhar AM. Regional cortical thinning, demyelination and iron loss in cerebral small vessel disease. Brain 2023; 146:4659-4673. [PMID: 37366338 PMCID: PMC10629800 DOI: 10.1093/brain/awad220] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/07/2023] [Accepted: 06/11/2023] [Indexed: 06/28/2023] Open
Abstract
The link between white matter hyperintensities (WMH) and cortical thinning is thought to be an important pathway by which WMH contributes to cognitive deficits in cerebral small vessel disease (SVD). However, the mechanism behind this association and the underlying tissue composition abnormalities are unclear. The objective of this study is to determine the association between WMH and cortical thickness, and the in vivo tissue composition abnormalities in the WMH-connected cortical regions. In this cross-sectional study, we included 213 participants with SVD who underwent standardized protocol including multimodal neuroimaging scans and cognitive assessment (i.e. processing speed, executive function and memory). We identified the cortex connected to WMH using probabilistic tractography starting from the WMH and defined the WMH-connected regions at three connectivity levels (low, medium and high connectivity level). We calculated the cortical thickness, myelin and iron of the cortex based on T1-weighted, quantitative R1, R2* and susceptibility maps. We used diffusion-weighted imaging to estimate the mean diffusivity of the connecting white matter tracts. We found that cortical thickness, R1, R2* and susceptibility values in the WMH-connected regions were significantly lower than in the WMH-unconnected regions (all Pcorrected < 0.001). Linear regression analyses showed that higher mean diffusivity of the connecting white matter tracts were related to lower thickness (β = -0.30, Pcorrected < 0.001), lower R1 (β = -0.26, Pcorrected = 0.001), lower R2* (β = -0.32, Pcorrected < 0.001) and lower susceptibility values (β = -0.39, Pcorrected < 0.001) of WMH-connected cortical regions at high connectivity level. In addition, lower scores on processing speed were significantly related to lower cortical thickness (β = 0.20, Pcorrected = 0.030), lower R1 values (β = 0.20, Pcorrected = 0.006), lower R2* values (β = 0.29, Pcorrected = 0.006) and lower susceptibility values (β = 0.19, Pcorrected = 0.024) of the WMH-connected regions at high connectivity level, independent of WMH volumes and the cortical measures of WMH-unconnected regions. Together, our study demonstrated that the microstructural integrity of white matter tracts passing through WMH is related to the regional cortical abnormalities as measured by thickness, R1, R2* and susceptibility values in the connected cortical regions. These findings are indicative of cortical thinning, demyelination and iron loss in the cortex, which is most likely through the disruption of the connecting white matter tracts and may contribute to processing speed impairment in SVD, a key clinical feature of SVD. These findings may have implications for finding intervention targets for the treatment of cognitive impairment in SVD by preventing secondary degeneration.
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Affiliation(s)
- Hao Li
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
| | - Mina A Jacob
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
| | - Mengfei Cai
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, 510080 Guangzhou, China
| | - Marco Duering
- Medical Image Analysis Center (MIAC AG) and Department of Biomedical Engineering, University of Basel, 4051 Basel, Switzerland
- LMU Munich, University Hospital, Institute for Stroke and Dementia Research (ISD), 81377 Munich, Germany
| | - Maxime Chamberland
- Donders Institute for Brain, Cognition and Behaviour, Center for Cognitive Neuroimaging, Radboud University, 6525 EN Nijmegen, The Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behaviour, Center for Cognitive Neuroimaging, Radboud University, 6525 EN Nijmegen, The Netherlands
| | - Roy P C Kessels
- Department of Medical Psychology and Radboudumc Alzheimer Center, Radboud University Medical Center, 6525 GC, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN Nijmegen, The Netherlands
- Vincent van Gogh Institute for Psychiatry, 5803 AC Venray, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Center for Cognitive Neuroimaging, Radboud University, 6525 EN Nijmegen, The Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands
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Campbell-Washburn AE, Keenan KE, Hu P, Mugler JP, Nayak KS, Webb AG, Obungoloch J, Sheth KN, Hennig J, Rosen MS, Salameh N, Sodickson DK, Stein JM, Marques JP, Simonetti OP. Low-field MRI: A report on the 2022 ISMRM workshop. Magn Reson Med 2023; 90:1682-1694. [PMID: 37345725 PMCID: PMC10683532 DOI: 10.1002/mrm.29743] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/21/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023]
Abstract
In March 2022, the first ISMRM Workshop on Low-Field MRI was held virtually. The goals of this workshop were to discuss recent low field MRI technology including hardware and software developments, novel methodology, new contrast mechanisms, as well as the clinical translation and dissemination of these systems. The virtual Workshop was attended by 368 registrants from 24 countries, and included 34 invited talks, 100 abstract presentations, 2 panel discussions, and 2 live scanner demonstrations. Here, we report on the scientific content of the Workshop and identify the key themes that emerged. The subject matter of the Workshop reflected the ongoing developments of low-field MRI as an accessible imaging modality that may expand the usage of MRI through cost reduction, portability, and ease of installation. Many talks in this Workshop addressed the use of computational power, efficient acquisitions, and contemporary hardware to overcome the SNR limitations associated with low field strength. Participants discussed the selection of appropriate clinical applications that leverage the unique capabilities of low-field MRI within traditional radiology practices, other point-of-care settings, and the broader community. The notion of "image quality" versus "information content" was also discussed, as images from low-field portable systems that are purpose-built for clinical decision-making may not replicate the current standard of clinical imaging. Speakers also described technical challenges and infrastructure challenges related to portability and widespread dissemination, and speculated about future directions for the field to improve the technology and establish clinical value.
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Affiliation(s)
- Adrienne E Campbell-Washburn
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Kathryn E Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Peng Hu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - John P Mugler
- Department of Radiology & Medical Imaging, Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Andrew G Webb
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | | | - Kevin N Sheth
- Division of Neurocritical Care and Emergency Neurology, Departments of Neurology and Neurosurgery, and the Yale Center for Brain and Mind Health, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jürgen Hennig
- Dept.of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany
- Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthew S Rosen
- Massachusetts General Hospital, A. A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
- Department of Physics, Harvard University, Cambridge, Massachusetts, USA
| | - Najat Salameh
- Center for Adaptable MRI Technology (AMT Center), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Daniel K Sodickson
- Department of Radiology, NYU Langone Health, New York, New York, USA
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York, New York, USA
| | - Joel M Stein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Orlando P Simonetti
- Division of Cardiovascular Medicine, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, Ohio, USA
- Department of Radiology, The Ohio State University, Columbus, Ohio, USA
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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson S, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. ArXiv 2023:arXiv:2307.02306v1. [PMID: 37461418 PMCID: PMC10350101] [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] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, MD, United States
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, NY, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, NY, United States
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, NY, United States
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Dietrich O, Cai M, Tuladhar AM, Jacob MA, Drenthen GS, Jansen JFA, Marques JP, Topalis J, Ingrisch M, Ricke J, de Leeuw FE, Duering M, Backes WH. Integrated intravoxel incoherent motion tensor and diffusion tensor brain MRI in a single fast acquisition. NMR Biomed 2023; 36:e4905. [PMID: 36637237 DOI: 10.1002/nbm.4905] [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] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 12/21/2022] [Accepted: 01/11/2023] [Indexed: 06/15/2023]
Abstract
The acquisition of intravoxel incoherent motion (IVIM) data and diffusion tensor imaging (DTI) data from the brain can be integrated into a single measurement, which offers the possibility to determine orientation-dependent (tensorial) perfusion parameters in addition to established IVIM and DTI parameters. The purpose of this study was to evaluate the feasibility of such a protocol with a clinically feasible scan time below 6 min and to use a model-selection approach to find a set of DTI and IVIM tensor parameters that most adequately describes the acquired data. Diffusion-weighted images of the brain were acquired at 3 T in 20 elderly participants with cerebral small vessel disease using a multiband echoplanar imaging sequence with 15 b-values between 0 and 1000 s/mm2 and six non-collinear diffusion gradient directions for each b-value. Seven different IVIM-diffusion models with 4 to 14 parameters were implemented, which modeled diffusion and pseudo-diffusion as scalar or tensor quantities. The models were compared with respect to their fitting performance based on the goodness of fit (sum of squared fit residuals, chi2 ) and their Akaike weights (calculated from the corrected Akaike information criterion). Lowest chi2 values were found using the model with the largest number of model parameters. However, significantly highest Akaike weights indicating the most appropriate models for the acquired data were found with a nine-parameter IVIM-DTI model (with isotropic perfusion modeling) in normal-appearing white matter (NAWM), and with an 11-parameter model (IVIM-DTI with additional pseudo-diffusion anisotropy) in white matter with hyperintensities (WMH) and in gray matter (GM). The latter model allowed for the additional calculation of the fractional anisotropy of the pseudo-diffusion tensor (with a median value of 0.45 in NAWM, 0.23 in WMH, and 0.36 in GM), which is not accessible with the usually performed IVIM acquisitions based on three orthogonal diffusion-gradient directions.
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Affiliation(s)
- Olaf Dietrich
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Mengfei Cai
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil Man Tuladhar
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mina A Jacob
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Gerald S Drenthen
- Schools for Mental Health and Neuroscience (MHeNs) and Cardiovascular Diseases (CARIM), Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Jacobus F A Jansen
- Schools for Mental Health and Neuroscience (MHeNs) and Cardiovascular Diseases (CARIM), Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - José P Marques
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Johanna Topalis
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marco Duering
- Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Walter H Backes
- Schools for Mental Health and Neuroscience (MHeNs) and Cardiovascular Diseases (CARIM), Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
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Li H, Cai M, Jacob MA, Norris DG, Marques JP, Chamberland M, Duering M, Kessels RPC, de Leeuw FE, Tuladhar AM. Dissociable Contributions of Thalamic-Subregions to Cognitive Impairment in Small Vessel Disease. Stroke 2023; 54:1367-1376. [PMID: 36912138 PMCID: PMC10121245 DOI: 10.1161/strokeaha.122.041687] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Abstract
BACKGROUND Structural network damage is a potentially important mechanism by which cerebral small vessel disease (SVD) can cause cognitive impairment. As a central hub of the structural network, the role of thalamus in SVD-related cognitive impairments remains unclear. We aimed to determine the associations between the structural alterations of thalamic subregions and cognitive impairments in SVD. METHODS In this cross-sectional study, 205 SVD participants without thalamic lacunes from the third follow-up (2020) of the prospective RUN DMC study (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort), which was initiated in 2006, Nijmegen, were included. Cognitive functions included processing speed, executive function, and memory. Probabilistic tractography was performed from thalamus to 6 cortical regions, followed by connectivity-based thalamic segmentation to assess each thalamic subregion volume and connectivity (measured by mean diffusivity [MD] of the connecting white matter tracts) with the cortex. Least absolute shrinkage and selection operator regression analysis was conducted to identify the volumes or connectivity of the total thalamus and 6 thalamic subregions that have the strongest association with cognitive performance. Linear regression and mediation analyses were performed to test the association of least absolute shrinkage and selection operator-selected thalamic subregion volume or MD with cognitive performance, while adjusting for age and education. RESULTS We found that higher MD of the thalamic-motor tract was associated with worse processing speed (β=-0.27; P<0.001), higher MD of the thalamic-frontal tract was associated with worse executive function (β=-0.24; P=0.001), and memory (β=-0.28; P<0.001), respectively. The mediation analysis showed that MD of thalamocortical tracts mediated the association between corresponding thalamic subregion volumes and the cognitive performances in 3 domains. CONCLUSIONS Our results suggest that the structural alterations of thalamus are linked to cognitive impairment in SVD, largely depending on the damage pattern of the white matter tracts connecting specific thalamic subregions and cortical regions.
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Affiliation(s)
- Hao Li
- Radboud University Medical Center, Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands. (H.L., M.C., M.A.., F.-E.d.L., A.M.T.)
| | - Mengfei Cai
- Radboud University Medical Center, Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands. (H.L., M.C., M.A.., F.-E.d.L., A.M.T.)
| | - Mina A Jacob
- Radboud University Medical Center, Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands. (H.L., M.C., M.A.., F.-E.d.L., A.M.T.)
| | - David G Norris
- Centre for Cognitive Neuroimaging, Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands. (D.G.N., J.P.M., M.C.)
| | - José P Marques
- Centre for Cognitive Neuroimaging, Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands. (D.G.N., J.P.M., M.C.)
| | - Maxime Chamberland
- Centre for Cognitive Neuroimaging, Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands. (D.G.N., J.P.M., M.C.).,Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China (M.C.)
| | - Marco Duering
- Medical Image Analysis Center (MIAC AG) and Department of Biomedical Engineering, University of Basel, Switzerland (M.D.).,Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany (M.D.)
| | - Roy P C Kessels
- Radboud University Medical Center, Department of Medical Psychology and Radboudumc Alzheimer Center, Nijmegen, the Netherlands (R.P.C.K.).,Radboud University, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands (R.P.C.K.).,Vincent van Gogh Institute for Psychiatry, Venray, the Netherlands (R.P.C.K.)
| | - Frank-Erik de Leeuw
- Radboud University Medical Center, Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands. (H.L., M.C., M.A.., F.-E.d.L., A.M.T.)
| | - Anil M Tuladhar
- Radboud University Medical Center, Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands. (H.L., M.C., M.A.., F.-E.d.L., A.M.T.)
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9
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Pinheiro D, Fernandes A, Godinho C, Machado J, Baptista G, Grilo F, Sustelo L, Sampaio JM, Amaro P, Leitão RG, Marques JP, Parente F, Indelicato P, de Avillez M, Santos JP, Guerra M. K- and L-shell theoretical fluorescence yields for the Fe isonuclear sequence. Radiat Phys Chem Oxf Engl 1993 2023. [DOI: 10.1016/j.radphyschem.2022.110594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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10
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Chan KS, Chamberland M, Marques JP. On the performance of multi-compartment relaxometry for myelin water imaging (MCR-MWI) - test-retest repeatability and inter-protocol reproducibility. Neuroimage 2023; 266:119824. [PMID: 36539169 DOI: 10.1016/j.neuroimage.2022.119824] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022] Open
Abstract
In this study, we optimized the variable flip angle (VFA) acquisition scheme using numerical simulations to shorten the acquisition time of multicompartment relaxometry for myelin water imaging (MCR-MWI) to a clinically practical range in the absence of advanced image reconstruction methods. As the primary objective of this study, the test-retest repeatability of myelin water fraction (MWF) measurements of MCR-MWI is evaluated on three gradient echo (GRE) sequence settings using the optimized VFA schemes with different echo times and repetition times, emulating various scanner setups. The cross-protocol reproducibility of MCR-MWI and MCR with diffusion-informed myelin water imaging (MCR-DIMWI) is also examined. As a secondary objective, we explore the bundle-specific profiles of various microstructural parameters from MCR-(DI)MWI and their cross-correlations to determine if these parameters possess supplementary microstructure information beyond myelin concentration. Numerical simulations indicate that MCR-MWI can be performed with a minimum of three flip angles covering a wide range of T1 weightings without adding significant bias. This is supported by the results of an in vivo experiment, allowing whole-brain 1.5 mm isotropic MWF maps to be acquired in 9 min, reducing the total scan time to 40% of the original implementation without significant quality degradation. Good test-retest repeatability is observed for MCR-MWI for all three GRE protocols. While good correlations can also be found in MWF across protocols, systematic differences are observed. Bundle-specific MWF analysis reveals that certain white matter bundles are similar in all participants. We also found that microstructure relaxation parameters have low linear correlations with MWF. MCR-MWI is a reproducible measure of myelin. However, attention should be paid to the protocol related MWF differences when comparing different studies, as the MWF bias up to 0.5% can be observed across the protocols examined in this work.
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Affiliation(s)
- Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN, Nijmegen, the Netherlands
| | - Maxime Chamberland
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN, Nijmegen, the Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN, Nijmegen, the Netherlands.
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11
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Correia J, Daghfous G, Silva D, Graça G, Beltran I, Reis J, Marques JP, Silva L, Guedes R, Morato T. (Very) long-term transport of Silurus glanis, Carcharhinus melanopterus, Scomber colias, Trachurus picturatus, Polyprion americanus, Rhinoptera marmoratus, Salmo salar, Scomber scombrus, Sardina pilchardus, and others, by land, water and air. Zoo Biol 2022; 41:560-575. [PMID: 35137968 DOI: 10.1002/zoo.21684] [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: 03/02/2021] [Revised: 12/27/2021] [Accepted: 01/18/2022] [Indexed: 12/16/2022]
Abstract
In this paper, we cover 4 years of live fish transports that ranged from 14 to 200 h (8 days), and bioloads from 3.8 to 76.9 kg/m3 . The key ingredients for success in all trips, where virtually no mortality occurred, was atributed to (1) pre-buffering the water with sodium bicarbonate and sodium carbonate at 50 g/m3 (each)-and/or ATM Alka-HaulTM at 25 g/m3 -and applying additional (partial or full) doses throughout each transport, whenever the tanks were accessible; (2) pre-quenching ammonia with ATM TriageTM at 32 g/m3 , and applying additional (partial or full) doses throughout each transport, whenever the tanks were accessible; (3) keeping the dissolved oxygen saturation rate above 100%, ideally above 150%; (4) Keeping temperature on the lower limit of each species' tolerance range; (5) Using foam fractionators to effectively eliminate organic matter from the water and (6) Using pure sine wave inverters, which allows for a steady supply of electrical current throughout the transport. The use of a 'preventive' versus 'corrective' pH buffering philosophy is also discussed.
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Affiliation(s)
- João Correia
- Flying Sharks, Lda., Horta, Portugal.,MARE-Marine and Environmental Sciences Centre, ESTM, Politécnico de Leiria, Peniche, Portugal
| | | | | | | | | | - João Reis
- Flying Sharks, Lda., Horta, Portugal
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12
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Dewenter A, Gesierich B, Ter Telgte A, Wiegertjes K, Cai M, Jacob MA, Marques JP, Norris DG, Franzmeier N, de Leeuw FE, Tuladhar AM, Duering M. Systematic validation of structural brain networks in cerebral small vessel disease. J Cereb Blood Flow Metab 2022; 42:1020-1032. [PMID: 34929104 PMCID: PMC9125482 DOI: 10.1177/0271678x211069228] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Cerebral small vessel disease (SVD) is considered a disconnection syndrome, which can be quantified using structural brain network analysis obtained from diffusion MRI. Network analysis is a demanding analysis approach and the added benefit over simpler diffusion MRI analysis is largely unexplored in SVD. In this pre-registered study, we assessed the clinical and technical validity of network analysis in two non-overlapping samples of SVD patients from the RUN DMC study (n = 52 for exploration and longitudinal analysis and n = 105 for validation). We compared two connectome pipelines utilizing single-shell or multi-shell diffusion MRI, while also systematically comparing different node and edge definitions. For clinical validation, we assessed the added benefit of network analysis in explaining processing speed and in detecting short-term disease progression. For technical validation, we determined test-retest repeatability.Our findings in clinical validation show that structural brain networks provide only a small added benefit over simpler global white matter diffusion metrics and do not capture short-term disease progression. Test-retest reliability was excellent for most brain networks. Our findings question the added value of brain network analysis in clinical applications in SVD and highlight the utility of simpler diffusion MRI based markers.
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Affiliation(s)
- Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Annemieke Ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.,VASCage - Research Centre on Vascular Ageing and Stroke, Innsbruck, Austria
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mengfei Cai
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mina A Jacob
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.,Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.,Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
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13
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Maltseva AL, Lobov AA, Pavlova PA, Panova M, Gafarova ER, Marques JP, Danilov LG, Granovitch AI. Orphan gene in Littorina: An unexpected role of symbionts in the host evolution. Gene 2022; 824:146389. [PMID: 35257790 DOI: 10.1016/j.gene.2022.146389] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [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: 10/07/2021] [Revised: 01/29/2022] [Accepted: 02/28/2022] [Indexed: 11/16/2022]
Abstract
Mechanisms of reproductive isolation between closely related sympatric species are of high evolutionary significance as they may function as initial drivers of speciation and protect species integrity afterwards. Proteins involved in the establishment of reproductive barriers often evolve fast and may be key players in cessation of gene flow between the incipient species. The five Atlantic Littorina (Neritrema) species represent a notable example of recent radiation. The geographic ranges of these young species largely overlap and the mechanisms of reproductive isolation are poorly understood. In this study, we performed a detailed analysis of the reproductive protein LOSP, previously identified in Littorina. We showed that this protein is evolutionary young and taxonomically restricted to the genus Littorina. It has high sequence variation both within and between Littorina species, which is compatible with its presumable role in the reproductive isolation. The strongest differences in the LOSP structure were detected between Littorina subgenera with distinctive repetitive motifs present exclusively in the Neritrema species, but not in L. littorea. Moreover, the sequence of these repetitive structural elements demonstrates a high homology with genetic elements of bacteria, identified as components of Littorina associated microbiomes. We suggest that these elements were acquired from a symbiotic bacterial donor via horizontal genetic transfer (HGT), which is indirectly confirmed by the presence of multiple transposable elements in the LOSP flanking and intronic regions. Furthermore, we hypothesize that this HGT-driven evolutionary innovation promoted LOSP function in reproductive isolation, which might be one of the factors determining the intensive cladogenesis in the Littorina (Neritrema) lineage in contrast to the anagenesis in the L. littorea clade.
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Affiliation(s)
- A L Maltseva
- Department of Invertebrate Zoology, St Petersburg State University, St Petersburg, Russia.
| | - A A Lobov
- Department of Invertebrate Zoology, St Petersburg State University, St Petersburg, Russia; Laboratory of Regenerative Biomedicine, Institute of Cytology Russian Academy of Sciences, St Petersburg, Russia
| | - P A Pavlova
- Department of Invertebrate Zoology, St Petersburg State University, St Petersburg, Russia
| | - M Panova
- Department of Invertebrate Zoology, St Petersburg State University, St Petersburg, Russia; Department of Marine Sciences - Tjärnö, University of Gothenburg, Sweden
| | - E R Gafarova
- Department of Invertebrate Zoology, St Petersburg State University, St Petersburg, Russia
| | - J P Marques
- CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Universidade do Porto, Vairão, Portugal; Departamento de Biologia, Faculdade de Ciências do Porto, 4169-007 Porto, Portugal; ISEM, Univ Montpellier, CNRS, EPHE, IRD, 34095 Montpellier, France
| | - L G Danilov
- Department of Genetics and Biotechnology, St. Petersburg State University, St. Petersburg, Russia
| | - A I Granovitch
- Department of Invertebrate Zoology, St Petersburg State University, St Petersburg, Russia
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14
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Janssen E, ter Telgte A, Verburgt E, de Jong JJA, Marques JP, Kessels RPC, Backes WH, Maas MC, Meijer FJA, Deinum J, Riksen NP, Tuladhar AM, de Leeuw FE. The Hyperintense study: Assessing the effects of induced blood pressure increase and decrease on MRI markers of cerebral small vessel disease: Study rationale and protocol. Eur Stroke J 2022; 7:331-338. [PMID: 36082259 PMCID: PMC9446329 DOI: 10.1177/23969873221100331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 03/22/2022] [Accepted: 04/24/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Neuroimaging markers of cerebral small vessel disease (SVD) are common in
older individuals, but the pathophysiological mechanisms causing these
lesions remain poorly understood. Although hypertension is a major risk
factor for SVD, the direct causal effects of increased blood pressure are
unknown. The Hyperintense study is designed to examine cerebrovascular and
structural abnormalities, possibly preceding SVD, in young adults with
hypertension. These patients undergo a diagnostic work-up that requires
patients to temporarily discontinue their antihypertensive agents, often
leading to an increase in blood pressure followed by a decrease once
effective medication is restarted. This allows examination of the effects of
blood pressure increase and decrease on the cerebral small vessels. Methods: Hyperintense is a prospective observational cohort study in 50 hypertensive
adults (18–55 years) who will temporarily discontinue antihypertensive
medication for diagnostic purposes. MRI and clinical data is collected at
four timepoints: before medication withdrawal (baseline), once
antihypertensives are largely or completely withdrawn
(T = 1), when patients have restarted medication
(T = 2) and reached target blood pressure and 1 year
later (T = 3). The 3T MRI protocol includes conventional
structural sequences and advanced techniques to assess various aspects of
microvascular integrity, including blood-brain barrier function using
Dynamic Contrast Enhanced MRI, white matter integrity, and microperfusion.
Clinical assessments include motor and cognitive examinations and blood
sampling. Discussion: The Hyperintense study will improve the understanding of the
pathophysiological mechanisms following hypertension that may cause SVD.
This knowledge can ultimately help to identify new targets for treatment of
SVD, aimed at prevention or limiting disease progression.
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Affiliation(s)
- Esther Janssen
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | | | - Esmée Verburgt
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Joost JA de Jong
- School for Mental Health & Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Roy PC Kessels
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
- Vincent van Gogh Institute for Psychiatry, Venray, The Netherlands
- Department of Medical Psychology and Radboudumc Alzheimer Center, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Walter H Backes
- School for Mental Health & Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Marnix C Maas
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Frederick JA Meijer
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jaap Deinum
- Department of Internal Medicine and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Niels P Riksen
- Department of Internal Medicine and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
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15
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Aarts E, Akkerman A, Altgassen M, Bartels R, Beckers D, Bevelander K, Bijleveld E, Davidson EB, Boleij A, Bralten J, Cillessen T, Claassen J, Cools R, Cornelissen I, Dresler M, Eijsvogels T, Faber M, Fernández G, Figner B, Fritsche M, Füllbrunn S, Gayet S, van Gelder MMHJ, Gerven MV, Geurts S, Greven CU, Groefsema M, Haak K, Hagoort P, Hartman Y, van der Heijden B, Hermans E, Heuvelmans V, Hintz F, Hollander JD, Hulsman AM, Idesis S, Jaeger M, Janse E, Janzing J, Kessels RPC, Karremans JC, Kleijn WD, Klein M, Klumpers F, Kohn N, Korzilius H, Krahmer B, Lange FD, Leeuwen JV, Liu H, Luijten M, Manders P, Manevska K, Marques JP, Matthews J, McQueen JM, Medendorp P, Melis R, Meyer A, Oosterman J, Overbeek L, Peelen M, Popma J, Postma G, Roelofs K, van Rossenberg YGT, Schaap G, Scheepers P, Selen L, Starren M, Swinkels DW, Tendolkar I, Thijssen D, Timmerman H, Tutunji R, Tuladhar A, Veling H, Verhagen M, Verkroost J, Vink J, Vriezekolk V, Vrijsen J, Vyrastekova J, Wal SVD, Willems R, Willemsen A. Correction: Protocol of the Healthy Brain Study: An accessible resource for understanding the human brain and how it dynamically and individually operates in its bio-social context. PLoS One 2022; 17:e0267071. [PMID: 35404975 PMCID: PMC9000123 DOI: 10.1371/journal.pone.0267071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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16
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Chan KS, Hédouin R, Mollink J, Schulz J, van Cappellen van Walsum AM, Marques JP. Imaging white matter microstructure with gradient-echo phase imaging: Is ex vivo imaging with formalin-fixed tissue a good approximation of the in vivo brain? Magn Reson Med 2022; 88:380-390. [PMID: 35344591 PMCID: PMC9314807 DOI: 10.1002/mrm.29213] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 01/17/2022] [Accepted: 02/10/2022] [Indexed: 11/20/2022]
Abstract
Purpose Ex vivo imaging is a commonly used approach to investigate the biophysical mechanism of orientation‐dependent signal phase evolution in white matter. Yet, how phase measurements are influenced by the structural alteration in the tissue after formalin fixation is not fully understood. Here, we study the effects on magnetic susceptibility, microstructural compartmentalization, and chemical exchange measurement with a postmortem formalin‐fixed whole‐brain human tissue. Methods A formalin‐fixed, postmortem human brain specimen was scanned with multiple orientations to the main magnetic field direction for robust bulk magnetic susceptibility measurement with conventional quantitative susceptibility imaging models. White matter samples were subsequently excised from the whole‐brain specimen and scanned in multiple rotations on an MRI scanner to measure the anisotropic magnetic susceptibility and microstructure‐related contributions in the signal phase and to validate the findings of the whole‐brain data. Results The bulk isotropic magnetic susceptibility of ex vivo whole‐brain imaging is comparable to in vivo imaging, with noticeable enhanced nonsusceptibility contributions. The excised specimen experiment reveals that anisotropic magnetic susceptibility and compartmentalization phase effect were considerably reduced in the formalin‐fixed white matter specimens. Conclusions Formalin‐fixed postmortem white matter exhibits comparable isotropic magnetic susceptibility to previous in vivo imaging findings. However, the measured phase and magnitude data of the fixed white matter tissue shows a significantly weaker orientation dependency and compartmentalization effect. Alternatives to formalin fixation are needed to better reproduce the in vivo microstructural effects in postmortem samples.
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Affiliation(s)
- Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Renaud Hédouin
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands.,Empenn, INRIA, INSERM, CNRS, Université de Rennes 1, Rennes, France
| | - Jeroen Mollink
- Department of Medical Imaging, Anatomy, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jenni Schulz
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Anne-Marie van Cappellen van Walsum
- Department of Medical Imaging, Anatomy, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
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17
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Aarts E, Akkerman A, Altgassen M, Bartels R, Beckers D, Bevelander K, Bijleveld E, Blaney Davidson E, Boleij A, Bralten J, Cillessen T, Claassen J, Cools R, Cornelissen I, Dresler M, Eijsvogels T, Faber M, Fernández G, Figner B, Fritsche M, Füllbrunn S, Gayet S, van Gelder MMHJ, van Gerven M, Geurts S, Greven CU, Groefsema M, Haak K, Hagoort P, Hartman Y, van der Heijden B, Hermans E, Heuvelmans V, Hintz F, den Hollander J, Hulsman AM, Idesis S, Jaeger M, Janse E, Janzing J, Kessels RPC, Karremans JC, de Kleijn W, Klein M, Klumpers F, Kohn N, Korzilius H, Krahmer B, de Lange F, van Leeuwen J, Liu H, Luijten M, Manders P, Manevska K, Marques JP, Matthews J, McQueen JM, Medendorp P, Melis R, Meyer A, Oosterman J, Overbeek L, Peelen M, Popma J, Postma G, Roelofs K, van Rossenberg YGT, Schaap G, Scheepers P, Selen L, Starren M, Swinkels DW, Tendolkar I, Thijssen D, Timmerman H, Tutunji R, Tuladhar A, Veling H, Verhagen M, Verkroost J, Vink J, Vriezekolk V, Vrijsen J, Vyrastekova J, van der Wal S, Willems R, Willemsen A. Protocol of the Healthy Brain Study: An accessible resource for understanding the human brain and how it dynamically and individually operates in its bio-social context. PLoS One 2021; 16:e0260952. [PMID: 34965252 PMCID: PMC8716054 DOI: 10.1371/journal.pone.0260952] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 11/20/2021] [Indexed: 12/29/2022] Open
Abstract
The endeavor to understand the human brain has seen more progress in the last few decades than in the previous two millennia. Still, our understanding of how the human brain relates to behavior in the real world and how this link is modulated by biological, social, and environmental factors is limited. To address this, we designed the Healthy Brain Study (HBS), an interdisciplinary, longitudinal, cohort study based on multidimensional, dynamic assessments in both the laboratory and the real world. Here, we describe the rationale and design of the currently ongoing HBS. The HBS is examining a population-based sample of 1,000 healthy participants (age 30–39) who are thoroughly studied across an entire year. Data are collected through cognitive, affective, behavioral, and physiological testing, neuroimaging, bio-sampling, questionnaires, ecological momentary assessment, and real-world assessments using wearable devices. These data will become an accessible resource for the scientific community enabling the next step in understanding the human brain and how it dynamically and individually operates in its bio-social context. An access procedure to the collected data and bio-samples is in place and published on https://www.healthybrainstudy.nl/en/data-and-methods/access. Trail registration:https://www.trialregister.nl/trial/7955.
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Affiliation(s)
- Healthy Brain Study consortium
- Radboud University, Nijmegen, The Netherlands
- Radboud University Medical Center, Nijmegen, The Netherlands
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Esther Aarts
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Agnes Akkerman
- Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | | | - Ronald Bartels
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Debby Beckers
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | | | - Erik Bijleveld
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | | | | | - Janita Bralten
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Toon Cillessen
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Jurgen Claassen
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Roshan Cools
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Martin Dresler
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Myrthe Faber
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Guillén Fernández
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
- * E-mail:
| | - Bernd Figner
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Matthias Fritsche
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Sascha Füllbrunn
- Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | - Surya Gayet
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | | | - Marcel van Gerven
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Sabine Geurts
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Corina U. Greven
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martine Groefsema
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Koen Haak
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Peter Hagoort
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Yvonne Hartman
- Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Erno Hermans
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Florian Hintz
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | | | - Anneloes M. Hulsman
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Sebastian Idesis
- Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain
| | - Martin Jaeger
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Esther Janse
- Centre for Language Studies, Radboud University, Nijmegen, The Netherlands
| | - Joost Janzing
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Roy P. C. Kessels
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Johan C. Karremans
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Willemien de Kleijn
- School of Psychology and Artificial Intelligence, Radboud University, Nijmegen, The Netherlands
| | - Marieke Klein
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Floris Klumpers
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Nils Kohn
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hubert Korzilius
- Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | - Bas Krahmer
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Floris de Lange
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Judith van Leeuwen
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Huaiyu Liu
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Maartje Luijten
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Peggy Manders
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Katerina Manevska
- Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | - José P. Marques
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jon Matthews
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - James M. McQueen
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Pieter Medendorp
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - René Melis
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Antje Meyer
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Joukje Oosterman
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Lucy Overbeek
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marius Peelen
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jean Popma
- Interdisciplinary Hub for Security, Privacy and Data Governance, Radboud University, Nijmegen, The Netherlands
| | - Geert Postma
- Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
| | - Karin Roelofs
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | | | - Gabi Schaap
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Paul Scheepers
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Luc Selen
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Marianne Starren
- Centre for Language Studies, Radboud University, Nijmegen, The Netherlands
| | | | - Indira Tendolkar
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dick Thijssen
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hans Timmerman
- University Medical Center Groningen, Groningen, The Netherlands
| | - Rayyan Tutunji
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil Tuladhar
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Harm Veling
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Maaike Verhagen
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | | | - Jacqueline Vink
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | | | - Janna Vrijsen
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jana Vyrastekova
- Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | | | - Roel Willems
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Centre for Language Studies, Radboud University, Nijmegen, The Netherlands
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18
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Wiegertjes K, Chan KS, Telgte AT, Gesierich B, Norris DG, Klijn CJ, Duering M, Tuladhar AM, Marques JP, Leeuw FED. Assessing cortical cerebral microinfarcts on iron-sensitive MRI in cerebral small vessel disease. J Cereb Blood Flow Metab 2021; 41:3391-3399. [PMID: 34415209 PMCID: PMC8669205 DOI: 10.1177/0271678x211039609] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Recent studies suggest that a subset of cortical microinfarcts may be identifiable on T2* but invisible on T1 and T2 follow-up images. We aimed to investigate whether cortical microinfarcts are associated with iron accumulation after the acute stage. The RUN DMC - InTENse study is a serial MRI study including individuals with cerebral small vessel disease (SVD). 54 Participants underwent 10 monthly 3 T MRIs, including diffusion-weighted imaging, quantitative R1 (=1/T1), R2 (=1/T2), and R2* (=1/T2*) mapping, from which MRI parameters within areas corresponding to microinfarcts and control region of interests (ROIs) were retrieved within 16 participants. Finally, we compared pre- and post-lesional values with repeated measures ANOVA and post-hoc paired t-tests using the mean difference between lesion and control ROI values. We observed 21 acute cortical microinfarcts in 7 of the 54 participants (median age 69 years [IQR 66-74], 63% male). R2* maps demonstrated an increase in R2* values at the moment of the last available follow-up MRI (median [IQR], 5 [5-14] weeks after infarction) relative to prelesional values (p = .08), indicative of iron accumulation. Our data suggest that cortical microinfarcts are associated with increased R2* values, indicative of iron accumulation, possibly due to microhemorrhages, neuroinflammation or neurodegeneration, awaiting histopathological verification.
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Affiliation(s)
- Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
| | - Annemieke Ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital LMU Munich, Munich, Germany
| | - David G Norris
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands.,Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany.,MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Catharina Jm Klijn
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital LMU Munich, Munich, Germany.,Medical Image Analysis Center (MIAC AG), Basel and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
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19
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Abreu R, Figueiredo P, Beckert P, Marques JP, Amorim S, Caetano C, Carvalho P, Sá C, Cotovio R, Cruz J, Dias T, Fernandes G, Gonçalves E, Leão C, Leitão A, Lopes J, Machado E, Neves M, Oliveira A, Pereira AI, Pereira B, Ribeiro F, Silva LM, Sousa F, Tinoco T, Teixeira VH, Sousa M, Brito J. Portuguese Football Federation consensus statement 2020: nutrition and performance in football. BMJ Open Sport Exerc Med 2021; 7:e001082. [PMID: 34527279 PMCID: PMC8395276 DOI: 10.1136/bmjsem-2021-001082] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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] [Accepted: 07/26/2021] [Indexed: 11/04/2022] Open
Abstract
Nutrition is an undeniable part of promoting health and performance among football (soccer) players. Nevertheless, nutritional strategies adopted in elite football can vary significantly depending on culture, habit and practical constraints and might not always be supported by scientific evidence. Therefore, a group of 28 Portuguese experts on sports nutrition, sports science and sports medicine sought to discuss current practices in the elite football landscape and review the existing evidence on nutritional strategies to be applied when supporting football players. Starting from understanding football's physical and physiological demands, five different moments were identified: preparing to play, match-day, recovery after matches, between matches and during injury or rehabilitation periods. When applicable, specificities of nutritional support to young athletes and female players were also addressed. The result is a set of practical recommendations that gathered consensus among involved experts, highlighting carbohydrates periodisation, hydration and conscious use of dietary supplements.
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Affiliation(s)
- Rodrigo Abreu
- Portugal Football School, Portuguese Football Federation, Cruz Quebrada, Portugal.,Universidade do Porto Faculdade de Ciências da Nutrição e Alimentação, Porto, Portugal
| | - Pedro Figueiredo
- Portugal Football School, Portuguese Football Federation, Cruz Quebrada, Portugal.,Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, University Institute of Maia, ISMAI, Maia, Portugal
| | - Paulo Beckert
- Portugal Football School, Portuguese Football Federation, Cruz Quebrada, Portugal
| | - José P Marques
- Portugal Football School, Portuguese Football Federation, Cruz Quebrada, Portugal
| | | | | | - Pedro Carvalho
- Universidade Catolica Portuguesa Escola Superior de Biotecnologia, Porto, Portugal
| | - Carla Sá
- ISMAI, Castelo da Maia, Porto, Portugal.,Polytechnic Institute of Bragança, Braganca, Portugal
| | | | - Joana Cruz
- Portimonense Futebol SAD, Portimao, Portugal
| | - Tiago Dias
- Clube Desportivo Santa Clara, Ponta Delgada, Portugal
| | | | | | - César Leão
- Instituto Politecnico de Viana do Castelo Escola Superior de Desporto e Lazer, Melgaco, Viana do Castelo, Portugal.,FC Paços de Ferreira, Paços de Ferreira, Portugal
| | | | - João Lopes
- Sporting Clube de Portugal, SAD, Lisboa, Portugal
| | | | - Mónica Neves
- Vitória Futebol Clube, Setúbal, Portugal.,Universidade do Algarve, Faro, Portugal
| | | | | | - Bruno Pereira
- Sports Medicine Control Training Unit, Instituto Portugues do Desporto e Juventude, Lisboa, Portugal
| | - Fernando Ribeiro
- Universidade do Porto Faculdade de Ciências da Nutrição e Alimentação, Porto, Portugal.,Moreirense FC, Moreira, Portugal
| | - Luis M Silva
- Centro de Medicina Desportiva do Porto, Porto, Portugal
| | - Filipe Sousa
- Futebol Clube de Vizela, Vizela, Portugal.,Futbolniy Klub Shakhtar, Shakhtar, Ukraine
| | | | - Vitor H Teixeira
- Universidade do Porto Faculdade de Ciências da Nutrição e Alimentação, Porto, Portugal.,Futebol Clube do Porto SAD, Porto, Portugal
| | - Monica Sousa
- Nutrition and Metabolism, Universidade Nova de Lisboa Faculdade de Ciências Médicas de Lisboa, Lisboa, Portugal.,NOVA Medical School, CINTESIS, Porto, Portugal
| | - João Brito
- Portugal Football School, Portuguese Football Federation, Cruz Quebrada, Portugal
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20
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Bilgic B, Langkammer C, Marques JP, Meineke J, Milovic C, Schweser F. QSM reconstruction challenge 2.0: Design and report of results. Magn Reson Med 2021; 86:1241-1255. [PMID: 33783037 DOI: 10.1002/mrm.28754] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/25/2021] [Accepted: 02/08/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE The aim of the second quantitative susceptibility mapping (QSM) reconstruction challenge (Oct 2019, Seoul, Korea) was to test the accuracy of QSM dipole inversion algorithms in simulated brain data. METHODS A two-stage design was chosen for this challenge. The participants were provided with datasets of multi-echo gradient echo images synthesized from two realistic in silico head phantoms using an MR simulator. At the first stage, participants optimized QSM reconstructions without ground truth data available to mimic the clinical setting. At the second stage, ground truth data were provided for parameter optimization. Submissions were evaluated using eight numerical metrics and visual ratings. RESULTS A total of 98 reconstructions were submitted for stage 1 and 47 submissions for stage 2. Iterative methods had the best quantitative metric scores, followed by deep learning and direct inversion methods. Priors derived from magnitude data improved the metric scores. Algorithms based on iterative approaches and total variation (and its derivatives) produced the best overall results. The reported results and analysis pipelines have been made public to allow researchers to compare new methods to the current state of the art. CONCLUSION The synthetic data provide a consistent framework to test the accuracy and robustness of QSM algorithms in the presence of noise, calcifications and minor voxel dephasing effects. Total Variation-based algorithms produced the best results among all metrics. Future QSM challenges should assess whether this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.
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Affiliation(s)
- Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | | | - José P Marques
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands
| | | | - Carlos Milovic
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, New York, USA
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21
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Marques JP, Meineke J, Milovic C, Bilgic B, Chan K, Hedouin R, van der Zwaag W, Langkammer C, Schweser F. QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures. Magn Reson Med 2021; 86:526-542. [PMID: 33638241 PMCID: PMC8048665 DOI: 10.1002/mrm.28716] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [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: 10/06/2020] [Revised: 01/12/2021] [Accepted: 01/15/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To create a realistic in silico head phantom for the second QSM reconstruction challenge and for future evaluations of processing algorithms for QSM. METHODS We created a digital whole-head tissue property phantom by segmenting and postprocessing high-resolution (0.64 mm isotropic), multiparametric MRI data acquired at 7 T from a healthy volunteer. We simulated the steady-state magnetization at 7 T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: TR and TE, voxel size, background fields, and RF phase biases. Diffusion-weighted imaging phantom data are provided, allowing future investigations of tissue-microstructure effects in phase and QSM algorithms. RESULTS The brain part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantom's properties, including the possibility of generating phase data with nonlinear evolution over TE due to partial-volume effects or complex distributions of frequency shifts within the voxel. CONCLUSION The presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative susceptibility reconstruction algorithms.
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Affiliation(s)
- José P. Marques
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenthe Netherlands
| | | | - Carlos Milovic
- Department of Electrical EngineeringPontificia Universidad Catolica de ChileSantiagoChile
- Biomedical Imaging CenterPontificia Universidad Catolica de ChileSantiagoChile
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUnited Kingdom
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical ImagingCharlestownMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
- Harvard‐MIT Health Sciences and TechnologyMITCambridgeMassachusettsUSA
| | - Kwok‐Shing Chan
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenthe Netherlands
| | - Renaud Hedouin
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenthe Netherlands
- Centre Inria Rennes ‐ Bretagne AtlantiqueRennesFrance
| | | | | | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis CenterDepartment of NeurologyJacobs School of Medicine and Biomedical SciencesUniversity at BuffaloThe State University of New YorkBuffaloNew YorkUSA
- Center for Biomedical Imaging, Clinical and Translational Science InstituteUniversity at BuffaloThe State University of New YorkBuffaloNew YorkUSA
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22
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Hédouin R, Metere R, Chan KS, Licht C, Mollink J, van Walsum AMC, Marques JP. Decoding the microstructural properties of white matter using realistic models. Neuroimage 2021; 237:118138. [PMID: 33964461 DOI: 10.1016/j.neuroimage.2021.118138] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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: 07/26/2020] [Revised: 04/14/2021] [Accepted: 04/19/2021] [Indexed: 11/24/2022] Open
Abstract
Multi-echo gradient echo (ME-GRE) magnetic resonance signal evolution in white matter has a strong dependence on the orientation of myelinated axons with respect to the main static field. Although analytical solutions have been able to predict some of the white matter (WM) signal behaviour of the hollow cylinder model, it has been shown that realistic models of WM offer a better description of the signal behaviour observed. In this work, we present a pipeline to (i) generate realistic 2D WM models with their microstructure based on real axon morphology with adjustable fiber volume fraction (FVF) and g-ratio. We (ii) simulate their interaction with the static magnetic field to be able to simulate their MR signal. For the first time, we (iii) demonstrate that realistic 2D WM models can be used to simulate a MR signal that provides a good approximation of the signal obtained from a real 3D WM model derived from electron microscopy. We then (iv) demonstrate in silico that 2D WM models can be used to predict microstructural parameters in a robust way if ME-GRE multi-orientation data is available and the main fiber orientation in each pixel is known using DTI. A deep learning network was trained and characterized in its ability to recover the desired microstructural parameters such as FVF, g-ratio, free and bound water transverse relaxation and magnetic susceptibility. Finally, the network was trained to recover these micro-structural parameters from an ex vivo dataset acquired in 9 orientations with respect to the magnetic field and 12 echo times. We demonstrate that this is an overdetermined problem and that as few as 3 orientations can already provide comparable results for some of the decoded metrics.
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Affiliation(s)
- Renaud Hédouin
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands; Empenn, INRIA, INSERM, CNRS, Université de Rennes 1, Rennes, France.
| | - Riccardo Metere
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands
| | - Kwok-Shing Chan
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands
| | - Christian Licht
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany
| | - Jeroen Mollink
- Radboud University Medical Centre, Medical Imaging and Anatomy, Nijmegen, Netherlands
| | | | - José P Marques
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands
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Ter Telgte A, Wiegertjes K, Gesierich B, Baskaran BS, Marques JP, Kuijf HJ, Norris DG, Tuladhar AM, Duering M, de Leeuw FE. Temporal Dynamics of Cortical Microinfarcts in Cerebral Small Vessel Disease. JAMA Neurol 2021; 77:643-647. [PMID: 32065609 DOI: 10.1001/jamaneurol.2019.5106] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Importance Neuropathology studies show a high prevalence of cortical microinfarcts (CMIs) in aging individuals, especially in patients with cerebrovascular disease and dementia. However, most, are invisible on T1- and T2-weighted magnetic resonance imaging (MRI), raising the question of how to explain this mismatch. Studies on small acute infarcts, detected on diffusion-weighted imaging (DWI), suggest that infarcts are largest in their acute phase and reduce in size thereafter. Therefore, we hypothesized that a subset of the CMI that are invisible on MRI can be detected on MRI in their acute phase. However, to our knowledge, a serial imaging study investigating the temporal dynamics of acute CMI (A-CMI) is lacking. Objective To determine the prevalence of chronic CMI (C-CMI) and the cumulative incidence and temporal dynamics of A-CMI in individuals with cerebral small vessel disease (SVD). Design, Setting, Participants and Exposures The RUN DMC-Intense study is a single-center hospital-based prospective cohort study on SVD performed between March 2016 and November 2017 and comprising 10 monthly 3-T MRI scans, including high-resolution DWI, 3-dimensional T1, 3-dimensional fluid-attenuated inversion recovery, and T2. One hundred six individuals from the previous longitudinal RUN DMC study were recruited based on the presence of progression of white matter hyperintensities on MRI between 2006 and 2015 and exclusion of causes of cerebral ischemia other than SVD. Fifty-four individuals (50.9%) participated. The median total follow-up duration was 39.5 weeks (interquartile range, 37.8-40.3). Statistical data analysis was performed between May and October 2019. Main Outcomes and Measures We determined the prevalence of C-CMI using the baseline T1, fluid-attenuated inversion recovery, and T2 scans. Monthly high-resolution DWI scans (n = 472) were screened to determine the cumulative incidence of A-CMI. The temporal dynamics of A-CMI were determined based on the MRI scans collected during the first follow-up visit after A-CMI onset and the last available follow-up visit. Results The median age of the cohort at baseline MRI was 69 years (interquartile range, 66-74 years) and 34 participants (63%) were men. The prevalence of C-CMI was 35% (95% CI, 0.24-0.49). Monthly DWI detected 21 A-CMI in 7 of 54 participants, resulting in a cumulative incidence of 13% (95% CI, 0.06-0.24). All A-CMI disappeared on follow-up MRI. Conclusions and Relevance Acute CMI never evolved into chronically MRI-detectable lesions. We suggest that these A-CMI underlie part of the submillimeter C-CMI encountered on neuropathological examination and thereby provide a source for the high CMI burden on neuropathology.
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Affiliation(s)
- Annemieke Ter Telgte
- Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kim Wiegertjes
- Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital LMU Munich, Munich, Germany
| | - Brendon Sri Baskaran
- Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
| | - Anil M Tuladhar
- Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Marco Duering
- Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands.,Institute for Stroke and Dementia Research (ISD), University Hospital LMU Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Frank-Erik de Leeuw
- Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
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Markuerkiaga I, Marques JP, Gallagher TE, Norris DG. Estimation of laminar BOLD activation profiles using deconvolution with a physiological point spread function. J Neurosci Methods 2021; 353:109095. [PMID: 33549635 DOI: 10.1016/j.jneumeth.2021.109095] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 12/30/2020] [Accepted: 01/31/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND The specificity of gradient echo (GE)-BOLD laminar fMRI activation profiles is degraded by intracortical veins that drain blood from lower to upper cortical layers, propagating activation signal in the same direction. This work describes an approach to obtain layer specific profiles by deconvolving the measured profiles with a physiological Point Spread Function (PSF). NEW METHOD It is shown that the PSF can be characterised by a TE-dependent peak to tail (p2t) value that is independent of cortical depth and can be estimated by simulation. An experimental estimation of individual p2t values and the sensitivity of the deconvolved profiles to variations in p2t is obtained using laminar data measured with a multi-echo 3D-FLASH sequence. These profiles are echo time dependent, but the underlying neuronal response is the same, allowing a data-based estimation of the PSF. RESULTS The deconvolved profiles are highly similar to the gold-standard obtained from extremely high resolution 3D-EPI data, for a range of p2t values of 5-9, which covers both the empirically determined value (6.8) and the value obtained by simulation (6.3). -Comparison with Existing Method(s) Corrected profiles show a flatter shape across the cortex and a high level of similarity with the gold-standard, defined as a subset of profiles that are unaffected by intracortical veins. CONCLUSIONS We conclude that deconvolution is a robust approach for removing the effect of signal propagation through intracortical veins. This makes it possible to obtain profiles with high laminar specificity while benefitting from the higher efficiency of GE-BOLD sequences.
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Affiliation(s)
- Irati Markuerkiaga
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
| | - José P Marques
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
| | - Tara E Gallagher
- Department of Physics and Astronomy, Dartmouth College, Hanover, NH, USA
| | - David G Norris
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands; Erwin L. Hahn Institute for Magnetic Resonance Imaging, 45141, Essen, Germany.
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Markuerkiaga I, Marques JP, Bains LJ, Norris DG. An in-vivo study of BOLD laminar responses as a function of echo time and static magnetic field strength. Sci Rep 2021; 11:1862. [PMID: 33479362 PMCID: PMC7820587 DOI: 10.1038/s41598-021-81249-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 06/10/2019] [Accepted: 12/22/2020] [Indexed: 11/18/2022] Open
Abstract
Layer specific functional MRI requires high spatial resolution data. To compensate the associated poor signal to noise ratio it is common to integrate the signal from voxels at a given cortical depth. If the region is sufficiently large then physiological noise will be the dominant noise source. In this work, activation profiles in response to the same visual stimulus are compared at 1.5 T, 3 T and 7 T using a multi-echo, gradient echo (GE) FLASH sequence, with a 0.75 mm isotropic voxel size and the cortical integration approach. The results show that after integrating over a cortical volume of 40, 60 and 100 mm3 (at 7 T, 3 T, and 1.5 T, respectively), the signal is in the physiological noise dominated regime. The activation profiles obtained are similar for equivalent echo times. BOLD-like noise is found to be the dominant source of physiological noise. Consequently, the functional contrast to noise ratio is not strongly echo-time or field-strength dependent. We conclude that laminar GE-BOLD fMRI at lower field strengths is feasible but that larger patches of cortex will need to be examined, and that the acquisition efficiency is reduced.
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Affiliation(s)
- Irati Markuerkiaga
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - José P Marques
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - Lauren J Bains
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - David G Norris
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands. .,Erwin L. Hahn Institute for Magnetic Resonance Imaging, 45141, Essen, Germany.
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Chan KS, Marques JP. SEPIA-Susceptibility mapping pipeline tool for phase images. Neuroimage 2020; 227:117611. [PMID: 33309901 DOI: 10.1016/j.neuroimage.2020.117611] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [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: 07/23/2020] [Revised: 10/14/2020] [Accepted: 11/25/2020] [Indexed: 12/20/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is a physics-driven computational technique that has a high sensitivity in quantifying iron deposition based on MRI phase images. Furthermore, it has a unique ability to distinguish paramagnetic and diamagnetic contributions such as haemorrhage and calcification based on image contrast. These properties have contributed to a growing interest to use QSM not only in research but also in clinical applications. However, it is challenging to obtain high quality susceptibility map because of its ill-posed nature, especially for researchers who have less experience with QSM and the optimisation of its pipeline. In this paper, we present an open-source processing pipeline tool called SuscEptibility mapping PIpeline tool for phAse images (SEPIA) dedicated to the post-processing of MRI phase images and QSM. SEPIA connects various QSM toolboxes freely available in the field to offer greater flexibility in QSM processing. It also provides an interactive graphical user interface to construct and execute a QSM processing pipeline, simplifying the workflow in QSM research. The extendable design of SEPIA also allows developers to deploy their methods in the framework, providing a platform for developers and researchers to share and utilise the state-of-the-art methods in QSM.
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Affiliation(s)
- Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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Konieczny MJ, Dewenter A, Ter Telgte A, Gesierich B, Wiegertjes K, Finsterwalder S, Kopczak A, Hübner M, Malik R, Tuladhar AM, Marques JP, Norris DG, Koch A, Dietrich O, Ewers M, Schmidt R, de Leeuw FE, Duering M. Multi-shell Diffusion MRI Models for White Matter Characterization in Cerebral Small Vessel Disease. Neurology 2020; 96:e698-e708. [PMID: 33199431 DOI: 10.1212/wnl.0000000000011213] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 09/21/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To test the hypothesis that multi-shell diffusion models improve the characterization of microstructural alterations in cerebral small vessel disease (SVD), we assessed associations with processing speed performance, longitudinal change, and reproducibility of diffusion metrics. METHODS We included 50 patients with sporadic and 59 patients with genetically defined SVD (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy [CADASIL]) with cognitive testing and standardized 3T MRI, including multi-shell diffusion imaging. We applied the simple diffusion tensor imaging (DTI) model and 2 advanced models: diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI). Linear regression and multivariable random forest regression (including conventional SVD markers) were used to determine associations between diffusion metrics and processing speed performance. The detection of short-term disease progression was assessed by linear mixed models in 49 patients with sporadic SVD with longitudinal high-frequency imaging (in total 459 MRIs). Intersite reproducibility was determined in 10 patients with CADASIL scanned back-to-back on 2 different 3T MRI scanners. RESULTS Metrics from DKI showed the strongest associations with processing speed performance (R 2 up to 21%) and the largest added benefit on top of conventional SVD imaging markers in patients with sporadic SVD and patients with CADASIL with lower SVD burden. Several metrics from DTI and DKI performed similarly in detecting disease progression. Reproducibility was excellent (intraclass correlation coefficient >0.93) for DTI and DKI metrics. NODDI metrics were less reproducible. CONCLUSION Multi-shell diffusion imaging and DKI improve the detection and characterization of cognitively relevant microstructural white matter alterations in SVD. Excellent reproducibility of diffusion metrics endorses their use as SVD markers in research and clinical care. Our publicly available intersite dataset facilitates future studies. CLASSIFICATION OF EVIDENCE This study provides Class I evidence that in patients with SVD, diffusion MRI metrics are associated with processing speed performance.
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Affiliation(s)
- Marek J Konieczny
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Anna Dewenter
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Annemieke Ter Telgte
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Benno Gesierich
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Kim Wiegertjes
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Sofia Finsterwalder
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Anna Kopczak
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Mathias Hübner
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Rainer Malik
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Anil M Tuladhar
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - José P Marques
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - David G Norris
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Alexandra Koch
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Olaf Dietrich
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Michael Ewers
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Reinhold Schmidt
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Frank-Erik de Leeuw
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany
| | - Marco Duering
- From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany.
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Chan KS, Marques JP. Multi-compartment relaxometry and diffusion informed myelin water imaging – Promises and challenges of new gradient echo myelin water imaging methods. Neuroimage 2020; 221:117159. [DOI: 10.1016/j.neuroimage.2020.117159] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 07/06/2020] [Accepted: 07/07/2020] [Indexed: 01/08/2023] Open
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Gesierich B, Tuladhar AM, ter Telgte A, Wiegertjes K, Konieczny MJ, Finsterwalder S, Hübner M, Pirpamer L, Koini M, Abdulkadir A, Franzmeier N, Norris DG, Marques JP, zu Eulenburg P, Ewers M, Schmidt R, de Leeuw F, Duering M. Alterations and test-retest reliability of functional connectivity network measures in cerebral small vessel disease. Hum Brain Mapp 2020; 41:2629-2641. [PMID: 32087047 PMCID: PMC7294060 DOI: 10.1002/hbm.24967] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [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: 10/09/2019] [Revised: 01/30/2020] [Accepted: 02/13/2020] [Indexed: 12/19/2022] Open
Abstract
While structural network analysis consolidated the hypothesis of cerebral small vessel disease (SVD) being a disconnection syndrome, little is known about functional changes on the level of brain networks. In patients with genetically defined SVD (CADASIL, n = 41) and sporadic SVD (n = 46), we independently tested the hypothesis that functional networks change with SVD burden and mediate the effect of disease burden on cognitive performance, in particular slowing of processing speed. We further determined test-retest reliability of functional network measures in sporadic SVD patients participating in a high-frequency (monthly) serial imaging study (RUN DMC-InTENse, median: 8 MRIs per participant). Functional networks for the whole brain and major subsystems (i.e., default mode network, DMN; fronto-parietal task control network, FPCN; visual network, VN; hand somatosensory-motor network, HSMN) were constructed based on resting-state multi-band functional MRI. In CADASIL, global efficiency (a graph metric capturing network integration) of the DMN was lower in patients with high disease burden (standardized beta = -.44; p [corrected] = .035) and mediated the negative effect of disease burden on processing speed (indirect path: std. beta = -.20, p = .047; direct path: std. beta = -.19, p = .25; total effect: std. beta = -.39, p = .02). The corresponding analyses in sporadic SVD showed no effect. Intraclass correlations in the high-frequency serial MRI dataset of the sporadic SVD patients revealed poor test-retest reliability and analysis of individual variability suggested an influence of age, but not disease burden, on global efficiency. In conclusion, our results suggest that changes in functional connectivity networks mediate the effect of SVD-related brain damage on cognitive deficits. However, limited reliability of functional network measures, possibly due to age-related comorbidities, impedes the analysis in elderly SVD patients.
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Affiliation(s)
- Benno Gesierich
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Anil Man Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Annemieke ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Marek J. Konieczny
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Sofia Finsterwalder
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Mathias Hübner
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Lukas Pirpamer
- Department of NeurologyMedical University of GrazGrazAustria
| | - Marisa Koini
- Department of NeurologyMedical University of GrazGrazAustria
| | - Ahmed Abdulkadir
- University Hospital of Old Age Psychiatry, Universitäre Psychiatrische Dienste (UPD) BernUniversity of BernBernSwitzerland
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - David G. Norris
- Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - José P. Marques
- Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - Peter zu Eulenburg
- German Center for Vertigo and Balance DisordersUniversity HospitalMunichGermany
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | | | - Frank‐Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
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Jorge J, Gretsch F, Najdenovska E, Tuleasca C, Levivier M, Maeder P, Gallichan D, Marques JP, Bach Cuadra M. Improved susceptibility-weighted imaging for high contrast and resolution thalamic nuclei mapping at 7T. Magn Reson Med 2020; 84:1218-1234. [PMID: 32052486 DOI: 10.1002/mrm.28197] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [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: 06/10/2019] [Revised: 01/10/2020] [Accepted: 01/13/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE The thalamus is an important brain structure and neurosurgical target, but its constituting nuclei are challenging to image non-invasively. Recently, susceptibility-weighted imaging (SWI) at ultra-high field has shown promising capabilities for thalamic nuclei mapping. In this work, several methodological improvements were explored to enhance SWI quality and contrast, and specifically its ability for thalamic imaging. METHODS High-resolution SWI was performed at 7T in healthy participants, and the following techniques were applied: (a) monitoring and retrospective correction of head motion and B0 perturbations using integrated MR navigators, (b) segmentation and removal of venous vessels on the SWI data using vessel enhancement filtering, and (c) contrast enhancement by tuning the parameters of the SWI phase-magnitude combination. The resulting improvements were evaluated with quantitative metrics of image quality, and by comparison to anatomo-histological thalamic atlases. RESULTS Even with sub-millimeter motion and natural breathing, motion and field correction produced clear improvements in both magnitude and phase data quality (76% and 41%, respectively). The improvements were stronger in cases of larger motion/field deviations, mitigating the dependence of image quality on subject performance. Optimizing the SWI phase-magnitude combination yielded substantial improvements in image contrast, particularly in the thalamus, well beyond previously reported SWI results. The atlas comparisons provided compelling evidence of anatomical correspondence between SWI features and several thalamic nuclei, for example, the ventral intermediate nucleus. Vein detection performed favorably inside the thalamus, and vein removal further improved visualization. CONCLUSION Altogether, the proposed developments substantially improve high-resolution SWI, particularly for thalamic nuclei imaging.
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Affiliation(s)
- João Jorge
- Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland.,Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Frédéric Gretsch
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Elena Najdenovska
- Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Constantin Tuleasca
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Marc Levivier
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.,Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippe Maeder
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Daniel Gallichan
- Cardiff University Brain Research Imaging Centre, School of Engineering, Cardiff University, Cardiff, UK
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Meritxell Bach Cuadra
- Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Ter Telgte A, Wiegertjes K, Gesierich B, Marques JP, Huebner M, de Klerk JJ, Schreuder FHBM, Araque Caballero MA, Kuijf HJ, Norris DG, Klijn CJM, Dichgans M, Tuladhar AM, Duering M, de Leeuw FE. Contribution of acute infarcts to cerebral small vessel disease progression. Ann Neurol 2019; 86:582-592. [PMID: 31340067 PMCID: PMC6771732 DOI: 10.1002/ana.25556] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [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: 04/17/2019] [Revised: 07/18/2019] [Accepted: 07/18/2019] [Indexed: 01/02/2023]
Abstract
Objective To determine the contribution of acute infarcts, evidenced by diffusion‐weighted imaging positive (DWI+) lesions, to progression of white matter hyperintensities (WMH) and other cerebral small vessel disease (SVD) markers. Methods We performed monthly 3T magnetic resonance imaging (MRI) for 10 consecutive months in 54 elderly individuals with SVD. MRI included high‐resolution multishell DWI, and 3‐dimensional fluid‐attenuated inversion recovery, T1, and susceptibility‐weighted imaging. We determined DWI+ lesion evolution, WMH progression rate (ml/mo), and number of incident lacunes and microbleeds, and calculated for each marker the proportion of progression explained by DWI+ lesions. Results We identified 39 DWI+ lesions on 21 of 472 DWI scans in 9 of 54 subjects. Of the 36 DWI+ lesions with follow‐up MRI, 2 evolved into WMH, 4 evolved into a lacune (3 with cavity <3mm), 3 evolved into a microbleed, and 27 were not detectable on follow‐up. WMH volume increased at a median rate of 0.027 ml/mo (interquartile range = 0.005–0.073), but was not significantly higher in subjects with DWI+ lesions compared to those without (p = 0.195). Of the 2 DWI+ lesions evolving into WMH on follow‐up, one explained 23% of the total WMH volume increase in one subject, whereas the WMH regressed in the other subject. DWI+ lesions preceded 4 of 5 incident lacunes and 3 of 10 incident microbleeds. Interpretation DWI+ lesions explain only a small proportion of the total WMH progression. Hence, WMH progression seems to be mostly driven by factors other than acute infarcts. DWI+ lesions explain the majority of incident lacunes and small cavities, and almost one‐third of incident microbleeds, confirming that WMH, lacunes, and microbleeds, although heterogeneous on MRI, can have a common initial appearance on MRI. ANN NEUROL 2019;86:582–592
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Affiliation(s)
- Annemieke Ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
| | - Mathias Huebner
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Jabke J de Klerk
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Floris H B M Schreuder
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Miguel A Araque Caballero
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE Munich), Munich, Germany
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
| | - Catharina J M Klijn
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE Munich), Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
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Hilbert T, Schulz J, Marques JP, Thiran J, Krueger G, Norris DG, Kober T. Fast model‐based T
2
mapping using SAR‐reduced simultaneous multislice excitation. Magn Reson Med 2019; 82:2090-2103. [DOI: 10.1002/mrm.27890] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 05/23/2019] [Accepted: 06/13/2019] [Indexed: 12/14/2022]
Affiliation(s)
- Tom Hilbert
- Advanced Clinical Imaging Technology Siemens Healthcare Lausanne Switzerland
- Department of Radiology Lausanne University Hospital Lausanne Switzerland
- Signal Processing Laboratory 5 École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Jenni Schulz
- Donders Institute for Brain, Cognition and Behavior Radboud University Nijmegen Nijmegen Netherlands
| | - José P. Marques
- Donders Institute for Brain, Cognition and Behavior Radboud University Nijmegen Nijmegen Netherlands
| | - Jean‐Philippe Thiran
- Department of Radiology Lausanne University Hospital Lausanne Switzerland
- Signal Processing Laboratory 5 École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Gunnar Krueger
- Technology and Innovation EMEA, Siemens Healthcare Lausanne Switzerland
| | - David G. Norris
- Donders Institute for Brain, Cognition and Behavior Radboud University Nijmegen Nijmegen Netherlands
| | - Tobias Kober
- Advanced Clinical Imaging Technology Siemens Healthcare Lausanne Switzerland
- Department of Radiology Lausanne University Hospital Lausanne Switzerland
- Signal Processing Laboratory 5 École Polytechnique Fédérale de Lausanne Lausanne Switzerland
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33
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Shams Z, Norris DG, Marques JP. A comparison of in vivo MRI based cortical myelin mapping using T1w/T2w and R1 mapping at 3T. PLoS One 2019; 14:e0218089. [PMID: 31269041 PMCID: PMC6609014 DOI: 10.1371/journal.pone.0218089] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 05/26/2019] [Indexed: 12/17/2022] Open
Abstract
In this manuscript, we compare two commonly used methods to perform cortical mapping based on myelination of the human neocortex. T1w/T2w and R1 maps with matched total acquisition times were obtained from a young cohort in randomized order and using a test–retest design. Both methodologies showed cortical myelin maps that enhanced similar anatomical features, namely primary sensory regions known to be myelin rich. T1w/T2w maps showed increased robustness to movement artifacts in comparison to R1 maps, while the test re-test reproducibility of both methods was comparable. Based on Brodmann parcellation, both methods showed comparable variability within each region. Having parcellated cortical myelin maps into VDG11b areas of 4a, 4p, 3a, 3b, 1, 2, V2, and MT, both methods behave identically with R1 showing an increased variability between subjects. In combination with the test re-test evaluation, we concluded that this increased variability between subjects reflects relevant tissue variability. A high level of correlation was found between the R1 and T1w/T2w regions with regions of higher deviations being co-localized with those where the transmit RF field deviated most from its nominal value. We conclude that R1 mapping strategies might be preferable when studying different population cohorts where cortical properties are expected to be altered while T1w/T2w mapping will have advantages when performing cortical based segmentation.
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Affiliation(s)
- Zahra Shams
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands
| | - David G. Norris
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands
| | - José P. Marques
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands
- * E-mail:
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Abstract
Historically, clinical MRI started with main magnetic field strengths in the ∼0.05-0.35T range. In the past 40 years there have been considerable developments in MRI hardware, with one of the primary ones being the trend to higher magnetic fields. While resulting in large improvements in data quality and diagnostic value, such developments have meant that conventional systems at 1.5 and 3T remain relatively expensive pieces of medical imaging equipment, and are out of the financial reach for much of the world. In this review we describe the current state-of-the-art of low-field systems (defined as 0.25-1T), both with respect to its low cost, low foot-print, and subject accessibility. Furthermore, we discuss how low field could potentially benefit from many of the developments that have occurred in higher-field MRI. In the first section, the signal-to-noise ratio (SNR) dependence on the static magnetic field and its impact on the achievable contrast, resolution, and acquisition times are discussed from a theoretical perspective. In the second section, developments in hardware (eg, magnet, gradient, and RF coils) used both in experimental low-field scanners and also those that are currently in the market are reviewed. In the final section the potential roles of new acquisition readouts, motion tracking, and image reconstruction strategies, currently being developed primarily at higher fields, are presented. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019.
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Affiliation(s)
- José P. Marques
- Radboud University, Donders Institute for Brain, Cognition and BehaviourNijmegenThe Netherlands
| | - Frank F.J. Simonis
- Magnetic Detection & Imaging, Technical Medical CentreUniversity of TwenteThe Netherlands
| | - Andrew G. Webb
- C.J.Gorter Center for High Field MRI, Department of RadiologyLeiden University Medical CentreThe Netherlands
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35
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Najdenovska E, Tuleasca C, Jorge J, Maeder P, Marques JP, Roine T, Gallichan D, Thiran JP, Levivier M, Bach Cuadra M. Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T. Sci Rep 2019; 9:1119. [PMID: 30718634 PMCID: PMC6361927 DOI: 10.1038/s41598-018-37825-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [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: 05/17/2018] [Accepted: 12/13/2018] [Indexed: 12/22/2022] Open
Abstract
The ventro-intermediate nucleus (Vim), as part of the motor thalamic nuclei, is a commonly used target in functional stereotactic neurosurgery for treatment of drug-resistant tremor. As it cannot be directly visualized on routinely used magnetic resonance imaging (MRI), its clinical targeting is performed using indirect methods. Recent literature suggests that the Vim can be directly visualized on susceptibility-weighted imaging (SWI) acquired at 7 T. Our work aims to assess the distinguishable Vim on 7 T SWI in both healthy-population and patients and, using it as a reference, to compare it with: (1) The clinical targeting, (2) The automated parcellation of thalamic subparts based on 3 T diffusion MRI (dMRI), and (3) The multi-atlas segmentation techniques. In 95.2% of the data, the manual outline was adjacent to the inferior lateral border of the dMRI-based motor-nuclei group, while in 77.8% of the involved cases, its ventral part enclosed the Guiot points. Moreover, the late MRI signature in the patients was always observed in the anterior part of the manual delineation and it overlapped with the multi-atlas outline. Overall, our study provides new insight on Vim discrimination through MRI and imply novel strategies for its automated segmentation, thereby opening new perspectives for standardizing the clinical targeting.
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Affiliation(s)
- Elena Najdenovska
- Centre d'Imagerie BioMédicale (CIBM), University of Lausanne (UNIL), Lausanne, Switzerland. .,Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
| | - Constantin Tuleasca
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland.,Sorbonne Université, Faculté de Médecine, Paris, France.,Assistance Publique - Hôpitaux de Paris, Hôpitaux Universitaires Paris-Sud, Hôpital Bicêtre, Service de Neurochirurgie, Le Kremlin Bicêtre, France
| | - João Jorge
- Centre d'Imagerie BioMédicale (CIBM), University of Lausanne (UNIL), Lausanne, Switzerland.,Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Philippe Maeder
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - José P Marques
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Timo Roine
- Centre d'Imagerie BioMédicale (CIBM), University of Lausanne (UNIL), Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Turku Brain and Mind Center, University of Turku, Turku, Finland
| | - Daniel Gallichan
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marc Levivier
- Department of Clinical Neurosciences, Neurosurgery Service and Gamma Knife Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Centre d'Imagerie BioMédicale (CIBM), University of Lausanne (UNIL), Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Caan MWA, Bazin PL, Marques JP, de Hollander G, Dumoulin SO, van der Zwaag W. MP2RAGEME: T 1 , T 2 * , and QSM mapping in one sequence at 7 tesla. Hum Brain Mapp 2018; 40:1786-1798. [PMID: 30549128 PMCID: PMC6590660 DOI: 10.1002/hbm.24490] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [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: 07/04/2018] [Revised: 11/21/2018] [Accepted: 11/28/2018] [Indexed: 12/19/2022] Open
Abstract
Quantitative magnetic resonance imaging generates images of meaningful physical or chemical variables measured in physical units that allow quantitative comparisons between tissue regions and among subjects scanned at the same or different sites. Here, we show that we can acquire quantitative T1, T2*, and quantitative susceptibility mapping (QSM) information in a single acquisition, using a multi‐echo (ME) extension of the second gradient‐echo image of the MP2RAGE sequence. This combination is called MP2RAGE ME, or MP2RAGEME. The simultaneous acquisition results in large time savings, perfectly coregistered data, and minimal image quality differences compared to separately acquired data. Following a correction for residual transmit B1+‐sensitivity, quantitative T1, T2*, and QSM values were in excellent agreement with those obtained from separately acquired, also B1+‐corrected, MP2RAGE data and ME gradient echo data. The quantitative values from reference regions of interests were also in very good correspondence with literature values. From the MP2RAGEME data, we further derived a multiparametric cortical parcellation, as well as a combined arterial and venous map. In sum, our MP2RAGEME sequence has the benefit in large time savings, perfectly coregistered data and minor image quality differences.
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Affiliation(s)
- Matthan W A Caan
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands.,Amsterdam UMC, University of Amsterdam, Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - Pierre-Louis Bazin
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands.,Social Brain Laboratory, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.,Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Gilles de Hollander
- Experimental and Applied Psychology, VU University, Amsterdam, The Netherlands
| | - Serge O Dumoulin
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands.,Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands.,Experimental and Applied Psychology, VU University, Amsterdam, The Netherlands
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van der Zwaag W, Buur PF, Fracasso A, van Doesum T, Uludağ K, Versluis M, Marques JP. Examples of sub-millimeter, 7T, T 1-weighted EPI datasets acquired with the T 123DEPI sequence. Data Brief 2018; 20:415-418. [PMID: 30175207 PMCID: PMC6116420 DOI: 10.1016/j.dib.2018.08.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 08/02/2018] [Accepted: 08/09/2018] [Indexed: 11/26/2022] Open
Abstract
These imaging data are examples of sub-millimeter resolution T1-weighted EPI (Echo Planar Imaging) acquired using the T123DEPI (T1-imaging with 2 3D-EPIs) sequence [1]; functional MRI data with matching resolution and distortion, and MP2RAGE (Magnetization Prepared 2 Rapid Acquisition Gradient Echoes) anatomical images [2], from the same subjects. Data from two protocols and subjects presented in the paper describing the sequence [1] are made available here: 0.8 mm protocol: whole brain, axial T123DEPI T1-weighted images; a 5-minute fMRI run with the same orientation and 27 mm coverage in the slice selection direction, covering the primary visual cortex. fMRI data were acquired while the volunteer viewed a flashing checkerboard stimulus; the unsmoothed GLM results of the fMRI and a 0.64 mm resolution MP2RAGE from the same subject. These data are from Experiment 3 in [1]
0.7 mm protocol: partial brain T123DEPI T1-weighted images with longer or shorter readouts; matching coronal echo planar images again acquired while viewing a flashing checkerboard stimulus and a 0.64 mm whole brain MP2RAGE from the same subject. These data are from Experiment 1 in [1]
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Affiliation(s)
| | | | - Alessio Fracasso
- Spinoza Centre for Neuroimaging, Amsterdam, Netherlands
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
| | | | | | | | - José P. Marques
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands
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Chan KS, Norris DG, Marques JP. Structure tensor informed fibre tractography at 3T. Hum Brain Mapp 2018; 39:4440-4451. [PMID: 30030945 DOI: 10.1002/hbm.24283] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [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: 02/07/2018] [Revised: 05/14/2018] [Accepted: 06/12/2018] [Indexed: 12/21/2022] Open
Abstract
Structure tensor informed fibre tractography (STIFT) based on informing tractography for diffusion-weighted images at 3T and by utilising the structure tensor obtained from gradient-recalled echo (GRE) images at 7T is able to delineate fibres when seed voxels are placed close to the fibre boundaries. However, incorporating data from two different field strengths limits the applicability of STIFT. In this study, STIFT was implemented with both diffusion-weighted images and GRE images acquired at 3T. Instead of using the magnitude GRE data directly for STIFT as in the previous work, the utility of T2 * maps and quantitative susceptibility maps derived from complex-valued GRE data to improve fibre delineation was explored. Single-seed tractography was performed and the results show that the optic radiation reconstructed with STIFT is more distinguishable from the inferior longitudinal fasciculus/inferior fronto-occipital fasciculus complex when compared to standard diffusion-weighted imaging tractography. We further investigated the quantitative effects of STIFT in a group of five healthy volunteers and evaluated its impact on measures of structural connectivity. The framework was extended to evaluate implementations of STIFT based on T2 *-weighted and quantitative susceptibility-weighted images in a whole-brain connectivity study. In terms of connectivity, no systematic differences were found between STIFT and diffusion-weighted imaging tractography, suggesting that local improvements in tractography are not translated to the atlas-based structural connectivity analysis. Nevertheless, the reduction in the number of statistically significant connections in the STIFT connectivity matrix suggests that STIFT can potentially reduce the false-positive connections in fibre tractography.
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Affiliation(s)
- Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.,Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany.,MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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Ter Telgte A, Wiegertjes K, Tuladhar AM, Noz MP, Marques JP, Gesierich B, Huebner M, Mutsaerts HJM, Elias-Smale SE, Beelen MJ, Ropele S, Kessels RP, Riksen NP, Klijn CJ, Norris DG, Duering M, de Leeuw FE. Investigating the origin and evolution of cerebral small vessel disease: The RUN DMC - InTENse study. Eur Stroke J 2018; 3:369-378. [PMID: 31236485 PMCID: PMC6571506 DOI: 10.1177/2396987318776088] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [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: 01/27/2018] [Accepted: 04/17/2018] [Indexed: 01/24/2023] Open
Abstract
Background Neuroimaging in older adults commonly reveals signs of cerebral small vessel
disease (SVD). SVD is believed to be caused by chronic hypoperfusion based
on animal models and longitudinal studies with inter-scan intervals of
years. Recent imaging evidence, however, suggests a role for acute
ischaemia, as indicated by incidental diffusion-weighted imaging lesions
(DWI+ lesions), in the origin of SVD. Furthermore, it becomes increasingly
recognised that focal SVD lesions likely affect the structure and function
of brain areas remote from the original SVD lesion. However, the temporal
dynamics of these events are largely unknown. Aims (1) To investigate the monthly incidence of DWI+ lesions in subjects with
SVD; (2) to assess to which extent these lesions explain progression of SVD
imaging markers; (3) to investigate their effects on cortical thickness,
structural and functional connectivity and cognitive and motor performance;
and (4) to investigate the potential role of the innate immune system in the
pathophysiology of SVD. Design/methods The RUN DMC – InTENse study is a longitudinal observational study among 54
non-demented RUN DMC survivors with mild to severe SVD and no other presumed
cause of ischaemia. We performed MRI assessments monthly during 10
consecutive months (totalling up to 10 scans per subject), complemented with
clinical, motor and cognitive examinations. Discussion Our study will provide a better understanding of the role of DWI+ lesions in
the pathophysiology of SVD and will further unravel the structural and
functional consequences and clinical importance of these lesions, with an
unprecedented temporal resolution. Understanding the role of acute,
potentially ischaemic, processes in SVD may provide new strategies for
therapies.
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Affiliation(s)
- Annemieke Ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Marlies P Noz
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Center for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Mathias Huebner
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | | | - Suzette E Elias-Smale
- Department of Cardiology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Marie-José Beelen
- Department of Surgery, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Roy Pc Kessels
- Department of Medical Psychology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Niels P Riksen
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Catharina Jm Klijn
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behaviour, Center for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
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Langkammer C, Schweser F, Shmueli K, Kames C, Li X, Guo L, Milovic C, Kim J, Wei H, Bredies K, Buch S, Guo Y, Liu Z, Meineke J, Rauscher A, Marques JP, Bilgic B. Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge. Magn Reson Med 2018; 79:1661-1673. [PMID: 28762243 PMCID: PMC5777305 DOI: 10.1002/mrm.26830] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [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: 03/01/2017] [Revised: 06/03/2017] [Accepted: 06/17/2017] [Indexed: 01/10/2023]
Abstract
PURPOSE The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully. METHODS Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations. Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions. RESULTS Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance. CONCLUSION Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria. Magn Reson Med 79:1661-1673, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; Clinical and Translational Science Institute, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Christian Kames
- UBC MRI Research Centre, Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Li Guo
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Carlos Milovic
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile; Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Jinsuh Kim
- Department of Radiology, University of Illinois at Chicago, IL, USA
| | - Hongjiang Wei
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Kristian Bredies
- Institute of Mathematics and Scientific Computing, University of Graz, Austria
| | - Sagar Buch
- The MRI Institute for Biomedical Research, Waterloo, Ontario, Canada
| | - Yihao Guo
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Zhe Liu
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | | | - Alexander Rauscher
- UBC MRI Research Centre, Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - José P. Marques
- Donders Centre for Cognitive Neuroimaging, Radboud University, The Netherlands
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, MGH, Boston, MA, USA
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41
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Gretsch F, Marques JP, Gallichan D. Investigating the accuracy of FatNav-derived estimates of temporal B 0 changes and their application to retrospective correction of high-resolution 3D GRE of the human brain at 7T. Magn Reson Med 2018; 80:585-597. [PMID: 29359352 DOI: 10.1002/mrm.27063] [Citation(s) in RCA: 12] [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: 06/06/2017] [Revised: 11/30/2017] [Accepted: 12/06/2017] [Indexed: 11/05/2022]
Abstract
PURPOSE To investigate the precision of estimates of temporal variations of magnetic field achievable by double-echo fat image navigators (FatNavs), and their potential application to retrospective correction of 3-dimensional gradient echo-based sequences. METHODS Both head motion and temporal changes of B0 were tracked using double-echo highly accelerated 3-dimensional FatNavs as navigators, allowing estimation of the temporal changes in low spatial-order field coefficients. The accuracy of the method was determined by direct comparison to controlled offsets in the linear imaging gradients. Double-echo FatNavs were also incorporated into a high-resolution, 3-dimensional gradient echo-based sequence to retrospectively correct for both motion and temporal changes in B0 during natural and deep breathing. The additional scan time was 5 min (a 40% increase). Correction was also investigated using only the first echo of the FatNav to explore the trade-off in accuracy versus scan time. RESULTS Excellent accuracy (0.27 Hz, 1.57-2.75 Hz/m) was achieved for tracking field changes, and no significant bias could be observed. Artifacts in the 3-dimensional gradient echo-based images induced by temporal field changes, if present, were effectively reduced using either the field estimates from the double echo or the first echo only from the FatNavs. CONCLUSION The FatNavs were shown to be an excellent candidate for accurate, fast, and precise estimation of global field variations for the tested patterns of respiration. Future work will investigate ways to increase the temporal sampling to increase robustness to variations in breathing patterns. Magn Reson Med 80:585-597, 2018. © 2018 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Frédéric Gretsch
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - José P Marques
- Donders Institute, Radboud University, Nijmegen, the Netherlands
| | - Daniel Gallichan
- Biomedical Imaging Research Center, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Cardiff University Brain Research Imaging Centre (CUBRIC), School of Engineering, Cardiff University, Cardiff, United Kingdom
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42
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Schulz J, P Marques J, Ter Telgte A, van Dorst A, de Leeuw FE, Meijer FJA, Norris DG. Clinical application of Half Fourier Acquisition Single Shot Turbo Spin Echo (HASTE) imaging accelerated by simultaneous multi-slice acquisition. Eur J Radiol 2017; 98:200-206. [PMID: 29279164 DOI: 10.1016/j.ejrad.2017.11.022] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [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: 04/12/2017] [Revised: 10/09/2017] [Accepted: 11/29/2017] [Indexed: 02/06/2023]
Abstract
PURPOSE As a single-shot sequence with a long train of refocusing pulses, Half-Fourier Acquisition Single-Shot Turbo-Spin-Echo (HASTE) suffers from high power deposition limiting use at high resolutions and high field strengths, particularly if combined with acceleration techniques such as simultaneous multi-slice (SMS) imaging. Using a combination of multiband (MB)-excitation and PINS-refocusing pulses will effectively accelerate the acquisition time while staying within the SAR limitations. In particular, uncooperative and young patients will profit from the speed of the MB-PINS HASTE sequence, as clinical diagnosis can be possible without sedation. Materials and MethodsMB-excitation and PINS-refocusing pulses were incorporated into a HASTE-sequence with blipped CAIPIRINHA and TRAPS including an internal FLASH reference scan for online reconstruction. Whole brain MB-PINS HASTE data were acquired on a Siemens 3T-Prisma system from 10 individuals and compared to a clinical HASTE protocol. ResultsThe proposed MB-PINS HASTE protocol accelerates the acquisition by about a factor 2 compared to the clinical HASTE. The diagnostic image quality proved to be comparable for both sequences for the evaluation of the overall aspect of the brain, the detection of white matter changes and areas of tissue loss, and for the evaluation of the CSF spaces although artifacts were more frequently encountered with MB-PINS HASTE. ConclusionsMB-PINS HASTE enables acquisition of slice accelerated highly T2-weighted images and provides good diagnostic image quality while reducing acquisition time.
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Affiliation(s)
- Jenni Schulz
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, The Netherlands.
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, The Netherlands
| | - Annemieke Ter Telgte
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, The Netherlands; Department of Neurology, Radboud University Medical Centre Nijmegen, The Netherlands
| | - Anouk van Dorst
- Department of Radiology and Nuclear Medicine, Jeroen Bosch Hospital, 's Hertogenbosch, The Netherlands
| | - Frank-Erik de Leeuw
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, The Netherlands; Department of Neurology, Radboud University Medical Centre Nijmegen, The Netherlands
| | - Frederick J A Meijer
- Department of Radiology and Nuclear Medicine, Radboud University Medical Centre Nijmegen, The Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, The Netherlands; Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany
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43
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Bonnier G, Maréchal B, Fartaria MJ, Falkowskiy P, Marques JP, Simioni S, Schluep M, Du Pasquier R, Thiran JP, Krueger G, Granziera C. The Combined Quantification and Interpretation of Multiple Quantitative Magnetic Resonance Imaging Metrics Enlightens Longitudinal Changes Compatible with Brain Repair in Relapsing-Remitting Multiple Sclerosis Patients. Front Neurol 2017; 8:506. [PMID: 29021778 PMCID: PMC5623825 DOI: 10.3389/fneur.2017.00506] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [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: 05/12/2017] [Accepted: 09/08/2017] [Indexed: 12/25/2022] Open
Abstract
Objective Quantitative and semi-quantitative MRI (qMRI) metrics provide complementary specificity and differential sensitivity to pathological brain changes compatible with brain inflammation, degeneration, and repair. Moreover, advanced magnetic resonance imaging (MRI) metrics with overlapping elements amplify the true tissue-related information and limit measurement noise. In this work, we combined multiple advanced MRI parameters to assess focal and diffuse brain changes over 2 years in a group of early-stage relapsing-remitting MS patients. Methods Thirty relapsing-remitting MS patients with less than 5 years disease duration and nine healthy subjects underwent 3T MRI at baseline and after 2 years including T1, T2, T2* relaxometry, and magnetization transfer imaging. To assess longitudinal changes in normal-appearing (NA) tissue and lesions, we used analyses of variance and Bonferroni correction for multiple comparisons. Multivariate linear regression was used to assess the correlation between clinical outcome and multiparametric MRI changes in lesions and NA tissue. Results In patients, we measured a significant longitudinal decrease of mean T2 relaxation times in NA white matter (p = 0.005) and a decrease of T1 relaxation times in the pallidum (p < 0.05), which are compatible with edema reabsorption and/or iron deposition. No longitudinal changes in qMRI metrics were observed in controls. In MS lesions, we measured a decrease in T1 relaxation time (p-value < 2.2e−16) and a significant increase in MTR (p-value < 1e−6), suggesting repair mechanisms, such as remyelination, increased axonal density, and/or a gliosis. Last, the evolution of advanced MRI metrics—and not changes in lesions or brain volume—were correlated to motor and cognitive tests scores evolution (Adj-R2 > 0.4, p < 0.05). In summary, the combination of multiple advanced MRI provided evidence of changes compatible with focal and diffuse brain repair at early MS stages as suggested by histopathological studies.
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Affiliation(s)
- Guillaume Bonnier
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Benedicte Maréchal
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare, Lausanne, Switzerland.,Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Mário João Fartaria
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare, Lausanne, Switzerland.,Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Pavel Falkowskiy
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare, Lausanne, Switzerland.,Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - José P Marques
- Donders Centre for Cognitive Neuroimaging, Radbound University, Nijmegen, Netherlands
| | - Samanta Simioni
- Neuropsychology, Institution de Lavigny, Denens, Switzerland
| | - Myriam Schluep
- Neurology Service and Neuroimmunology Laboratory, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Renaud Du Pasquier
- Neurology Service and Neuroimmunology Laboratory, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Gunnar Krueger
- Siemens Medical Solutions USA IM MR COL NEZ, Burlington, MA, United States
| | - Cristina Granziera
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States.,Neurology Service and Neuroimmunology Laboratory, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
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44
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van der Zwaag W, Reynaud O, Narsude M, Gallichan D, Marques JP. High spatio-temporal resolution in functional MRI with 3D echo planar imaging using cylindrical excitation and a CAIPIRINHA undersampling pattern. Magn Reson Med 2017; 79:2589-2596. [PMID: 28905414 DOI: 10.1002/mrm.26906] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [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: 03/20/2017] [Revised: 08/17/2017] [Accepted: 08/17/2017] [Indexed: 12/24/2022]
Abstract
PURPOSE The combination of 3D echo planar imaging (3D-EPI) with a 2D-CAIPIRINHA undersampling scheme provides high flexibility in the optimization for spatial or temporal resolution. This flexibility can be increased further with the addition of a cylindrical excitation pulse, which exclusively excites the brain regions of interest. Here, 3D-EPI was combined with a 2D radiofrequency pulse to reduce the brain area from which signal is generated, and hence, allowing either reduction of the field of view or reduction of parallel imaging noise amplification. METHODS 3D-EPI with cylindrical excitation and 4 × 3-fold undersampling in a 2D-CAIPIRINHA sampling scheme was used to generate functional MRI (fMRI) data with either 2-mm or 0.9-mm in-plane resolution and 1.1-s temporal resolution over a 5-cm diameter cylinder placed over both temporal lobes for an auditory fMRI experiment. RESULTS Significant increases in image signal-to-noise ratio (SNR) and temporal SNR (tSNR) were found for both 2-mm isotropic data and the high-resolution protocol when using the cylindrical excitation pulse. Both protocols yielded highly significant blood oxygenation level-dependent responses for the presentation of natural sounds. CONCLUSION The higher tSNR of the cylindrical excitation 3D-EPI data makes this sequence an ideal choice for high spatiotemporal resolution fMRI acquisitions. Magn Reson Med 79:2589-2596, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Wietske van der Zwaag
- Spinoza Centre for Neuroimaging, Amsterdam, Netherlands.,Centre d'Imagerie BioMédicale, EPFL, Lausanne, Switzerland
| | | | | | - Daniel Gallichan
- Centre d'Imagerie BioMédicale, EPFL, Lausanne, Switzerland.,Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK
| | - José P Marques
- Donders Institute for Brain Behaviour and Cognition, Radboud University, Nijmegen, Netherlands
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Wilhelm RA, Gruber E, Schwestka J, Kozubek R, Madeira TI, Marques JP, Kobus J, Krasheninnikov AV, Schleberger M, Aumayr F. Interatomic Coulombic Decay: The Mechanism for Rapid Deexcitation of Hollow Atoms. Phys Rev Lett 2017; 119:103401. [PMID: 28949190 DOI: 10.1103/physrevlett.119.103401] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Indexed: 05/23/2023]
Abstract
The impact of a highly charged ion onto a solid gives rise to charge exchange between the ion and target atoms, so that a slow ion gets neutralized in the vicinity of the surface. Using highly charged Ar and Xe ions and the surface-only material graphene as a target, we show that the neutralization and deexcitation of the ions proceeds on a sub-10 fs time scale. We further demonstrate that a multiple Interatomic Coulombic Decay (ICD) model can describe the observed ultrafast deexcitation. Other deexcitation mechanisms involving nonradiative decay and quasimolecular orbital formation during the impact are not important, as follows from the comparison of our experimental data with the results of first-principles calculations. Our method also enables the estimation of ICD rates directly.
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Affiliation(s)
- Richard A Wilhelm
- TU Wien, Institute of Applied Physics, Wiedner Hauptstrasse 8-10, 1040 Vienna, Austria, EU
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Ion Beam Physics and Materials Research, Bautzner Landstrasse 400, 01328 Dresden, Germany, EU
| | - Elisabeth Gruber
- TU Wien, Institute of Applied Physics, Wiedner Hauptstrasse 8-10, 1040 Vienna, Austria, EU
| | - Janine Schwestka
- TU Wien, Institute of Applied Physics, Wiedner Hauptstrasse 8-10, 1040 Vienna, Austria, EU
| | - Roland Kozubek
- University Duisburg-Essen, Faculty of Physics and CENIDE, Lotharstrasse 1, 47048 Duisburg, Germany, EU
| | - Teresa I Madeira
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Ion Beam Physics and Materials Research, Bautzner Landstrasse 400, 01328 Dresden, Germany, EU
| | - José P Marques
- BioISI-Biosystems & Integrative Sciences Institute, Faculdade de Ciéncias da Universidade de Lisboa, 1749-016 Lisbon, Portugal, EU
| | - Jacek Kobus
- Nicolaus Copernicus University, Faculty of Physics, Astronomy and Informatics, Institute of Physics, Grudziądzka 5, 87-100 Toruń, Poland, EU
| | - Arkady V Krasheninnikov
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Ion Beam Physics and Materials Research, Bautzner Landstrasse 400, 01328 Dresden, Germany, EU
| | - Marika Schleberger
- University Duisburg-Essen, Faculty of Physics and CENIDE, Lotharstrasse 1, 47048 Duisburg, Germany, EU
| | - Friedrich Aumayr
- TU Wien, Institute of Applied Physics, Wiedner Hauptstrasse 8-10, 1040 Vienna, Austria, EU
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46
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Hartogsveld B, Bramson B, Vijayakumar S, van Campen AD, Marques JP, Roelofs K, Toni I, Bekkering H, Mars RB. Lateral frontal pole and relational processing: Activation patterns and connectivity profile. Behav Brain Res 2017; 355:2-11. [PMID: 28811179 DOI: 10.1016/j.bbr.2017.08.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [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: 01/17/2017] [Revised: 06/06/2017] [Accepted: 08/02/2017] [Indexed: 01/23/2023]
Abstract
The functional contribution of the lateral frontal cortex to behavior has been discussed with reference to several higher-order cognitive domains. In a separate line of research, recent studies have focused on the anatomical organization of this part of the brain. These different approaches are rarely combined. Here, we combine previous work using anatomical connectivity that identified a lateral subdivision of the human frontal pole and work that suggested a general role for rostrolateral prefrontal cortex in processing higher-order relations, irrespective of the type of information. We asked healthy human volunteers to judge the relationship between pairs of stimuli, a task previously suggested to engage the lateral frontal pole. Presenting both shape and face stimuli, we indeed observed overlapping activation of the lateral prefrontal cortex when subjects judged relations between pairs. Using resting state functional MRI, we confirmed that the activated region's whole-brain connectivity most strongly resembles that of the lateral frontal pole. Using diffusion MRI, we showed that the pattern of connections of this region with the main association fibers again is most similar to that of the lateral frontal pole, consistent with the observation that it is this anatomical region that is involved in relational processing.
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Affiliation(s)
- Bart Hartogsveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EZ Nijmegen, The Netherlands
| | - Bob Bramson
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EZ Nijmegen, The Netherlands
| | - Suhas Vijayakumar
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EZ Nijmegen, The Netherlands
| | - A Dilene van Campen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EZ Nijmegen, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EZ Nijmegen, The Netherlands
| | - Karin Roelofs
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EZ Nijmegen, The Netherlands
| | - Ivan Toni
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EZ Nijmegen, The Netherlands
| | - Harold Bekkering
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EZ Nijmegen, The Netherlands
| | - Rogier B Mars
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EZ Nijmegen, The Netherlands; Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, United Kingdom.
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47
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Reynaud O, Jorge J, Gruetter R, Marques JP, van der Zwaag W. Influence of physiological noise on accelerated 2D and 3D resting state functional MRI data at 7 T. Magn Reson Med 2017; 78:888-896. [PMID: 28686788 DOI: 10.1002/mrm.26823] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [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: 03/28/2017] [Revised: 06/06/2017] [Accepted: 06/13/2017] [Indexed: 12/28/2022]
Abstract
PURPOSE Physiological noise often dominates the blood-oxygen level-dependent (BOLD) signal fluctuations in high-field functional MRI (fMRI) data. Therefore, to optimize fMRI protocols, it becomes crucial to investigate how physiological signal fluctuations impact various acquisition and reconstruction schemes at different acquisition speeds. In particular, further differences can arise between 2D and 3D fMRI acquisitions due to different encoding strategies, thereby impacting fMRI sensitivity in potentially significant ways. METHODS The amount of physiological noise to be removed from the BOLD fMRI signal acquired at 7 T was quantified for different sampling rates (repetition time from 3300 to 350 ms, acceleration 1 to 8) and techniques dedicated to fast fMRI (simultaneous multislice echo planar imaging [EPI] and 3D EPI). Resting state fMRI (rsfMRI) performances were evaluated using temporal signal-to-noise ratio (tSNR) and network characterization based on seed correlation and independent component analysis. RESULTS Overall, acceleration enhanced tSNR and rsfMRI metrics. 3D EPI benefited the most from physiological noise removal at long repetition times. Differences between 2D and 3D encoding strategies disappeared at high acceleration factors (6- to 8-fold). CONCLUSION After physiological noise correction, 2D- and 3D-accelerated sequences provide similar performances at high fields, both in terms of tSNR and resting state network identification and characterization. Magn Reson Med 78:888-896, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Olivier Reynaud
- Centre d'Imagerie Biomédicale, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - João Jorge
- Laboratoire for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Rolf Gruetter
- Centre d'Imagerie Biomédicale, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Laboratoire for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Department of Radiology, University of Geneva, Geneva, Switzerland.,Department of Radiology, University of Lausanne, Lausanne, Switzerland
| | - José P Marques
- Centre d'Imagerie Biomédicale, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Donders Institute for Brain Behaviour and Cognition, Nijmegen, Netherlands
| | - Wietske van der Zwaag
- Centre d'Imagerie Biomédicale, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Spinoza Centre for Neuroimaging, Amsterdam, Netherlands
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Robinson SD, Bredies K, Khabipova D, Dymerska B, Marques JP, Schweser F. An illustrated comparison of processing methods for MR phase imaging and QSM: combining array coil signals and phase unwrapping. NMR Biomed 2017; 30:e3601. [PMID: 27619999 PMCID: PMC5348291 DOI: 10.1002/nbm.3601] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Revised: 06/14/2016] [Accepted: 07/18/2016] [Indexed: 05/11/2023]
Abstract
Phase imaging benefits from strong susceptibility effects at very high field and the high signal-to-noise ratio (SNR) afforded by multi-channel coils. Combining the information from coils is not trivial, however, as the phase that originates in local field effects (the source of interesting contrast) is modified by the inhomogeneous sensitivity of each coil. This has historically been addressed by referencing individual coil sensitivities to that of a volume coil, but alternative approaches are required for ultra-high field systems in which no such coil is available. An additional challenge in phase imaging is that the phase that develops up to the echo time is "wrapped" into a range of 2π radians. Phase wraps need to be removed in order to reveal the underlying phase distribution of interest. Beginning with a coil combination using a homogeneous reference volume coil - the Roemer approach - which can be applied at 3 T and lower field strengths, we review alternative methods for combining single-echo and multi-echo phase images where no such reference coil is available. These are applied to high-resolution data acquired at 7 T and their effectiveness assessed via an index of agreement between phase values over channels and the contrast-to-noise ratio in combined images. The virtual receiver coil and COMPOSER approaches were both found to be computationally efficient and effective. The main features of spatial and temporal phase unwrapping methods are reviewed, placing particular emphasis on recent developments in temporal phase unwrapping and Laplacian approaches. The features and performance of these are illustrated in application to simulated and high-resolution in vivo data. Temporal unwrapping was the fastest of the methods tested and the Laplacian the most robust in images with low SNR. © 2016 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.
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Affiliation(s)
- Simon Daniel Robinson
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Kristian Bredies
- Institute of Mathematics and Scientific Computing, University of Graz, Austria
| | - Diana Khabipova
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Switzerland
- Donders Institute for Brain, Cognition and Behaviou, Radboud University Nijmegen, The Netherlands
| | - Barbara Dymerska
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - José P Marques
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Switzerland
- Donders Institute for Brain, Cognition and Behaviou, Radboud University Nijmegen, The Netherlands
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, New York, USA
- MRI Clinical and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, New York, USA
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Jorge J, Gretsch F, Gallichan D, Marques JP. Tracking discrete off-resonance markers with three spokes (trackDOTS) for compensation of head motion and B0
perturbations: Accuracy and performance in anatomical imaging. Magn Reson Med 2017; 79:160-171. [DOI: 10.1002/mrm.26654] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 02/03/2017] [Accepted: 02/03/2017] [Indexed: 01/29/2023]
Affiliation(s)
- João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne; Lausanne Switzerland
| | - Frédéric Gretsch
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne; Lausanne Switzerland
| | - Daniel Gallichan
- Biomedical Imaging Research Center, École Polytechnique Fédérale de Lausanne; Lausanne Switzerland
| | - José P. Marques
- Donders Institute; Radboud University; Nijmegen the Netherlands
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Marques JP, Khabipova D, Gruetter R. Studying cyto and myeloarchitecture of the human cortex at ultra-high field with quantitative imaging: R1, R2* and magnetic susceptibility. Neuroimage 2017; 147:152-163. [DOI: 10.1016/j.neuroimage.2016.12.009] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 11/30/2016] [Accepted: 12/05/2016] [Indexed: 12/15/2022] Open
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