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Tang S, Harrison DM, Bardhoshi A, Cureton R, Yan X, Parcon PA, Morse CL, Ecker C, Choi S, Pike VW, Innis RB, Zanotti-Fregonara P. Cyclooxygenase-1 and cyclooxygenase-2 densities measured using positron emission tomography are not altered in the brains of individuals with stable multiple sclerosis. J Cereb Blood Flow Metab 2025:271678X251332490. [PMID: 40367389 PMCID: PMC12078256 DOI: 10.1177/0271678x251332490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2024] [Revised: 03/03/2025] [Accepted: 03/14/2025] [Indexed: 05/16/2025]
Abstract
Multiple sclerosis (MS) is a chronic inflammatory disease affecting the central nervous system that involves immune-mediated demyelination and axonal degeneration. Clinical imaging techniques play a critical role in diagnosing and assessing the prognosis of MS. Magnetic resonance imaging has been most frequently used to visualize demyelination and detect acute and chronic active lesions, which are key indicators of clinical course of illness. Previous research has also highlighted the effectiveness of translocator protein 18-kDa (TSPO) positron emission tomography (PET) imaging for identifying chronic active lesions and progressive pathology. Building on this work, the present study used PET imaging to explore the role of cyclooxygenase-1 and -2 (COX-1 and COX-2)-key enzymes involved in neuroinflammation-in individuals with MS. Five participants with MS were recruited, and lesions were identified using 7 Tesla MRI. No significant differences in COX radioligand binding were observed in the co-registered PET images between lesioned areas and normal-appearing brain tissues, nor between individuals with MS and healthy volunteers. The negative findings underscore the complexity of MS pathology and raise several important considerations for planning future studies using COX PET for imaging in MS.
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Affiliation(s)
- Shiyu Tang
- Molecular Imaging Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Daniel M Harrison
- Dept of Neurology, University of Maryland School of Medicine, Baltimore, Maryland, USA
- Dept of Neurology, Baltimore Veterans Affairs Medical Center, Baltimore, Maryland, USA
| | - Amanda Bardhoshi
- Molecular Imaging Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Raven Cureton
- Molecular Imaging Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Xuefeng Yan
- Molecular Imaging Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Paul A Parcon
- Molecular Imaging Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Cheryl L Morse
- Molecular Imaging Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Christina Ecker
- Dept of Neurology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Seongjin Choi
- Dept of Neurology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Victor W Pike
- Molecular Imaging Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Robert B Innis
- Molecular Imaging Branch, National Institute of Mental Health, Bethesda, Maryland, USA
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Harrison DM, Choi S, Bakshi R, Beck ES, Callen AM, Chu R, Silva JDS, Fetco D, Greenwald M, Kolind S, Narayanan S, Okar SV, Quattrucci MK, Reich DS, Rudko D, Russell‐Schulz B, Schindler MK, Tauhid S, Traboulsee A, Vavasour Z, Zurawski JD. Pooled analysis of multiple sclerosis findings on multisite 7 Tesla MRI: Protocol and initial observations. Hum Brain Mapp 2024; 45:e26816. [PMID: 39169546 PMCID: PMC11339124 DOI: 10.1002/hbm.26816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/26/2024] [Accepted: 07/30/2024] [Indexed: 08/23/2024] Open
Abstract
Although 7 T MRI research has contributed much to our understanding of multiple sclerosis (MS) pathology, most prior data has come from small, single-center studies with varying methods. In order to truly know if such findings have widespread applicability, multicenter methods and studies are needed. To address this, members of the North American Imaging in MS (NAIMS) Cooperative worked together to create a multicenter collaborative study of 7 T MRI in MS. In this manuscript, we describe the methods we have developed for the purpose of pooling together a large, retrospective dataset of 7 T MRIs acquired in multiple MS studies at five institutions. To date, this group has contributed five-hundred and twenty-eight 7 T MRI scans from 350 individuals with MS to a common data repository, with plans to continue to increase this sample size in the coming years. We have developed unified methods for image processing for data harmonization and lesion identification/segmentation. We report here our initial observations on intersite differences in acquisition, which includes site/device differences in brain coverage and image quality. We also report on the development of our methods and training of image evaluators, which resulted in median Dice Similarity Coefficients for trained raters' annotation of cortical and deep gray matter lesions, paramagnetic rim lesions, and meningeal enhancement between 0.73 and 0.82 compared to final consensus masks. We expect this publication to act as a resource for other investigators aiming to combine multicenter 7 T MRI datasets for the study of MS, in addition to providing a methodological reference for all future analysis projects to stem from the development of this dataset.
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Affiliation(s)
- Daniel M. Harrison
- Department of NeurologyUniversity of Maryland School of MedicineBaltimoreMarylandUSA
- Department of NeurologyBaltimore VA Medical Center, VA Maryland Healthcare SystemBaltimoreMarylandUSA
| | - Seongjin Choi
- Department of NeurologyUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Rohit Bakshi
- Department of NeurologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Erin S. Beck
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Translational Neuroradiology SectionNational Institute of Neurological Disorders and StrokeBethesdaMarylandUSA
| | - Alexis M. Callen
- Department of NeurologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Renxin Chu
- Department of NeurologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | | | - Dumitru Fetco
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Department of Neurology and NeurosurgeryMcGill UniversityMontrealQuebecCanada
- NeuroRx ResearchMontrealQuebecCanada
| | - Matthew Greenwald
- Translational Neuroradiology SectionNational Institute of Neurological Disorders and StrokeBethesdaMarylandUSA
| | - Shannon Kolind
- Department of Medicine (Neurology)University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Sridar Narayanan
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Department of Neurology and NeurosurgeryMcGill UniversityMontrealQuebecCanada
- NeuroRx ResearchMontrealQuebecCanada
| | - Serhat V. Okar
- Translational Neuroradiology SectionNational Institute of Neurological Disorders and StrokeBethesdaMarylandUSA
| | - Molly K. Quattrucci
- Department of NeurologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Daniel S. Reich
- Translational Neuroradiology SectionNational Institute of Neurological Disorders and StrokeBethesdaMarylandUSA
| | - David Rudko
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Department of Neurology and NeurosurgeryMcGill UniversityMontrealQuebecCanada
| | - Bretta Russell‐Schulz
- Department of Medicine (Neurology)University of British ColumbiaVancouverBritish ColumbiaCanada
| | | | - Shahamat Tauhid
- Department of NeurologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Anthony Traboulsee
- Department of Medicine (Neurology)University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Zachary Vavasour
- Department of Medicine (Neurology)University of British ColumbiaVancouverBritish ColumbiaCanada
| | - Jonathan D. Zurawski
- Department of NeurologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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3
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Serrarens C, Ruiz-Fernandez J, Otter M, Campforts BCM, Stumpel CTRM, Linden DEJ, van Amelsvoort TAMJ, Kashyap S, Vingerhoets C. Intracortical myelin across laminae in adult individuals with 47,XXX: a 7 Tesla MRI study. Cereb Cortex 2024; 34:bhae343. [PMID: 39183364 PMCID: PMC11345119 DOI: 10.1093/cercor/bhae343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/31/2024] [Accepted: 08/12/2024] [Indexed: 08/27/2024] Open
Abstract
47,XXX (Triple X syndrome) is a sex chromosome aneuploidy characterized by the presence of a supernumerary X chromosome in affected females and is associated with a variable cognitive, behavioral, and psychiatric phenotype. The effect of a supernumerary X chromosome in affected females on intracortical microstructure is currently unknown. Therefore, we conducted 7 Tesla structural MRI and compared T1 (ms), as a proxy for intracortical myelin (ICM), across laminae of 21 adult women with 47,XXX and 22 age-matched typically developing females using laminar analyses. Relationships between phenotypic traits and T1 values in 47,XXX were also investigated. Adults with 47,XXX showed higher bilateral T1 across supragranular laminae in the banks of the superior temporal sulcus, and in the right inferior temporal gyrus, suggesting decreases of ICM primarily within the temporal cortex in 47,XXX. Higher social functioning in 47,XXX was related to larger inferior temporal gyrus ICM content. Our findings indicate an effect of a supernumerary X chromosome in adult-aged women on ICM across supragranular laminae within the temporal cortex. These findings provide insight into the role of X chromosome dosage on ICM across laminae. Future research is warranted to further explore the functional significance of altered ICM across laminae in 47,XXX.
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Affiliation(s)
- Chaira Serrarens
- Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience Institute (MHeNS), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Julia Ruiz-Fernandez
- Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience Institute (MHeNS), Maastricht University, Maastricht, 6200 MD, The Netherlands
- INSERM U1299, Centre Borelli UMR 9010, ENS-Paris-Saclay, Université Paris Saclay, Paris, France
| | - Maarten Otter
- Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience Institute (MHeNS), Maastricht University, Maastricht, 6200 MD, The Netherlands
- Medical Department, SIZA, Arnhem, 6800 AM, The Netherlands
| | - Bea C M Campforts
- Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience Institute (MHeNS), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Constance T R M Stumpel
- Department of Clinical Genetics and School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, 6229 ER, The Netherlands
| | - David E J Linden
- Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience Institute (MHeNS), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Therese A M J van Amelsvoort
- Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience Institute (MHeNS), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Sriranga Kashyap
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, 6229 EV, The Netherlands
- Krembil Brain Institute, University Health Network, Toronto, ON M5T 2S8, Canada
| | - Claudia Vingerhoets
- Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience Institute (MHeNS), Maastricht University, Maastricht, 6200 MD, The Netherlands
- ‘s Heeren Loo Zorggroep, Amersfoort, 3818 LA, The Netherlands
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4
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Huerta MM, Conway DS, Planchon SM, Thoomukuntla B, Se-Hong O, Sakaie KE, Ontaneda D, Nakamura K. Longitudinal myelin content measures of slowly expanding lesions using 7T MRI in multiple sclerosis. J Neuroimaging 2024; 34:451-458. [PMID: 38778455 DOI: 10.1111/jon.13209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND AND PURPOSE Slowly expanding lesions (SELs) are thought to represent a subset of chronic active lesions and have been associated with clinical disability, severity, and disease progression. The purpose of this study was to characterize SELs using advanced magnetic resonance imaging (MRI) measures related to myelin and neurite density on 7 Tesla (T) MRI. METHODS The study design was retrospective, longitudinal, observational cohort with multiple sclerosis (n = 15). Magnetom 7T scanner was used to acquire magnetization-prepared 2 rapid acquisition gradient echo and advanced MRI including visualization of short transverse relaxation time component (ViSTa) for myelin, quantitative magnetization transfer (qMT) for myelin, and neurite orientation dispersion density imaging (NODDI). SELs were defined as lesions showing ≥12% of growth over 12 months on serial MRI. Comparisons of quantitative measures in SELs and non-SELs were performed at baseline and over time. Statistical analyses included two-sample t-test, analysis of variance, and mixed-effects linear model for MRI metrics between lesion types. RESULTS A total of 1075 lesions were evaluated. Two hundred twenty-four lesions (21%) were SELs, and 216 (96%) of the SELs were black holes. At baseline, compared to non-SELs, SELs showed significantly lower ViSTa (1.38 vs. 1.53, p < .001) and qMT (2.47 vs. 2.97, p < .001) but not in NODDI measures (p > .27). Longitudinally, only ViSTa showed a greater loss when comparing SEL and non-SEL (p = .03). CONCLUSIONS SELs have a lower myelin content relative to non-SELs without a difference in neurite measures. SELs showed a longitudinal decrease in apparent myelin water fraction reflecting greater tissue injury.
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Affiliation(s)
- Mina M Huerta
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Devon S Conway
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Sarah M Planchon
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | | | - Oh Se-Hong
- Department of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea
| | - Ken E Sakaie
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis Treatment and Research, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Kunio Nakamura
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, USA
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5
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Romascano D, Piredda GF, Caneschi S, Hilbert T, Corredor R, Maréchal B, Kober T, Ledoux JB, Fornari E, Hagmann P, Denervaud S. Normative volumes and relaxation times at 3T during brain development. Sci Data 2024; 11:429. [PMID: 38664431 PMCID: PMC11045735 DOI: 10.1038/s41597-024-03267-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
While research has unveiled and quantified brain markers of abnormal neurodevelopment, clinicians still work with qualitative metrics for MRI brain investigation. The purpose of the current article is to bridge the knowledge gap between case-control cohort studies and individual patient care. Here, we provide a unique dataset of seventy-three 3-to-17 years-old healthy subjects acquired with a 6-minute MRI protocol encompassing T1 and T2 relaxation quantitative sequence that can be readily implemented in the clinical setting; MP2RAGE for T1 mapping and the prototype sequence GRAPPATINI for T2 mapping. White matter and grey matter volumes were automatically quantified. We further provide normative developmental curves based on these two imaging sequences; T1, T2 and volume normative ranges with respect to age were computed, for each ROI of a pediatric brain atlas. This open-source dataset provides normative values allowing to position individual patients acquired with the same protocol on the brain maturation curve and as such provides potentially useful quantitative biomarkers facilitating precise and personalized care.
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Affiliation(s)
- David Romascano
- Department of Radiology, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland.
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland.
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark.
| | - Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Samuele Caneschi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Signal Processing laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Department of Radiology, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Signal Processing laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ricardo Corredor
- Department of Radiology, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Signal Processing laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bénédicte Maréchal
- Department of Radiology, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Signal Processing laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Department of Radiology, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Signal Processing laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jean-Baptiste Ledoux
- Department of Radiology, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | | | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
| | - Solange Denervaud
- Department of Radiology, Lausanne University Hospital and University of Lausanne, 1011, Lausanne, Switzerland
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6
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Bian B, Hou L, Chai Y, Jiang Y, Pan X, Sun Y, Wang H, Qiu D, Yu Z, Zhao H, Zhang H, Meng F, Zhang L. Visualizing the Habenula Using 3T High-Resolution MP2RAGE and QSM: A Preliminary Study. AJNR Am J Neuroradiol 2024; 45:504-510. [PMID: 38453416 PMCID: PMC11288573 DOI: 10.3174/ajnr.a8156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 12/18/2023] [Indexed: 03/09/2024]
Abstract
BACKGROUND AND PURPOSE The habenula is a key node in the regulation of emotion-related behavior. Accurate visualization of the habenula and its reliable quantitative analysis is vital for the assessment of psychiatric disorders. To obtain high-contrast habenula images and allow them to be compatible with clinical applications, this preliminary study compared 3T MP2RAGE and quantitative susceptibility mapping with MPRAGE by evaluating the habenula segmentation performance. MATERIALS AND METHODS Ten healthy volunteers were scanned twice with 3T MPRAGE and MP2RAGE and once with quantitative susceptibility mapping. Image quality and visibility of habenula anatomic features were analyzed by 3 radiologists using a 5-point scale. Contrast assessments of the habenula and thalamus were also performed. The reproducibility of the habenula volume from MPRAGE and MP2RAGE was evaluated by manual segmentation and the Multiple Automatically Generated Template brain segmentation algorithm (MAGeTbrain). T1 values and susceptibility were measured in the whole habenula and habenula geometric subregion using MP2RAGE T1-mapping and quantitative susceptibility mapping. RESULTS The 3T MP2RAGE and quantitative susceptibility mapping demonstrated clear boundaries and anatomic features of the habenula compared with MPRAGE, with a higher SNR and contrast-to-noise ratio (all P < .05). Additionally, 3T MP2RAGE provided reliable habenula manual and MAGeTbrain segmentation volume estimates with greater reproducibility. T1-mapping derived from MP2RAGE was highly reliable, and susceptibility contrast was highly nonuniform within the habenula. CONCLUSIONS We identified an optimized sequence combination (3T MP2RAGE combined with quantitative susceptibility mapping) that may be useful for enhancing habenula visualization and yielding more reliable quantitative data.
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Affiliation(s)
- BingYang Bian
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Lin Hou
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - YaTing Chai
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - YueLuan Jiang
- MR Scientific Marketing, Diagnostic Imaging (Y.J.), Siemens Healthineers Ltd, Beijing, China
| | - XingChen Pan
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Yang Sun
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - HongChao Wang
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - DongDong Qiu
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - ZeChen Yu
- Siemens Healthineers Digital Technology (Shanghai) Co Ltd (Z.Y.), Shanghai, China
| | - Hua Zhao
- Department of Physiology (H. Zhao), College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
| | - HuiMao Zhang
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - FanYang Meng
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Lei Zhang
- From the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China
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7
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Kronlage C, Heide EC, Hagberg GE, Bender B, Scheffler K, Martin P, Focke N. MP2RAGE vs. MPRAGE surface-based morphometry in focal epilepsy. PLoS One 2024; 19:e0296843. [PMID: 38330027 PMCID: PMC10852321 DOI: 10.1371/journal.pone.0296843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/19/2023] [Indexed: 02/10/2024] Open
Abstract
In drug-resistant focal epilepsy, detecting epileptogenic lesions using MRI poses a critical diagnostic challenge. Here, we assessed the utility of MP2RAGE-a T1-weighted sequence with self-bias correcting properties commonly utilized in ultra-high field MRI-for the detection of epileptogenic lesions using a surface-based morphometry pipeline based on FreeSurfer, and compared it to the common approach using T1w MPRAGE, both at 3T. We included data from 32 patients with focal epilepsy (5 MRI-positive, 27 MRI-negative with lobar seizure onset hypotheses) and 94 healthy controls from two epilepsy centres. Surface-based morphological measures and intensities were extracted and evaluated in univariate GLM analyses as well as multivariate unsupervised 'novelty detection' machine learning procedures. The resulting prediction maps were analyzed over a range of possible thresholds using alternative free-response receiver operating characteristic (AFROC) methodology with respect to the concordance with predefined lesion labels or hypotheses on epileptogenic zone location. We found that MP2RAGE performs at least comparable to MPRAGE and that especially analysis of MP2RAGE image intensities may provide additional diagnostic information. Secondly, we demonstrate that unsupervised novelty-detection machine learning approaches may be useful for the detection of epileptogenic lesions (maximum AFROC AUC 0.58) when there is only a limited lesional training set available. Third, we propose a statistical method of assessing lesion localization performance in MRI-negative patients with lobar hypotheses of the epileptogenic zone based on simulation of a random guessing process as null hypothesis. Based on our findings, it appears worthwhile to study similar surface-based morphometry approaches in ultra-high field MRI (≥ 7 T).
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Affiliation(s)
- Cornelius Kronlage
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | - Ev-Christin Heide
- Clinic of Neurology, University Medical Center Goettingen, Goettingen, Germany
| | - Gisela E. Hagberg
- High-Field MR Centre, Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany
- Department for Biomedical Magnetic Resonances, University of Tuebingen, Tuebingen, Germany
| | - Benjamin Bender
- Department of Neuroradiology, University of Tuebingen, Tuebingen, Germany
| | - Klaus Scheffler
- High-Field MR Centre, Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany
- Department for Biomedical Magnetic Resonances, University of Tuebingen, Tuebingen, Germany
| | - Pascal Martin
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | - Niels Focke
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
- Clinic of Neurology, University Medical Center Goettingen, Goettingen, Germany
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8
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Doose A, Tam FI, Hellerhoff I, King JA, Boehm I, Gottloeber K, Wahl H, Werner A, Raschke F, Bartnik-Olson B, Lin AP, Akgün K, Roessner V, Linn J, Ehrlich S. Triangulating brain alterations in anorexia nervosa: a multimodal investigation of magnetic resonance spectroscopy, morphometry and blood-based biomarkers. Transl Psychiatry 2023; 13:277. [PMID: 37573444 PMCID: PMC10423271 DOI: 10.1038/s41398-023-02580-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 08/14/2023] Open
Abstract
The acute state of anorexia nervosa (AN) is associated with widespread reductions in cortical gray matter (GM) thickness and white matter (WM) volume, suspected changes in myelin content and elevated levels of the neuronal damage marker neurofilament light (NF-L), but the underlying mechanisms remain largely unclear. To gain a deeper understanding of brain changes in AN, we applied a multimodal approach combining advanced neuroimaging methods with analysis of blood-derived biomarkers. In addition to standard measures of cortical GM thickness and WM volume, we analyzed tissue-specific profiles of brain metabolites using multivoxel proton magnetic resonance spectroscopy, T1 relaxation time as a proxy of myelin content leveraging advanced quantitative MRI methods and serum NF-L concentrations in a sample of 30 female, predominately adolescent patients with AN and 30 age-matched female healthy control participants. In patients with AN, we found a reduction in GM cortical thickness and GM total N-acetyl aspartate. The latter predicted higher NF-L levels, which were elevated in AN. Furthermore, GM total choline was elevated. In WM, there were no group differences in either imaging markers, choline levels or N-acetyl aspartate levels. The current study provides evidence for neuronal damage processes as well as for increased membrane lipid catabolism and turnover in GM in acute AN but no evidence for WM pathology. Our results illustrate the potential of multimodal research including tissue-specific proton magnetic resonance spectroscopy analyses to shed light on brain changes in psychiatric and neurological conditions, which may ultimately lead to better treatments.
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Affiliation(s)
- Arne Doose
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neuroscience, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Friederike I Tam
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neuroscience, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- Eating Disorder Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Inger Hellerhoff
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neuroscience, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Joseph A King
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neuroscience, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Ilka Boehm
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neuroscience, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Kim Gottloeber
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neuroscience, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Hannes Wahl
- Department of Neuroradiology, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Annett Werner
- Department of Neuroradiology, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Felix Raschke
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
| | - Brenda Bartnik-Olson
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Alexander P Lin
- Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Katja Akgün
- Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Veit Roessner
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Jennifer Linn
- Department of Neuroradiology, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neuroscience, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
- Eating Disorder Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
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9
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Elliott ML, Hanford LC, Hamadeh A, Hilbert T, Kober T, Dickerson BC, Mair RW, Eldaief MC, Buckner RL. Brain morphometry in older adults with and without dementia using extremely rapid structural scans. Neuroimage 2023; 276:120173. [PMID: 37201641 PMCID: PMC10330834 DOI: 10.1016/j.neuroimage.2023.120173] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/25/2023] [Accepted: 05/15/2023] [Indexed: 05/20/2023] Open
Abstract
T1-weighted structural MRI is widely used to measure brain morphometry (e.g., cortical thickness and subcortical volumes). Accelerated scans as fast as one minute or less are now available but it is unclear if they are adequate for quantitative morphometry. Here we compared the measurement properties of a widely adopted 1.0 mm resolution scan from the Alzheimer's Disease Neuroimaging Initiative (ADNI = 5'12'') with two variants of highly accelerated 1.0 mm scans (compressed-sensing, CSx6 = 1'12''; and wave-controlled aliasing in parallel imaging, WAVEx9 = 1'09'') in a test-retest study of 37 older adults aged 54 to 86 (including 19 individuals diagnosed with a neurodegenerative dementia). Rapid scans produced highly reliable morphometric measures that largely matched the quality of morphometrics derived from the ADNI scan. Regions of lower reliability and relative divergence between ADNI and rapid scan alternatives tended to occur in midline regions and regions with susceptibility-induced artifacts. Critically, the rapid scans yielded morphometric measures similar to the ADNI scan in regions of high atrophy. The results converge to suggest that, for many current uses, extremely rapid scans can replace longer scans. As a final test, we explored the possibility of a 0'49'' 1.2 mm CSx6 structural scan, which also showed promise. Rapid structural scans may benefit MRI studies by shortening the scan session and reducing cost, minimizing opportunity for movement, creating room for additional scan sequences, and allowing for the repetition of structural scans to increase precision of the estimates.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology, Center for Brain Science, Harvard University, 52 Oxford Street, Northwest Laboratory 280.10, Cambridge, MA 02138, USA.
| | - Lindsay C Hanford
- Department of Psychology, Center for Brain Science, Harvard University, 52 Oxford Street, Northwest Laboratory 280.10, Cambridge, MA 02138, USA
| | - Aya Hamadeh
- Baylor College of Medicine, Houston, TX 77030, USA
| | - 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
| | - Tobias Kober
- 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
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit, Massachusetts General Hospital, USA; Alzheimer's Disease Research Center, Massachusetts General Hospital, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA; Department of Neurology, Massachusetts General Hospital & Harvard Medical School, USA; Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Ross W Mair
- Department of Psychology, Center for Brain Science, Harvard University, 52 Oxford Street, Northwest Laboratory 280.10, Cambridge, MA 02138, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
| | - Mark C Eldaief
- Frontotemporal Disorders Unit, Massachusetts General Hospital, USA; Alzheimer's Disease Research Center, Massachusetts General Hospital, USA; Department of Neurology, Massachusetts General Hospital & Harvard Medical School, USA; Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, 52 Oxford Street, Northwest Laboratory 280.10, Cambridge, MA 02138, USA; Alzheimer's Disease Research Center, Massachusetts General Hospital, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA; Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
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10
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Patel LD, Raghavan P, Tang S, Choi S, Harrison DM. Imaging of the meningeal lymphatic network in healthy adults: A 7T MRI study. J Neuroradiol 2023; 50:369-376. [PMID: 36918053 PMCID: PMC10981496 DOI: 10.1016/j.neurad.2023.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/08/2023] [Accepted: 03/08/2023] [Indexed: 03/13/2023]
Abstract
BACKGROUND AND PURPOSE Meningeal lymphatic vessels (MLVs) along the dural venous sinuses are suspected to be important in connecting the glymphatic and peripheral lymphatic system. Understanding the topography of MLVs may clarify the role of the glymphatic system in neurological diseases. The aim of this analysis was to use high resolution pre- and post-contrast FLAIR 7T MRI to identify and characterize the morphology of MLV in a cohort of healthy volunteers. MATERIALS AND METHODS MRI examinations of seventeen healthy volunteers enrolled as controls in a larger 7T MRI study were reviewed. Pre- and post-contrast 3-D FLAIR subtractions and MP2RAGE sequences were spatially normalized and reviewed for signal intensity and enhancement patterns within putative MLVs along pre-determined dural and venous structures. Frequency of occurrence of MLVs at the above-described locations and patterns of their enhancement were analyzed. RESULTS Putative MLVs are commonly located along the superior sagittal sinus (SSS) and cortical veins. A "fixed enhancement" signal pattern was more frequent at these locations (p<.05). The morphology of MLVs along the SSS qualitatively changes in an antero-posterior direction. Lack of signal was more frequent along the straight and transverse sinuses (p<.05). CONCLUSION Putative MLVs in healthy individuals are concentrated along the SSS and cortical veins. FLAIR signal and enhancement characteristics suggest these structures may transport proteinaceous fluid. Pathways connecting MLVs to cervical lymph nodes however remain unclear.
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Affiliation(s)
- Lakir D Patel
- University of Maryland School of Medicine, Department of Diagnostic Radiology and Nuclear Medicine, Baltimore, Maryland, USA.
| | - Prashant Raghavan
- University of Maryland School of Medicine, Department of Diagnostic Radiology and Nuclear Medicine, Baltimore, Maryland, USA.
| | - Shiyu Tang
- University of Maryland School of Medicine, Department of Diagnostic Radiology and Nuclear Medicine, Baltimore, Maryland, USA; University of Maryland School of Medicine, Center for Advanced Imaging Research (CAIR), Baltimore, Maryland, USA.
| | - Seongjin Choi
- University of Maryland School of Medicine, Department of Neurology, Baltimore, Maryland, USA.
| | - Daniel M Harrison
- University of Maryland School of Medicine, Department of Neurology, Baltimore, Maryland, USA; Baltimore VA Medical Center, Baltimore, Maryland, USA.
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11
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Li Z, Fan Q, Bilgic B, Wang G, Wu W, Polimeni JR, Miller KL, Huang SY, Tian Q. Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat). Med Image Anal 2023; 86:102744. [PMID: 36867912 PMCID: PMC10517382 DOI: 10.1016/j.media.2023.102744] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 12/25/2022] [Accepted: 01/05/2023] [Indexed: 01/20/2023]
Abstract
Diffusion MRI is a useful neuroimaging tool for non-invasive mapping of human brain microstructure and structural connections. The analysis of diffusion MRI data often requires brain segmentation, including volumetric segmentation and cerebral cortical surfaces, from additional high-resolution T1-weighted (T1w) anatomical MRI data, which may be unacquired, corrupted by subject motion or hardware failure, or cannot be accurately co-registered to the diffusion data that are not corrected for susceptibility-induced geometric distortion. To address these challenges, this study proposes to synthesize high-quality T1w anatomical images directly from diffusion data using convolutional neural networks (CNNs) (entitled "DeepAnat"), including a U-Net and a hybrid generative adversarial network (GAN), and perform brain segmentation on synthesized T1w images or assist the co-registration using synthesized T1w images. The quantitative and systematic evaluations using data of 60 young subjects provided by the Human Connectome Project (HCP) show that the synthesized T1w images and results for brain segmentation and comprehensive diffusion analysis tasks are highly similar to those from native T1w data. The brain segmentation accuracy is slightly higher for the U-Net than the GAN. The efficacy of DeepAnat is further validated on a larger dataset of 300 more elderly subjects provided by the UK Biobank. Moreover, the U-Nets trained and validated on the HCP and UK Biobank data are shown to be highly generalizable to the diffusion data from Massachusetts General Hospital Connectome Diffusion Microstructure Dataset (MGH CDMD) acquired with different hardware systems and imaging protocols and therefore can be used directly without retraining or with fine-tuning for further improved performance. Finally, it is quantitatively demonstrated that the alignment between native T1w images and diffusion images uncorrected for geometric distortion assisted by synthesized T1w images substantially improves upon that by directly co-registering the diffusion and T1w images using the data of 20 subjects from MGH CDMD. In summary, our study demonstrates the benefits and practical feasibility of DeepAnat for assisting various diffusion MRI data analyses and supports its use in neuroscientific applications.
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Affiliation(s)
- Ziyu Li
- Department of Biomedical Engineering, Tsinghua University, Beijing, China; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Guangzhi Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Qiyuan Tian
- Department of Biomedical Engineering, Tsinghua University, Beijing, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States.
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12
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Piredda GF, Caneschi S, Hilbert T, Bonanno G, Joseph A, Egger K, Peter J, Klöppel S, Jehli E, Grieder M, Slotboom J, Seiffge D, Goeldlin M, Hoepner R, Willems T, Vulliemoz S, Seeck M, Venkategowda PB, Corredor Jerez RA, Maréchal B, Thiran JP, Wiest R, Kober T, Radojewski P. Submillimeter T 1 atlas for subject-specific abnormality detection at 7T. Magn Reson Med 2023; 89:1601-1616. [PMID: 36478417 DOI: 10.1002/mrm.29540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/14/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE Studies at 3T have shown that T1 relaxometry enables characterization of brain tissues at the single-subject level by comparing individual physical properties to a normative atlas. In this work, an atlas of normative T1 values at 7T is introduced with 0.6 mm isotropic resolution and its clinical potential is explored in comparison to 3T. METHODS T1 maps were acquired in two separate healthy cohorts scanned at 3T and 7T. Using transfer learning, a template-based brain segmentation algorithm was adapted to ultra-high field imaging data. After segmenting brain tissues, volumes were normalized into a common space, and an atlas of normative T1 values was established by modeling the T1 inter-subject variability. A method for single-subject comparisons restricted to white matter and subcortical structures was developed by computing Z-scores. The comparison was applied to eight patients scanned at both field strengths for proof of concept. RESULTS The proposed method for morphometry delivered segmentation masks without statistically significant differences from those derived with the original pipeline at 3T and achieved accurate segmentation at 7T. The established normative atlas allowed characterizing tissue alterations in single-subject comparisons at 7T, and showed greater anatomical details compared with 3T results. CONCLUSION A high-resolution quantitative atlas with an adapted pipeline was introduced and validated. Several case studies on different clinical conditions showed the feasibility, potential and limitations of high-resolution single-subject comparisons based on quantitative MRI atlases. This method in conjunction with 7T higher resolution broadens the range of potential applications of quantitative MRI in clinical practice.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland.,CIBM-AIT, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Samuele Caneschi
- 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
| | - 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
| | - Gabriele Bonanno
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Bern, Switzerland.,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Arun Joseph
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Bern, Switzerland.,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Karl Egger
- Department of Neuroradiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jessica Peter
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Elisabeth Jehli
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.,Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland
| | - Matthias Grieder
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - David Seiffge
- Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Martina Goeldlin
- Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Robert Hoepner
- Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Tom Willems
- Institute of Psychology, University of Bern, Bern, Switzerland
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Margitta Seeck
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | | | - Ricardo A Corredor Jerez
- 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
| | - Bénédicte Maréchal
- 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
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Roland Wiest
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Tobias Kober
- 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
| | - Piotr Radojewski
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
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13
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Haenelt D, Trampel R, Nasr S, Polimeni JR, Tootell RBH, Sereno MI, Pine KJ, Edwards LJ, Helbling S, Weiskopf N. High-resolution quantitative and functional MRI indicate lower myelination of thin and thick stripes in human secondary visual cortex. eLife 2023; 12:e78756. [PMID: 36888685 PMCID: PMC9995117 DOI: 10.7554/elife.78756] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 02/08/2023] [Indexed: 03/09/2023] Open
Abstract
The characterization of cortical myelination is essential for the study of structure-function relationships in the human brain. However, knowledge about cortical myelination is largely based on post-mortem histology, which generally renders direct comparison to function impossible. The repeating pattern of pale-thin-pale-thick stripes of cytochrome oxidase (CO) activity in the primate secondary visual cortex (V2) is a prominent columnar system, in which histology also indicates different myelination of thin/thick versus pale stripes. We used quantitative magnetic resonance imaging (qMRI) in conjunction with functional magnetic resonance imaging (fMRI) at ultra-high field strength (7 T) to localize and study myelination of stripes in four human participants at sub-millimeter resolution in vivo. Thin and thick stripes were functionally localized by exploiting their sensitivity to color and binocular disparity, respectively. Resulting functional activation maps showed robust stripe patterns in V2 which enabled further comparison of quantitative relaxation parameters between stripe types. Thereby, we found lower longitudinal relaxation rates (R1) of thin and thick stripes compared to surrounding gray matter in the order of 1-2%, indicating higher myelination of pale stripes. No consistent differences were found for effective transverse relaxation rates (R2*). The study demonstrates the feasibility to investigate structure-function relationships in living humans within one cortical area at the level of columnar systems using qMRI.
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Affiliation(s)
- Daniel Haenelt
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- International Max Planck Research School on Neuroscience of Communication: Function, Structure, and PlasticityLeipzigGermany
| | - Robert Trampel
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Shahin Nasr
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownUnited States
- Department of Radiology, Harvard Medical SchoolBostonUnited States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownUnited States
- Department of Radiology, Harvard Medical SchoolBostonUnited States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Roger BH Tootell
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownUnited States
- Department of Radiology, Harvard Medical SchoolBostonUnited States
| | - Martin I Sereno
- Department of Psychology, College of Sciences, San Diego State UniversitySan DiegoUnited States
| | - Kerrin J Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Luke J Edwards
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Saskia Helbling
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Poeppel Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck SocietyFrankfurt am MainGermany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig UniversityLeipzigGermany
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14
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Burles F, Williams R, Berger L, Pike GB, Lebel C, Iaria G. The Unresolved Methodological Challenge of Detecting Neuroplastic Changes in Astronauts. Life (Basel) 2023; 13:500. [PMID: 36836857 PMCID: PMC9966542 DOI: 10.3390/life13020500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/04/2023] [Accepted: 02/07/2023] [Indexed: 02/15/2023] Open
Abstract
After completing a spaceflight, astronauts display a salient upward shift in the position of the brain within the skull, accompanied by a redistribution of cerebrospinal fluid. Magnetic resonance imaging studies have also reported local changes in brain volume following a spaceflight, which have been cautiously interpreted as a neuroplastic response to spaceflight. Here, we provide evidence that the grey matter volume changes seen in astronauts following spaceflight are contaminated by preprocessing errors exacerbated by the upwards shift of the brain within the skull. While it is expected that an astronaut's brain undergoes some neuroplastic adaptations during spaceflight, our findings suggest that the brain volume changes detected using standard processing pipelines for neuroimaging analyses could be contaminated by errors in identifying different tissue types (i.e., tissue segmentation). These errors may undermine the interpretation of such analyses as direct evidence of neuroplastic adaptation, and novel or alternate preprocessing or experimental paradigms are needed in order to resolve this important issue in space health research.
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Affiliation(s)
- Ford Burles
- Canadian Space Health Research Network, Department of Psychology, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Rebecca Williams
- Faculty of Health, School of Human Services, Charles Darwin University, Darwin, NT 0810, Australia
| | - Lila Berger
- Canadian Space Health Research Network, Department of Psychology, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - G. Bruce Pike
- Department of Radiology, Department of Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Catherine Lebel
- Department of Radiology, Alberta Children’s Hospital Research Institute, Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Giuseppe Iaria
- Canadian Space Health Research Network, Department of Psychology, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
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15
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Ferraro PM, Gualco L, Costagli M, Schiavi S, Ponzano M, Signori A, Massa F, Pardini M, Castellan L, Levrero F, Zacà D, Piredda GF, Hilbert T, Kober T, Roccatagliata L. Compressed sensing (CS) MP2RAGE versus standard MPRAGE: A comparison of derived brain volume measurements. Phys Med 2022; 103:166-174. [DOI: 10.1016/j.ejmp.2022.10.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/16/2022] [Accepted: 10/23/2022] [Indexed: 11/11/2022] Open
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16
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Nian R, Gao M, Zhang S, Yu J, Gholipour A, Kong S, Wang R, Sui Y, Velasco-Annis C, Tomas-Fernandez X, Li Q, Lv H, Qian Y, Warfield SK. Toward evaluation of multiresolution cortical thickness estimation with FreeSurfer, MaCRUISE, and BrainSuite. Cereb Cortex 2022; 33:5082-5096. [PMID: 36288912 DOI: 10.1093/cercor/bhac401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Advances in Magnetic Resonance Imaging hardware and methodologies allow for promoting the cortical morphometry with submillimeter spatial resolution. In this paper, we generated 3D self-enhanced high-resolution (HR) MRI imaging, by adapting 1 deep learning architecture, and 3 standard pipelines, FreeSurfer, MaCRUISE, and BrainSuite, have been collectively employed to evaluate the cortical thickness. We systematically investigated the differences in cortical thickness estimation for MRI sequences at multiresolution homologously originated from the native image. It has been revealed that there systematically exhibited the preferences in determining both inner and outer cortical surfaces at higher resolution, yielding most deeper cortical surface placements toward GM/WM or GM/CSF boundaries, which directs a consistent reduction tendency of mean cortical thickness estimation; on the contrary, the lower resolution data will most probably provide a more coarse and rough evaluation in cortical surface reconstruction, resulting in a relatively thicker estimation. Although the differences of cortical thickness estimation at the diverse spatial resolution varied with one another, almost all led to roughly one-sixth to one-fifth significant reduction across the entire brain at the HR, independent to the pipelines we applied, which emphasizes on generally coherent improved accuracy in a data-independent manner and endeavors to cost-efficiency with quantitative opportunities.
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Affiliation(s)
- Rui Nian
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Mingshan Gao
- Citigroup Services and Technology Limited, 1000 Chenhi Road, Shanghai, China
| | | | - Junjie Yu
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Ali Gholipour
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Shuang Kong
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Ruirui Wang
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Yao Sui
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Clemente Velasco-Annis
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Xavier Tomas-Fernandez
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Qiuying Li
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Hangyu Lv
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Yuqi Qian
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Simon K Warfield
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
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17
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Karahan E, Tait L, Si R, Özkan A, Szul MJ, Graham KS, Lawrence AD, Zhang J. The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure. Commun Biol 2022; 5:1007. [PMID: 36151363 PMCID: PMC9508245 DOI: 10.1038/s42003-022-03974-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 09/09/2022] [Indexed: 11/09/2022] Open
Abstract
Humans differ from each other in a wide range of biometrics, but to what extent brain connectivity varies between individuals remains largely unknown. By combining diffusion-weighted imaging (DWI) and magnetoencephalography (MEG), this study characterizes the inter-subject variability (ISV) of multimodal brain connectivity. Structural connectivity is characterized by higher ISV in association cortices including the core multiple-demand network and lower ISV in the sensorimotor cortex. MEG ISV exhibits frequency-dependent signatures, and the extent of MEG ISV is consistent with that of structural connectivity ISV in selective macroscopic cortical clusters. Across the cortex, the ISVs of structural connectivity and beta-band MEG functional connectivity are negatively associated with cortical myelin content indexed by the quantitative T1 relaxation rate measured by high-resolution 7 T MRI. Furthermore, MEG ISV from alpha to gamma bands relates to the hindrance and restriction of the white-matter tissue estimated by DWI microstructural models. Our findings depict the inter-relationship between the ISV of brain connectivity from multiple modalities, and highlight the role of tissue microstructure underpinning the ISV.
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Affiliation(s)
- Esin Karahan
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Luke Tait
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Ruoguang Si
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Ayşegül Özkan
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Maciek J Szul
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Bron, France.,Université Claude Bernard Lyon I, Lyon, France
| | - Kim S Graham
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom. .,Department of Computer Science, Swansea University, Swansea, United Kingdom.
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18
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Piredda GF, Hilbert T, Ravano V, Canales-Rodríguez EJ, Pizzolato M, Meuli R, Thiran JP, Richiardi J, Kober T. Data-driven myelin water imaging based on T 1 and T 2 relaxometry. NMR IN BIOMEDICINE 2022; 35:e4668. [PMID: 34936147 DOI: 10.1002/nbm.4668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 11/16/2021] [Accepted: 11/30/2021] [Indexed: 06/14/2023]
Abstract
Long acquisition times preclude the application of multiecho spin echo (MESE) sequences for myelin water fraction (MWF) mapping in daily clinical practice. In search of alternative methods, previous studies of interest explored the biophysical modeling of MWF from measurements of different tissue properties that can be obtained in scan times shorter than those required for the MESE. In this work, a novel data-driven estimation of MWF maps from fast relaxometry measurements is proposed and investigated. T1 and T2 relaxometry maps were acquired in a cohort of 20 healthy subjects along with a conventional MESE sequence. Whole-brain quantitative mapping was achieved with a fast protocol in 6 min 24 s. Reference MWF maps were derived from the MESE sequence (TA = 11 min 17 s) and their data-driven estimation from relaxometry measurements was investigated using three different modeling strategies: two general linear models (GLMs) with linear and quadratic regressors, respectively; a random forest regression model; and two deep neural network architectures, a U-Net and a conditional generative adversarial network (cGAN). Models were validated using a 10-fold crossvalidation. The resulting maps were visually and quantitatively compared by computing the root mean squared error (RMSE) between the estimated and reference MWF maps, the intraclass correlation coefficients (ICCs) between corresponding MWF values in different brain regions, and by performing Bland-Altman analysis. Qualitatively, the estimated maps appear to generally provide a similar, yet more blurred MWF contrast in comparison with the reference, with the cGAN model best capturing MWF variabilities in small structures. By estimating the average adjusted coefficient of determination of the GLM with quadratic regressors, we showed that 87% of the variability in the MWF values can be explained by relaxation times alone. Further quantitative analysis showed an average RMSE smaller than 0.1% for all methods. The ICC was greater than 0.81 for all methods, and the bias smaller than 2.19%. It was concluded that this work confirms the notion that relaxometry parameters contain a large part of the information on myelin water and that MWF maps can be generated from T1 /T2 data with minimal error. Among the investigated modeling approaches, the cGAN provided maps with the best trade-off between accuracy and blurriness. Fast relaxometry, like the 6 min 24 s whole-brain protocol used in this work in conjunction with machine learning, may thus have the potential to replace time-consuming MESE acquisitions.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Veronica Ravano
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Marco Pizzolato
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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19
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Trotier AJ, Dilharreguy B, Anandra S, Corbin N, Lefrançois W, Ozenne V, Miraux S, Ribot EJ. The Compressed Sensing MP2RAGE as a Surrogate to the MPRAGE for Neuroimaging at 3 T. Invest Radiol 2022; 57:366-378. [PMID: 35030106 PMCID: PMC9390231 DOI: 10.1097/rli.0000000000000849] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/08/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVES The magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) sequence provides quantitative T1 maps in addition to high-contrast morphological images. Advanced acceleration techniques such as compressed sensing (CS) allow its acquisition time to be compatible with clinical applications. To consider its routine use in future neuroimaging protocols, the repeatability of the segmented brain structures was evaluated and compared with the standard morphological sequence (magnetization-prepared rapid gradient echo [MPRAGE]). The repeatability of the T1 measurements was also assessed. MATERIALS AND METHODS Thirteen healthy volunteers were scanned either 3 or 4 times at several days of interval, on a 3 T clinical scanner, with the 2 sequences (CS-MP2RAGE and MPRAGE), set with the same spatial resolution (0.8-mm isotropic) and scan duration (6 minutes 21 seconds). The reconstruction time of the CS-MP2RAGE outputs (including the 2 echo images, the MP2RAGE image, and the T1 map) was 3 minutes 33 seconds, using an open-source in-house algorithm implemented in the Gadgetron framework.Both precision and variability of volume measurements obtained from CAT12 and VolBrain were assessed. The T1 accuracy and repeatability were measured on phantoms and on humans and were compared with literature.Volumes obtained from the CS-MP2RAGE and the MPRAGE images were compared using Student t tests (P < 0.05 was considered significant). RESULTS The CS-MP2RAGE acquisition provided morphological images of the same quality and higher contrasts than the standard MPRAGE images. Similar intravolunteer variabilities were obtained with the CS-MP2RAGE and the MPRAGE segmentations. In addition, high-resolution T1 maps were obtained from the CS-MP2RAGE. T1 times of white and gray matters and several deep gray nuclei are consistent with the literature and show very low variability (<1%). CONCLUSIONS The CS-MP2RAGE can be used in future protocols to rapidly obtain morphological images and quantitative T1 maps in 3-dimensions while maintaining high repeatability in volumetry and relaxation times.
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Affiliation(s)
- Aurélien J. Trotier
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Bixente Dilharreguy
- Biomedical Imaging Facility (pIBIO), UMS3767, CNRS/Université de Bordeaux, Bordeaux, France
| | - Serge Anandra
- Biomedical Imaging Facility (pIBIO), UMS3767, CNRS/Université de Bordeaux, Bordeaux, France
| | - Nadège Corbin
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
- UCL Queen Square Institute of Neurology, Wellcome Centre for Human Neuroimaging, University College of London, London, United Kingdom
| | - William Lefrançois
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Valery Ozenne
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Sylvain Miraux
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Emeline J. Ribot
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
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20
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Test-retest reliability of FreeSurfer-derived volume, area and cortical thickness from MPRAGE and MP2RAGE brain MRI images. NEUROIMAGE: REPORTS 2022; 2. [PMID: 36032692 PMCID: PMC9409374 DOI: 10.1016/j.ynirp.2022.100086] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background and purpose: Large MRI studies often pool data gathered from widely varying imaging sequences. Pooled data creates a potential source of variation in structural analyses which may cause misinterpretation of findings. The purpose of this study is to determine if data acquired using different scan sequences, head coils and scanners offers consistent structural measurements. Materials and methods: Participants (163 right-handed males: 82 typically developing controls, 81 participants with autism spectrum disorder) were scanned on the same day using an MPRAGE sequence with a 12-channel headcoil on a Siemens 3T Trio scanner and an MP2RAGE sequence with a 64-channel headcoil on a Siemens 3T Prisma scanner. Segmentation was performed using FreeSurfer to identify regions exhibiting variation between sequences on measures of volume, surface area, and cortical thickness. Intraclass correlation coefficient (ICC) and mean percent difference (MPD) were used as test-retest reproducibility measures. Results: ICC for total brain segmented volume yielded a 0.99 intraclass correlation, demonstrating high overall volumetric reproducibility. Comparison of individual regions of interest resulted in greater variation. Volumetric variability, although low overall, was greatest in the entorhinal cortex (ICC = 0.71), frontal (ICC = 0.60) and temporal (ICC = 0.60) poles. Surface area variability was greatest in the insula (ICC = 0.65), temporal (ICC = 0.64) and frontal (ICC = 0.68) poles. Cortical thickness was most variable in the frontal (ICC = 0.41) and temporal (ICC = 0.35) poles. Conclusion: Data collected on different scanners and head coils using MPRAGE and MP2RAGE are generally consistent for surface area and volume estimates. However, regional variability may constrain accuracy in some regions and cortical thickness measurements exhibit higher generalized variability.
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21
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Okada T, Fujimoto K, Fushimi Y, Akasaka T, Thuy DHD, Shima A, Sawamoto N, Oishi N, Zhang Z, Funaki T, Nakamoto Y, Murai T, Miyamoto S, Takahashi R, Isa T. Neuroimaging at 7 Tesla: a pictorial narrative review. Quant Imaging Med Surg 2022; 12:3406-3435. [PMID: 35655840 PMCID: PMC9131333 DOI: 10.21037/qims-21-969] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/05/2022] [Indexed: 01/26/2024]
Abstract
Neuroimaging using the 7-Tesla (7T) human magnetic resonance (MR) system is rapidly gaining popularity after being approved for clinical use in the European Union and the USA. This trend is the same for functional MR imaging (MRI). The primary advantages of 7T over lower magnetic fields are its higher signal-to-noise and contrast-to-noise ratios, which provide high-resolution acquisitions and better contrast, making it easier to detect lesions and structural changes in brain disorders. Another advantage is the capability to measure a greater number of neurochemicals by virtue of the increased spectral resolution. Many structural and functional studies using 7T have been conducted to visualize details in the white matter and layers of the cortex and hippocampus, the subnucleus or regions of the putamen, the globus pallidus, thalamus and substantia nigra, and in small structures, such as the subthalamic nucleus, habenula, perforating arteries, and the perivascular space, that are difficult to observe at lower magnetic field strengths. The target disorders for 7T neuroimaging range from tumoral diseases to vascular, neurodegenerative, and psychiatric disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, major depressive disorder, and schizophrenia. MR spectroscopy has also been used for research because of its increased chemical shift that separates overlapping peaks and resolves neurochemicals more effectively at 7T than a lower magnetic field. This paper presents a narrative review of these topics and an illustrative presentation of images obtained at 7T. We expect 7T neuroimaging to provide a new imaging biomarker of various brain disorders.
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Affiliation(s)
- Tomohisa Okada
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koji Fujimoto
- Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Thai Akasaka
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Dinh H. D. Thuy
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Atsushi Shima
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Nobukatsu Sawamoto
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Oishi
- Medial Innovation Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Zhilin Zhang
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takeshi Funaki
- Department of Neurosurgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Susumu Miyamoto
- Department of Neurosurgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tadashi Isa
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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22
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Ren J, Hu Q, Wang W, Zhang W, Hubbard CS, Zhang P, An N, Zhou Y, Dahmani L, Wang D, Fu X, Sun Z, Wang Y, Wang R, Li L, Liu H. Fast cortical surface reconstruction from MRI using deep learning. Brain Inform 2022; 9:6. [PMID: 35262808 PMCID: PMC8907118 DOI: 10.1186/s40708-022-00155-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 02/25/2022] [Indexed: 11/23/2022] Open
Abstract
Reconstructing cortical surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based functional and anatomical image analyses. Conventional algorithms for cortical surface reconstruction are computationally inefficient and typically take several hours for each subject, causing a bottleneck in applications when a fast turnaround time is needed. To address this challenge, we propose a fast cortical surface reconstruction (FastCSR) pipeline by leveraging deep machine learning. We trained our model to learn an implicit representation of the cortical surface in volumetric space, termed the “level set representation”. A fast volumetric topology correction method and a topology-preserving surface mesh extraction procedure were employed to reconstruct the cortical surface based on the level set representation. Using 1-mm isotropic T1-weighted images, the FastCSR pipeline was able to reconstruct a subject’s cortical surfaces within 5 min with comparable surface quality, which is approximately 47 times faster than the traditional FreeSurfer pipeline. The advantage of FastCSR becomes even more apparent when processing high-resolution images. Importantly, the model demonstrated good generalizability in previously unseen data and showed high test–retest reliability in cortical morphometrics and anatomical parcellations. Finally, FastCSR was robust to images with compromised quality or with distortions caused by lesions. This fast and robust pipeline for cortical surface reconstruction may facilitate large-scale neuroimaging studies and has potential in clinical applications wherein brain images may be compromised.
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Affiliation(s)
- Jianxun Ren
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Qingyu Hu
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China
| | | | - Wei Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100080, China
| | - Catherine S Hubbard
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29425, USA
| | | | - Ning An
- Neural Galaxy, Beijing, 102206, China
| | - Ying Zhou
- Neural Galaxy, Beijing, 102206, China
| | - Louisa Dahmani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Xiaoxuan Fu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA.,Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29425, USA.,State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300401, China
| | | | | | - Ruiqi Wang
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China. .,Precision Medicine and Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, 518055, China. .,IDG/McGovern Institute for Brain Research at Tsinghua University, Beijing, 100084, China. .,Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA. .,Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29425, USA.
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23
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Cohen-Adad J, Alonso-Ortiz E, Abramovic M, Arneitz C, Atcheson N, Barlow L, Barry RL, Barth M, Battiston M, Büchel C, Budde M, Callot V, Combes AJE, De Leener B, Descoteaux M, de Sousa PL, Dostál M, Doyon J, Dvorak A, Eippert F, Epperson KR, Epperson KS, Freund P, Finsterbusch J, Foias A, Fratini M, Fukunaga I, Gandini Wheeler-Kingshott CAM, Germani G, Gilbert G, Giove F, Gros C, Grussu F, Hagiwara A, Henry PG, Horák T, Hori M, Joers J, Kamiya K, Karbasforoushan H, Keřkovský M, Khatibi A, Kim JW, Kinany N, Kitzler HH, Kolind S, Kong Y, Kudlička P, Kuntke P, Kurniawan ND, Kusmia S, Labounek R, Laganà MM, Laule C, Law CS, Lenglet C, Leutritz T, Liu Y, Llufriu S, Mackey S, Martinez-Heras E, Mattera L, Nestrasil I, O'Grady KP, Papinutto N, Papp D, Pareto D, Parrish TB, Pichiecchio A, Prados F, Rovira À, Ruitenberg MJ, Samson RS, Savini G, Seif M, Seifert AC, Smith AK, Smith SA, Smith ZA, Solana E, Suzuki Y, Tackley G, Tinnermann A, Valošek J, Van De Ville D, Yiannakas MC, Weber Ii KA, Weiskopf N, Wise RG, Wyss PO, Xu J. Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers. Sci Data 2021; 8:219. [PMID: 34400655 PMCID: PMC8368310 DOI: 10.1038/s41597-021-00941-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/26/2021] [Indexed: 12/21/2022] Open
Abstract
In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/ . The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.
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Affiliation(s)
- Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.
- Mila - Quebec AI Institute, Montreal, QC, Canada.
| | - Eva Alonso-Ortiz
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Mihael Abramovic
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Carina Arneitz
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Nicole Atcheson
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Laura Barlow
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, MA, USA
| | - Markus Barth
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Marco Battiston
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Christian Büchel
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthew Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Virginie Callot
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hopital Universitaire Timone, CEMEREM, Marseille, France
| | - Anna J E Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin De Leener
- Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, Canada
- CHU Sainte-Justine Research Centre, Montreal, QC, Canada
| | - Maxime Descoteaux
- Centre de Recherche CHUS, CIMS, Sherbrooke, Canada
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke, Sherbrooke, Canada
| | | | - Marek Dostál
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Julien Doyon
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Adam Dvorak
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Falk Eippert
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Karla R Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin S Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Patrick Freund
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexandru Foias
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Michela Fratini
- Institute of Nanotechnology, CNR, Rome, Italy
- IRCCS Santa Lucia Foundation, Rome, Italy
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Giancarlo Germani
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Federico Giove
- IRCCS Santa Lucia Foundation, Rome, Italy
- CREF - Museo storico della fisica e Centro studi e ricerche Enrico Fermi, Rome, Italy
| | - Charley Gros
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tomáš Horák
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - James Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Kouhei Kamiya
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | - Haleh Karbasforoushan
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA
| | - Miloš Keřkovský
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Ali Khatibi
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Joo-Won Kim
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nawal Kinany
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Hagen H Kitzler
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Shannon Kolind
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Department Of Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Yazhuo Kong
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Petr Kudlička
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Paul Kuntke
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Nyoman D Kurniawan
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Slawomir Kusmia
- CUBRIC, Cardiff University, Wales, UK
- Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, UK
| | - René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Departments of Neurology and Biomedical Engineering, University Hospital Olomouc, Olomouc, Czech Republic
| | | | - Cornelia Laule
- Departments of Radiology, Pathology & Laboratory Medicine, Physics & Astronomy; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Christine S Law
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tobias Leutritz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Sean Mackey
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Loan Mattera
- Fondation Campus Biotech Genève, 1202, Geneva, Switzerland
| | - Igor Nestrasil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Kristin P O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nico Papinutto
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Papp
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Deborah Pareto
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Todd B Parrish
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Anna Pichiecchio
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Àlex Rovira
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Marc J Ruitenberg
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Rebecca S Samson
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Giovanni Savini
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Maryam Seif
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Alan C Seifert
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alex K Smith
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zachary A Smith
- University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Y Suzuki
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | | | - Alexandra Tinnermann
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Valošek
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
| | - Dimitri Van De Ville
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Kenneth A Weber Ii
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Richard G Wise
- CUBRIC, Cardiff University, Wales, UK
- Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio University" of Chieti-Pescara, Chieti-Pescara, Italy
| | - Patrik O Wyss
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Junqian Xu
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Oliveira ÍAF, Roos T, Dumoulin SO, Siero JCW, van der Zwaag W. Can 7T MPRAGE match MP2RAGE for gray-white matter contrast? Neuroimage 2021; 240:118384. [PMID: 34265419 DOI: 10.1016/j.neuroimage.2021.118384] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 06/25/2021] [Accepted: 07/08/2021] [Indexed: 10/20/2022] Open
Abstract
Ultra-High Field (UHF) MRI provides a significant increase in Signal-to-Noise Ratio (SNR) and gains in contrast weighting in several functional and structural acquisitions. Unfortunately, an increase in field strength also induces non-uniformities in the transmit field (B1+) that can compromise image contrast non-uniformly. The MPRAGE is one of the most common T1 weighted (T1w) image acquisitions for structural imaging. It provides excellent contrast between gray and white matter and is widely used for brain segmentation. At 7T, the signal non-uniformities tend to complicate this and therefore, the self-bias-field corrected MP2RAGE is often used there. In both MPRAGE and MP2RAGE, more homogeneous image contrast can be achieved with adiabatic pulses, like the TR-FOCI inversion pulse, or special pulse design on parallel transmission systems, like Universal Pulses (UP). In the present study, we investigate different strategies to improve the bias-field for MPRAGE at 7T, comparing the contrast and GM/WM segmentability against MP2RAGE. The higher temporal efficiency of MPRAGE combined with the potential of the user-friendly UPs was the primary motivation for this MPRAGE-MP2RAGE comparison. We acquired MPRAGE data in six volunteers, adding a k-space shutter to reduce scan time, a kt-point UP approach for homogeneous signal excitation, and a TR-FOCI pulse for homogeneous inversion. Our results show remarkable signal contrast improvement throughout the brain, including regions of low B1+ such as the cerebellum. The improvements in the MPRAGE were largest following the introduction of the UPs. In addition to the CNR, both SNR and GM/WM segmentability were also assessed. Among the MPRAGEs, the combined strategy (UP + TR-FOCI) yielded highest SNR and showed highest spatial similarity between GM segments to the MP2RAGE. Interestingly, the distance between gray and white matter peaks in the intensity histograms did not increase, as better pulses and higher SNR especially benefitted the (cerebellar) gray matter. Overall, the gray-white matter contrast from MP2RAGE is higher, with higher CNR and higher intensity peak distances, even when scaled to scan time. Hence, the extra acquisition time for MP2RAGE is justified by the improved segmentability.
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Affiliation(s)
- Ícaro A F Oliveira
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands.
| | - Thomas Roos
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands
| | - Serge O Dumoulin
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands; Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands
| | - Jeroen C W Siero
- Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Radiology, Utrecht Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands
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25
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Serru M, Marechal B, Kober T, Ribier L, Sembely Taveau C, Sirinelli D, Cottier JP, Morel B. Improving diagnosis accuracy of brain volume abnormalities during childhood with an automated MP2RAGE-based MRI brain segmentation. J Neuroradiol 2021; 48:259-265. [DOI: 10.1016/j.neurad.2019.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 06/04/2019] [Accepted: 06/07/2019] [Indexed: 11/30/2022]
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Choi S, Spini M, Hua J, Harrison DM. Blood-brain barrier breakdown in non-enhancing multiple sclerosis lesions detected by 7-Tesla MP2RAGE ΔT1 mapping. PLoS One 2021; 16:e0249973. [PMID: 33901207 PMCID: PMC8075220 DOI: 10.1371/journal.pone.0249973] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 03/28/2021] [Indexed: 11/19/2022] Open
Abstract
Although the blood-brain barrier (BBB) is altered in most multiple sclerosis (MS) lesions, gadolinium enhancement is seen only in acute lesions. In this study, we aimed to investigate gadolinium-induced changes in T1 relaxation time in MS lesions on 7-tesla (7T) MRI as a means to quantify BBB breakdown in non-enhancing MS lesions. Forty-seven participants with MS underwent 7T MRI of the brain with a magnitude-prepared rapid acquisition of 2 gradient echoes (MP2RAGE) sequence before and after contrast. Subtraction of pre- and post-contrast T1 maps was used to measure T1 relaxation time change (ΔT1) from gadolinium. ΔT1 values were interrogated in enhancing white matter lesions (ELs), non-enhancing white matter lesions (NELs), and normal appearing white matter (NAWM) and metrics were compared to clinical data. ΔT1 was measurable in NELs (median: -0.139 (-0.304, 0.174) seconds; p < 0.001) and was negligible in NAWM (median: -0.001 (-0.036, 0.155) seconds; p = 0.516). Median ΔT1 in NELs correlated with disability as measured by Expanded Disability Status Scale (EDSS) (rho = -0.331, p = 0.026). Multiple measures of NEL ΔT1 variability also correlated with EDSS. NEL ΔT1 values were greater and more variable in patients with progressive forms of MS and greater in those not on MS treatment. Measurement of the changes in T1 relaxation time caused by contrast on 7T MP2RAGE reveals clinically relevant evidence of BBB breakdown in NELs in MS. This data suggests that NEL ΔT1 should be evaluated further as a potential biomarker of persistently disrupted BBB in MS.
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Affiliation(s)
- Seongjin Choi
- Department of Neurology, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Margaret Spini
- University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Jun Hua
- Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, United Stated of America
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States of America
| | - Daniel M. Harrison
- Department of Neurology, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- * E-mail:
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Kashyap S, Ivanov D, Havlicek M, Huber L, Poser BA, Uludağ K. Sub-millimetre resolution laminar fMRI using Arterial Spin Labelling in humans at 7 T. PLoS One 2021; 16:e0250504. [PMID: 33901230 PMCID: PMC8075193 DOI: 10.1371/journal.pone.0250504] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 04/07/2021] [Indexed: 12/02/2022] Open
Abstract
Laminar fMRI at ultra-high magnetic field strength is typically carried out using the Blood Oxygenation Level-Dependent (BOLD) contrast. Despite its unrivalled sensitivity to detecting activation, the BOLD contrast is limited in its spatial specificity due to signals stemming from intra-cortical ascending and pial veins. Alternatively, regional changes in perfusion (i.e., cerebral blood flow through tissue) are colocalised to neuronal activation, which can be non-invasively measured using Arterial Spin Labelling (ASL) MRI. In addition, ASL provides a quantitative marker of neuronal activation in terms of perfusion signal, which is simultaneously acquired along with the BOLD signal. However, ASL for laminar imaging is challenging due to the lower SNR of the perfusion signal and higher RF power deposition i.e., specific absorption rate (SAR) of ASL sequences. In the present study, we present for the first time in humans, isotropic sub-millimetre spatial resolution functional perfusion images using Flow-sensitive Alternating Inversion Recovery (FAIR) ASL with a 3D-EPI readout at 7 T. We show that robust statistical activation maps can be obtained with perfusion-weighting in a single session. We observed the characteristic BOLD amplitude increase towards the superficial laminae, and, in apparent discrepancy, the relative perfusion profile shows a decrease of the amplitude and the absolute perfusion profile a much smaller increase towards the cortical surface. Considering the draining vein effect on the BOLD signal using model-based spatial “convolution”, we show that the empirically measured perfusion and BOLD profiles are, in fact, consistent with each other. This study demonstrates that laminar perfusion fMRI in humans is feasible at 7 T and that caution must be exercised when interpreting BOLD signal laminar profiles as direct representation of the cortical distribution of neuronal activity.
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Affiliation(s)
- Sriranga Kashyap
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre (M-BIC), Maastricht University, Maastricht, The Netherlands
- * E-mail: (SK); (DI)
| | - Dimo Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre (M-BIC), Maastricht University, Maastricht, The Netherlands
- * E-mail: (SK); (DI)
| | - Martin Havlicek
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Laurentius Huber
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre (M-BIC), Maastricht University, Maastricht, The Netherlands
| | - Benedikt A. Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre (M-BIC), Maastricht University, Maastricht, The Netherlands
| | - Kâmil Uludağ
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
- Department of Biomedical Engineering, N Center, Sungkyunkwan University, Suwon, South Korea
- Techna Institute & Koerner Scientist in MR Imaging, University Health Network, Toronto, Canada
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28
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Lu PJ, Barakovic M, Weigel M, Rahmanzadeh R, Galbusera R, Schiavi S, Daducci A, La Rosa F, Bach Cuadra M, Sandkühler R, Kuhle J, Kappos L, Cattin P, Granziera C. GAMER-MRI in Multiple Sclerosis Identifies the Diffusion-Based Microstructural Measures That Are Most Sensitive to Focal Damage: A Deep-Learning-Based Analysis and Clinico-Biological Validation. Front Neurosci 2021; 15:647535. [PMID: 33889069 PMCID: PMC8055933 DOI: 10.3389/fnins.2021.647535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 02/23/2021] [Indexed: 12/02/2022] Open
Abstract
Conventional magnetic resonance imaging (cMRI) in multiple sclerosis (MS) patients provides measures of focal brain damage and activity, which are fundamental for disease diagnosis, prognosis, and the evaluation of response to therapy. However, cMRI is insensitive to the damage to the microenvironment of the brain tissue and the heterogeneity of MS lesions. In contrast, the damaged tissue can be characterized by mathematical models on multishell diffusion imaging data, which measure different compartmental water diffusion. In this work, we obtained 12 diffusion measures from eight diffusion models, and we applied a deep-learning attention-based convolutional neural network (CNN) (GAMER-MRI) to select the most discriminating measures in the classification of MS lesions and the perilesional tissue by attention weights. Furthermore, we provided clinical and biological validation of the chosen metrics-and of their most discriminative combinations-by correlating their respective mean values in MS patients with the corresponding Expanded Disability Status Scale (EDSS) and the serum level of neurofilament light chain (sNfL), which are measures of disability and neuroaxonal damage. Our results show that the neurite density index from neurite orientation and dispersion density imaging (NODDI), the measures of the intra-axonal and isotropic compartments from microstructural Bayesian approach, and the measure of the intra-axonal compartment from the spherical mean technique NODDI were the most discriminating (respective attention weights were 0.12, 0.12, 0.15, and 0.13). In addition, the combination of the neurite density index from NODDI and the measures for the intra-axonal and isotropic compartments from the microstructural Bayesian approach exhibited a stronger correlation with EDSS and sNfL than the individual measures. This work demonstrates that the proposed method might be useful to select the microstructural measures that are most discriminative of focal tissue damage and that may also be combined to a unique contrast to achieve stronger correlations to clinical disability and neuroaxonal damage.
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Affiliation(s)
- Po-Jui Lu
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Reza Rahmanzadeh
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Riccardo Galbusera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | | | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Robin Sandkühler
- Center for Medical Image Analysis and Navigation, Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Philippe Cattin
- Center for Medical Image Analysis and Navigation, Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
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Tian Q, Zaretskaya N, Fan Q, Ngamsombat C, Bilgic B, Polimeni JR, Huang SY. Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising. Neuroimage 2021; 233:117946. [PMID: 33711484 PMCID: PMC8421085 DOI: 10.1016/j.neuroimage.2021.117946] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 02/28/2021] [Accepted: 03/03/2021] [Indexed: 11/24/2022] Open
Abstract
Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-mm isotropic resolution for improved accuracy of cortical surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of cortical surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the cortical surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ~0.016, high whole-brain averaged peak signal-to-noise ratio of ~33.5 dB and structural similarity index of ~0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray matter–white matter surface placement, gray matter–cerebrospinal fluid surface placement and cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. These discrepancies were approximately one third to half of those from 1-mm isotropic resolution data. The denoising performance was equivalent to averaging ~2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the cortical surface placement accuracy. The scan-rescan variability of the cortical surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved cortical surface reconstruction at sub-millimeter resolution.
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Affiliation(s)
- Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States.
| | - Natalia Zaretskaya
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States; Institute of Psychology, University of Graz, Graz, Austria; BioTechMed-Graz, Austria
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Thailand
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
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30
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Kim JH, Dodd S, Ye FQ, Knutsen AK, Nguyen D, Wu H, Su S, Mastrogiacomo S, Esparza TJ, Swenson RE, Brody DL. Sensitive detection of extremely small iron oxide nanoparticles in living mice using MP2RAGE with advanced image co-registration. Sci Rep 2021; 11:106. [PMID: 33420210 PMCID: PMC7794370 DOI: 10.1038/s41598-020-80181-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 12/15/2020] [Indexed: 02/05/2023] Open
Abstract
Magnetic resonance imaging (MRI) is a widely used non-invasive methodology for both preclinical and clinical studies. However, MRI lacks molecular specificity. Molecular contrast agents for MRI would be highly beneficial for detecting specific pathological lesions and quantitatively evaluating therapeutic efficacy in vivo. In this study, an optimized Magnetization Prepared—RApid Gradient Echo (MP-RAGE) with 2 inversion times called MP2RAGE combined with advanced image co-registration is presented as an effective non-invasive methodology to quantitatively detect T1 MR contrast agents. The optimized MP2RAGE produced high quality in vivo mouse brain T1 (or R1 = 1/T1) map with high spatial resolution, 160 × 160 × 160 µm3 voxel at 9.4 T. Test–retest signal to noise was > 20 for most voxels. Extremely small iron oxide nanoparticles (ESIONPs) having 3 nm core size and 11 nm hydrodynamic radius after polyethylene glycol (PEG) coating were intracranially injected into mouse brain and detected as a proof-of-concept. Two independent MP2RAGE MR scans were performed pre- and post-injection of ESIONPs followed by advanced image co-registration. The comparison of two T1 (or R1) maps after image co-registration provided precise and quantitative assessment of the effects of the injected ESIONPs at each voxel. The proposed MR protocol has potential for future use in the detection of T1 molecular contrast agents.
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Affiliation(s)
- Joong H Kim
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD, USA.,Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Stephen Dodd
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, and National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD, USA
| | - Duong Nguyen
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Haitao Wu
- Chemistry and Synthesis Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shiran Su
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Simone Mastrogiacomo
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Thomas J Esparza
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD, USA.,Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Rolf E Swenson
- Chemistry and Synthesis Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - David L Brody
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD, USA. .,Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA. .,Department of Neurology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
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31
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Tian Q, Bilgic B, Fan Q, Ngamsombat C, Zaretskaya N, Fultz NE, Ohringer NA, Chaudhari AS, Hu Y, Witzel T, Setsompop K, Polimeni JR, Huang SY. Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution. Cereb Cortex 2021; 31:463-482. [PMID: 32887984 PMCID: PMC7727379 DOI: 10.1093/cercor/bhaa237] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 07/30/2020] [Accepted: 07/30/2020] [Indexed: 11/14/2022] Open
Abstract
Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μm at the group level for the simulated data, and below 200 μm at the single-subject level and below 100 μm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.
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Affiliation(s)
- Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Natalia Zaretskaya
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Department of Experimental Psychology and Cognitive Neuroscience, Institute of Psychology, University of Graz, Graz, Austria
- BioTechMed-Graz, Austria
| | - Nina E Fultz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Ned A Ohringer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Akshay S Chaudhari
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, United States
| | - Yuxin Hu
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, United States
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
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32
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Lu PJ, Yoo Y, Rahmanzadeh R, Galbusera R, Weigel M, Ceccaldi P, Nguyen TD, Spincemaille P, Wang Y, Daducci A, La Rosa F, Bach Cuadra M, Sandkühler R, Nael K, Doshi A, Fayad ZA, Kuhle J, Kappos L, Odry B, Cattin P, Gibson E, Granziera C. GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology. NEUROIMAGE-CLINICAL 2020; 29:102522. [PMID: 33360973 PMCID: PMC7773673 DOI: 10.1016/j.nicl.2020.102522] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/11/2020] [Accepted: 11/30/2020] [Indexed: 12/24/2022]
Abstract
INTRODUCTION During the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood. METHODS GAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure. RESULTS In the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p < 0.001), respectively. CONCLUSIONS This work demonstrates that the proposed GAMER MRI might be a useful method to assess the relative importance of MRI measures in neurological diseases with focal pathology. Moreover, the obtained AWs may in fact help to choose the best combination of MR contrasts for a specific classification problem.
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Affiliation(s)
- Po-Jui Lu
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Youngjin Yoo
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Reza Rahmanzadeh
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Riccardo Galbusera
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Pascal Ceccaldi
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | | | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Robin Sandkühler
- Center for Medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA, USA; Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amish Doshi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zahi A Fayad
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; BioMedical Engineering and Imaging Institute, Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Benjamin Odry
- AI for Clinical Analytics, Covera Health, New York, NY, USA
| | - Philippe Cattin
- Center for Medical Image Analysis & Navigation, Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Eli Gibson
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
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Oran OF, Klassen LM, Gilbert KM, Gati JS, Menon RS. Elimination of low-inversion-efficiency induced artifacts in whole-brain MP2RAGE using multiple RF-shim configurations at 7 T. NMR IN BIOMEDICINE 2020; 33:e4387. [PMID: 32749022 DOI: 10.1002/nbm.4387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 07/10/2020] [Accepted: 07/16/2020] [Indexed: 06/11/2023]
Abstract
The magnetization-prepared two-rapid-gradient-echo (MP2RAGE) sequence is used for structural T1 -weighted imaging and T1 mapping of the human brain. In this sequence, adiabatic inversion RF pulses are commonly used, which require the B1+ magnitude to be above a certain threshold. Achieving this threshold in the whole brain may not be possible at ultra-high fields because of the short RF wavelength. This results in low-inversion regions especially in the inferior brain (eg cerebellum and temporal lobes), which is reflected as regions of bright signal in MP2RAGE images. This study aims at eliminating the low-inversion-efficiency induced artifacts in MP2RAGE images at 7 T. The proposed technique takes advantage of parallel RF transmission systems by splitting the brain into two overlapping slabs and calculating the complex weights of transmit channels (ie RF shims) on these slabs for excitation and inversion independently. RF shims were calculated using fast methods implemented in the standard workflow. The excitation RF pulse was designed to obtain slabs with flat plateaus and sharp edges. These slabs were joined into a single volume during the online image reconstruction. The two-slab strategy naturally results in a signal-to-noise ratio loss; however, it allowed the use of independent shims to make the B1+ field exceed the adiabatic threshold in the inferior brain, eliminating regions of low inversion efficiency. Accordingly, the normalized root-mean-square errors in the inversion were reduced to below 2%. The two-slab strategy was found to outperform subject-specific kT -point inversion RF pulses in terms of inversion error. The proposed strategy is a simple yet effective method to eliminate low-inversion-efficiency artifacts; consequently, MP2RAGE-based, artifact-free T1 -weighted structural images were obtained in the whole brain at 7 T.
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Affiliation(s)
- Omer F Oran
- Centre for Functional and Metabolic Mapping, University of Western Ontario, London, Ontario, Canada
| | - L Martyn Klassen
- Centre for Functional and Metabolic Mapping, University of Western Ontario, London, Ontario, Canada
| | - Kyle M Gilbert
- Centre for Functional and Metabolic Mapping, University of Western Ontario, London, Ontario, Canada
| | - Joseph S Gati
- Centre for Functional and Metabolic Mapping, University of Western Ontario, London, Ontario, Canada
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
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34
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Barquero G, La Rosa F, Kebiri H, Lu PJ, Rahmanzadeh R, Weigel M, Fartaria MJ, Kober T, Théaudin M, Du Pasquier R, Sati P, Reich DS, Absinta M, Granziera C, Maggi P, Bach Cuadra M. RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis. NEUROIMAGE-CLINICAL 2020; 28:102412. [PMID: 32961401 PMCID: PMC7509077 DOI: 10.1016/j.nicl.2020.102412] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 08/23/2020] [Accepted: 09/01/2020] [Indexed: 12/22/2022]
Abstract
OBJECTIVES In multiple sclerosis (MS), the presence of a paramagnetic rim at the edge of non-gadolinium-enhancing lesions indicates perilesional chronic inflammation. Patients featuring a higher paramagnetic rim lesion burden tend to have more aggressive disease. The objective of this study was to develop and evaluate a convolutional neural network (CNN) architecture (RimNet) for automated detection of paramagnetic rim lesions in MS employing multiple magnetic resonance (MR) imaging contrasts. MATERIALS AND METHODS Imaging data were acquired at 3 Tesla on three different scanners from two different centers, totaling 124 MS patients, and studied retrospectively. Paramagnetic rim lesion detection was independently assessed by two expert raters on T2*-phase images, yielding 462 rim-positive (rim+) and 4857 rim-negative (rim-) lesions. RimNet was designed using 3D patches centered on candidate lesions in 3D-EPI phase and 3D FLAIR as input to two network branches. The interconnection of branches at both the first network blocks and the last fully connected layers favors the extraction of low and high-level multimodal features, respectively. RimNet's performance was quantitatively evaluated against experts' evaluation from both lesion-wise and patient-wise perspectives. For the latter, patients were categorized based on a clinically relevant threshold of 4 rim+ lesions per patient. The individual prediction capabilities of the images were also explored and compared (DeLong test) by testing a CNN trained with one image as input (unimodal). RESULTS The unimodal exploration showed the superior performance of 3D-EPI phase and 3D-EPI magnitude images in the rim+/- classification task (AUC = 0.913 and 0.901), compared to the 3D FLAIR (AUC = 0.855, Ps < 0.0001). The proposed multimodal RimNet prototype clearly outperformed the best unimodal approach (AUC = 0.943, P < 0.0001). The sensitivity and specificity achieved by RimNet (70.6% and 94.9%, respectively) are comparable to those of experts at the lesion level. In the patient-wise analysis, RimNet performed with an accuracy of 89.5% and a Dice coefficient (or F1 score) of 83.5%. CONCLUSIONS The proposed prototype showed promising performance, supporting the usage of RimNet for speeding up and standardizing the paramagnetic rim lesions analysis in MS.
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Affiliation(s)
- Germán Barquero
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland; Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland; Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Hamza Kebiri
- Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Po-Jui Lu
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Reza Rahmanzadeh
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Mário João Fartaria
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Tobias Kober
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Marie Théaudin
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Renaud Du Pasquier
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Martina Absinta
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Pietro Maggi
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Department of Neurology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland; Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland.
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Morel B, Piredda GF, Cottier JP, Tauber C, Destrieux C, Hilbert T, Sirinelli D, Thiran JP, Maréchal B, Kober T. Normal volumetric and T1 relaxation time values at 1.5 T in segmented pediatric brain MRI using a MP2RAGE acquisition. Eur Radiol 2020; 31:1505-1516. [PMID: 32885296 DOI: 10.1007/s00330-020-07194-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 07/02/2020] [Accepted: 08/13/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES This study introduced a tailored MP2RAGE-based brain acquisition for a comprehensive assessment of the normal maturing brain. METHODS Seventy normal patients (35 girls and 35 boys) from 1 to 16 years of age were recruited within a prospective monocentric study conducted from a single University Hospital. Brain MRI examinations were performed at 1.5 T using a 20-channel head coil and an optimized 3D MP2RAGE sequence with a total acquisition time of 6:36 min. Automated 38 region segmentation was performed using the MorphoBox (template registration, bias field correction, brain extraction, and tissue classification) which underwent a major adaptation of three age-group T1-weighted templates. Volumetry and T1 relaxometry reference ranges were established using a logarithmic model and a modified Gompertz growth respectively. RESULTS Detailed automated brain segmentation and T1 mapping were successful in all patients. Using these data, an age-dependent model of normal brain maturation with respect to changes in volume and T1 relaxometry was established. After an initial rapid increase until 24 months of life, the total intracranial volume was found to converge towards 1400 mL during adolescence. The expected volumes of white matter (WM) and cortical gray matter (GM) showed a similar trend with age. After an initial major decrease, T1 relaxation times were observed to decrease progressively in all brain structures. The T1 drop in the first year of life was more pronounced in WM (from 1000-1100 to 650-700 ms) than in GM structures. CONCLUSION The 3D MP2RAGE sequence allowed to establish brain volume and T1 relaxation time normative ranges in pediatrics. KEY POINTS • The 3D MP2RAGE sequence provided a reliable quantitative assessment of brain volumes and T1 relaxation times during childhood. • An age-dependent model of normal brain maturation was established. • The normative ranges enable an objective comparison to a normal cohort, which can be useful to further understand, describe, and identify neurodevelopmental disorders in children.
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Affiliation(s)
- Baptiste Morel
- Inserm UMR 1253, iBrain, Université de Tours, Tours, France. .,Pediatric Radiology Department, Clocheville Hospital, CHRU de Tours, 49 Boulevard Beranger, 37000, Tours, France.
| | - Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique FÉdÉrale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Clovis Tauber
- Inserm UMR 1253, iBrain, Université de Tours, Tours, France
| | | | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique FÉdÉrale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique FÉdÉrale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique FÉdÉrale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique FÉdÉrale de Lausanne (EPFL), Lausanne, Switzerland
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36
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Alonso J, Pareto D, Alberich M, Kober T, Maréchal B, Lladó X, Rovira A. Assessment of brain volumes obtained from MP-RAGE and MP2RAGE images, quantified using different segmentation methods. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 33:757-767. [DOI: 10.1007/s10334-020-00854-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 05/14/2020] [Accepted: 05/19/2020] [Indexed: 11/30/2022]
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37
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Gonzalez-Escamilla G, Ciolac D, De Santis S, Radetz A, Fleischer V, Droby A, Roebroeck A, Meuth SG, Muthuraman M, Groppa S. Gray matter network reorganization in multiple sclerosis from 7-Tesla and 3-Tesla MRI data. Ann Clin Transl Neurol 2020; 7:543-553. [PMID: 32255566 PMCID: PMC7187719 DOI: 10.1002/acn3.51029] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 03/13/2020] [Accepted: 03/13/2020] [Indexed: 12/21/2022] Open
Abstract
Objective The objective of this study was to determine the ability of 7T‐MRI for characterizing brain tissue integrity in early relapsing‐remitting MS patients compared to conventional 3T‐MRI and to investigate whether 7T‐MRI improves the performance for detecting cortical gray matter neurodegeneration and its associated network reorganization dynamics. Methods Seven early relapsing‐remitting MS patients and seven healthy individuals received MRI at 7T and 3T, whereas 30 and 40 healthy controls underwent separate 3T‐ and 7T‐MRI sessions, respectively. Surface‐based cortical thickness (CT) and gray‐to‐white contrast (GWc) measures were used to model morphometric networks, analyzed with graph theory by means of modularity, clustering coefficient, path length, and small‐worldness. Results 7T‐MRI had lower CT and higher GWc compared to 3T‐MRI in MS. CT and GWc measures robustly differentiated MS from controls at 3T‐MRI. 7T‐ and 3T‐MRI showed high regional correspondence for CT (r = 0.72, P = 2e‐78) and GWc (r = 0.83, P = 5.5e‐121) in MS patients. MS CT and GWc morphometric networks at 7T‐MRI showed higher modularity, clustering coefficient, and small‐worldness than 3T, also compared to controls. Interpretation 7T‐MRI allows to more precisely quantify morphometric alterations across the cortical mantle and captures more sensitively MS‐related network reorganization. Our findings open new avenues to design more accurate studies quantifying brain tissue loss and test treatment effects on tissue repair.
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Affiliation(s)
- Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Dumitru Ciolac
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | | | - Angela Radetz
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Amgad Droby
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Sven G Meuth
- Department of Neurology with Institute of Translational Neurology, University of Münster, Münster, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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Yan S, Qian T, Maréchal B, Kober T, Zhang X, Zhu J, Lei J, Li M, Jin Z. Test-retest variability of brain morphometry analysis: an investigation of sequence and coil effects. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:12. [PMID: 32055603 DOI: 10.21037/atm.2019.11.149] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background Precise and reliable brain morphometry analysis is critical for clinical and research purposes. The magnetization-prepared rapid gradient echo (MPRAGE), multi-echo MPRAGE (MEMPRAGE) and magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) sequences have all been used to acquire brain structural images, but it is unclear which of these sequences is the most suitable for brain morphometry and whether the number of coil channels (20 or 32) affects scan precision. This study aimed to assess the impact of T1-weighted image acquisition variables (sequence and head coil) on the repeatability of resultant automated volumetric measurements. Methods Twenty-four healthy volunteers underwent back-to-back scanning protocols with three sequences and two different coils (i.e., six scanning conditions in total) presented in a randomized order in a single session. MorphoBox prototype and FreeSurfer were used for brain segmentation. Brain structures were divided into cortical and subcortical regions for more precise analysis. The acquired volume and thickness values were used to calculate test-retest variability (TRV) values. TRV values from the six different combinations were compared for total brain structures, total cortical structures, total subcortical structures, and every single structure. Results The median TRV value for all brain regions was 1.23% with MorphoBox and 3.14% with FreeSurfer. When using FreeSurfer results to compare the six combinations, for total brain structures volume and total cortical structures volume and thickness, the MEMPRAGE-32 channel combination showed significantly lower TRV values than the others (P<0.01). Similar results were observed with MorphoBox. For total subcortical structures, the MP2RAGE-32 channel combination showed the lowest TRV values with both MorphoBox (lower about 0.01% to 0.17%) and FreeSurfer analyses (lower about 0.02% to 0.37%). Conclusions TRV values were generally low, indicating generally high reliability for every region. The MEMPRAGE sequence was the most reliable of the three sequences for total brain structures and cortical structures. However, MP2RAGE was the most reliable for subcortical structures. The 32-channel coil showed better repeatability results than the 20-channel coil.
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Affiliation(s)
- Shuang Yan
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Tianyi Qian
- Department of MR Collaboration, Siemens Healthcare Ltd., Beijing 100102, China
| | - Bénédicte Maréchal
- Department of Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Department of Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Xianchang Zhang
- Department of MR Collaboration, Siemens Healthcare Ltd., Beijing 100102, China
| | - Jinxia Zhu
- Department of MR Collaboration, Siemens Healthcare Ltd., Beijing 100102, China
| | - Jing Lei
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Mingli Li
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
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Piredda GF, Hilbert T, Granziera C, Bonnier G, Meuli R, Molinari F, Thiran JP, Kober T. Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology. Magn Reson Med 2019; 83:337-351. [PMID: 31418910 DOI: 10.1002/mrm.27927] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 07/08/2019] [Accepted: 07/12/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE To exploit the improved comparability and hardware independency of quantitative MRI, databases of MR physical parameters in healthy tissue are required, to which tissue properties of patients can be compared. In this work, normative values for longitudinal and transverse relaxation times in the brain were established and tested in single-subject comparisons for detection of abnormal relaxation times. METHODS Relaxometry maps of the brain were acquired from 52 healthy volunteers. After spatially normalizing the volumes into a common space, T1 and T2 inter-subject variability within the healthy cohort was modeled voxel-wise. A method for a single-subject comparison against the atlases was developed by computing z-scores with respect to the established healthy norms. The comparison was applied to two multiple sclerosis and one clinically isolated syndrome cases for a proof of concept. RESULTS The established atlases exhibit a low variation in white matter structures (median RMSE of models equal to 32 ms for T1 and 4 ms for T2 ), indicating that relaxation times are in a narrow range for normal tissues. The proposed method for single-subject comparison detected relaxation time deviations from healthy norms in the example patient data sets. Relaxation times were found to be increased in brain lesions (mean z-scores >5). Moreover, subtle and confluent differences (z-scores ~2-4) were observed in clinically plausible regions (between lesions, corpus callosum). CONCLUSIONS Brain T1 and T2 quantitative norms were derived voxel-wise with low variability in healthy tissue. Example patient deviation maps demonstrated good sensitivity of the atlases for detecting relaxation time alterations.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Guillaume Bonnier
- Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,Swiss Center for Electronics and Microtechnology, Neuchatel, Switzerland
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecommunication, Polytechnic University of Turin, Turin, Italy
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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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: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [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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Boto J, Askin NC, Regnaud A, Kober T, Gkinis G, Lazeyras F, Lövblad KO, Vargas MI. Cerebral Gray and White Matter Involvement in Anorexia Nervosa Evaluated by T1, T2, and T2* Mapping. J Neuroimaging 2019; 29:598-604. [PMID: 31259451 DOI: 10.1111/jon.12647] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 05/29/2019] [Accepted: 06/14/2019] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND AND PURPOSE Changes in the brain composition of anorexics could potentially be expected, opening the door to new imaging approaches where quantitative and qualitative MRI have a role. Our purpose was to investigate anorexia-related brain dehydration and myelin depletion by analyzing T1, T2, and T2* relaxation times of different brain structures in anorexics and controls. METHODS Thirty-eight anorexic female patients (mean age, 26.2 years; age range, 16.2-48.7 years; mean BMI, 14.5 kg/m2 ; BMI range, 10.0-18.4 kg/m2 ) underwent brain MRI between August 2014 and August 2018. Controls were 16 healthy females (mean age, 28.0 years; age range, 22.3-34.7 years; mean BMI, 20.9 kg/m2 ; BMI range, 18.4-26.6 kg/m2 ). T1, T2, and T2* relaxation times were obtained for different brain structures in anorexics and controls as part of this retrospective case-control study. RESULTS The T1 relaxation times of gray and white matter were significantly lower in anorexics (P = .009), whereas the T2 relaxation times of gray matter were higher (P < .001). There were no statistically significant differences in gray matter T2* relaxation times or in white matter T2 and T2* relaxation times between anorexics and controls. Occipital lobe gray matter showed the shortest T1, T2, and T2* relaxation times of all brain regions (P < .05). CONCLUSIONS T1 shortening in anorexics suggests both dehydration and myelin loss, whereas T2 prolongation points toward myelin loss (myelin water has lower T2), which seems to be less discernible in white matter. Shorter overall relaxation times in the most posterior regions of the brain suggest higher iron content.
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Affiliation(s)
- José Boto
- Division of Neuroradiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Nurten Ceren Askin
- Division of Radiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Alice Regnaud
- Division of Neuroradiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare HC CEMEA SUI DI BM PI, Siemens ACIT, Lausanne, Switzerland.,Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - François Lazeyras
- Division of Radiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Karl-Olof Lövblad
- Division of Neuroradiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Maria Isabel Vargas
- Division of Neuroradiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
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King JB, Prigge MBD, King CK, Morgan J, Weathersby F, Fox JC, Dean DC, Freeman A, Villaruz JAM, Kane KL, Bigler ED, Alexander AL, Lange N, Zielinski B, Lainhart JE, Anderson JS. Generalizability and reproducibility of functional connectivity in autism. Mol Autism 2019; 10:27. [PMID: 31285817 PMCID: PMC6591952 DOI: 10.1186/s13229-019-0273-5] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 04/24/2019] [Indexed: 12/18/2022] Open
Abstract
Background Autism is hypothesized to represent a disorder of brain connectivity, yet patterns of atypical functional connectivity show marked heterogeneity across individuals. Methods We used a large multi-site dataset comprised of a heterogeneous population of individuals with autism and typically developing individuals to compare a number of resting-state functional connectivity features of autism. These features were also tested in a single site sample that utilized a high-temporal resolution, long-duration resting-state acquisition technique. Results No one method of analysis provided reproducible results across research sites, combined samples, and the high-resolution dataset. Distinct categories of functional connectivity features that differed in autism such as homotopic, default network, salience network, long-range connections, and corticostriatal connectivity, did not align with differences in clinical and behavioral traits in individuals with autism. One method, lag-based functional connectivity, was not correlated to other methods in describing patterns of resting-state functional connectivity and their relationship to autism traits. Conclusion Overall, functional connectivity features predictive of autism demonstrated limited generalizability across sites, with consistent results only for large samples. Different types of functional connectivity features do not consistently predict different symptoms of autism. Rather, specific features that predict autism symptoms are distributed across feature types. Electronic supplementary material The online version of this article (10.1186/s13229-019-0273-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jace B King
- 1Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT 84108 USA.,2Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, UT 84112 USA
| | - Molly B D Prigge
- 3Department of Pediatrics, University of Utah, Salt Lake City, UT 84108 USA.,4Waisman Center, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Carolyn K King
- 1Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT 84108 USA.,3Department of Pediatrics, University of Utah, Salt Lake City, UT 84108 USA
| | - Jubel Morgan
- 1Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT 84108 USA.,3Department of Pediatrics, University of Utah, Salt Lake City, UT 84108 USA.,4Waisman Center, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Fiona Weathersby
- 1Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT 84108 USA.,5Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112 USA
| | - J Chancellor Fox
- 1Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT 84108 USA
| | - Douglas C Dean
- 4Waisman Center, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Abigail Freeman
- 4Waisman Center, University of Wisconsin-Madison, Madison, WI 53705 USA.,6Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719 USA
| | | | - Karen L Kane
- 4Waisman Center, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Erin D Bigler
- 7Psychology Department and Neuroscience Center, Brigham Young University, Provo, UT 84604 USA
| | - Andrew L Alexander
- 4Waisman Center, University of Wisconsin-Madison, Madison, WI 53705 USA.,6Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719 USA.,8Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705 USA
| | - Nicholas Lange
- 9McLean Hospital and Department of Psychiatry, Harvard University, Cambridge, MA 02478 USA
| | - Brandon Zielinski
- 3Department of Pediatrics, University of Utah, Salt Lake City, UT 84108 USA.,10Department of Neurology, University of Utah, Salt Lake City, UT 84132 USA
| | - Janet E Lainhart
- 4Waisman Center, University of Wisconsin-Madison, Madison, WI 53705 USA.,6Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719 USA
| | - Jeffrey S Anderson
- 1Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT 84108 USA.,2Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, UT 84112 USA.,5Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112 USA
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Radial MP2RAGE sequence for rapid 3D T 1 mapping of mouse abdomen: application to hepatic metastases. Eur Radiol 2019; 29:5844-5851. [PMID: 30888483 DOI: 10.1007/s00330-019-06081-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 01/22/2019] [Accepted: 02/07/2019] [Indexed: 10/27/2022]
Abstract
OBJECTIVES The T1 longitudinal recovery time is regarded as a biomarker of cancer treatment efficiency. In this scope, the Magnetization Prepared 2 RApid Gradient Echo (MP2RAGE) sequence relevantly complies with fast 3D T1 mapping. Nevertheless, with its Cartesian encoding scheme, it is very sensitive to respiratory motion. Consequently, a radial encoding scheme was implemented for the detection and T1 measurement of hepatic metastases in mice at 7T. METHODS A 3D radial encoding scheme was developed using a golden angle distribution for the k-space trajectories. As in that case, each projection contributes to the image contrast, the signal equations had to be modified. Phantoms containing increasing gadoteridol concentrations were used to determine the accuracy of the sequence in vitro. Healthy mice were repetitively scanned to assess the reproducibility of the T1 values. The growth of hepatic metastases was monitored. Undersampling robustness was also evaluated. RESULTS The accuracy of the T1 values obtained with the radial MP2RAGE sequence was > 90% compared to the Inversion-Recovery sequence. The motion robustness of this new sequence also enabled repeatable T1 measurements on abdominal organs. Hepatic metastases of less than 1-mm diameter were easily detected and T1 heterogeneities within the metastasis and between the metastases within the same animal were measured. With a twofold acceleration factor using undersampling, high-quality 3D T1 abdominal maps were achieved in 9 min. CONCLUSIONS The radial MP2RAGE sequence could be used for fast 3D T1 mapping, to detect and characterize metastases in regions subjected to respiratory motion. KEY POINTS • The Cartesian encoding of the MP2RAGE sequence was modified to a radial encoding. The modified sequence enabled accurate T 1 measurements on phantoms and on abdominal organs of mice. • Hepatic metastases were easily detected due to high contrast. Heterogeneity in T 1 was measured within the metastases and between each metastasis within the same animal. • As implementation of this sequence does not require specific hardware, we expect that it could be readily available for clinical practice in humans.
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Mainta IC, Vargas MI, Trombella S, Frisoni GB, Unschuld PG, Garibotto V. Hybrid PET-MRI in Alzheimer's Disease Research. Methods Mol Biol 2019; 1750:185-200. [PMID: 29512073 DOI: 10.1007/978-1-4939-7704-8_12] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Multiple factors, namely amyloid, tau, inflammation, metabolic, and perfusion changes, contribute to the cascade of neurodegeneration and functional decline occurring in Alzheimer's disease (AD). These molecular and cellular processes and related functional and morphological changes can be visualized in vivo by two imaging modalities, namely positron emission tomography (PET) and magnetic resonance imaging (MRI). These imaging biomarkers are now part of the diagnostic algorithm and of particular interest for patient stratification and targeted drug development.In this field the availability of hybrid PET/MR systems not only offers a comprehensive evaluation in a single imaging session, but also opens new possibilities for the integration of the two imaging information. Here, we cover the clinical protocols and practical details of FDG, amyloid, and tau PET/MR imaging as applied in our institutions.
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Affiliation(s)
- Ismini C Mainta
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland. .,Faculty of Medicine, Nuclear Medicine Department, Geneva University Medical Center, University of Geneva, Geneva, Switzerland.
| | - Maria I Vargas
- Faculty of Medicine, Nuclear Medicine Department, Geneva University Medical Center, University of Geneva, Geneva, Switzerland.,Division of Neuroradiology, Geneva University Hospitals, Geneva, Switzerland
| | - Sara Trombella
- Faculty of Medicine, Nuclear Medicine Department, Geneva University Medical Center, University of Geneva, Geneva, Switzerland
| | - Giovanni B Frisoni
- Faculty of Medicine, Nuclear Medicine Department, Geneva University Medical Center, University of Geneva, Geneva, Switzerland.,Department of Internal Medicine, Geneva University Hospitals, Geneva, Switzerland
| | - Paul G Unschuld
- Institute for Regenerative Medicine and Hospital for Psychogeriatric Medicine, University of Zurich, Zurich, Switzerland
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland.,Faculty of Medicine, Nuclear Medicine Department, Geneva University Medical Center, University of Geneva, Geneva, Switzerland
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Application of Automated Brain Segmentation and Fiber Tracking in Hemimegalencephaly. Can J Neurol Sci 2019; 46:258-260. [PMID: 30665473 DOI: 10.1017/cjn.2018.381] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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46
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Pittau F, Baud MO, Jorge J, Xin L, Grouiller F, Iannotti GR, Seeck M, Lazeyras F, Vulliémoz S, Vargas MI. MP2RAGE and Susceptibility-Weighted Imaging in Lesional Epilepsy at 7T. J Neuroimaging 2018; 28:365-369. [DOI: 10.1111/jon.12523] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 04/27/2018] [Indexed: 01/22/2023] Open
Affiliation(s)
- Francesca Pittau
- Division of Neurology; Geneva University Hospitals; Geneva Switzerland
| | - Maxime O. Baud
- Division of Neurology; Geneva University Hospitals; Geneva Switzerland
| | - João Jorge
- Laboratory for Functional and Metabolic Imaging; École Polytechnique Fédérale de Lausanne; Lausanne Switzerland
| | - Lijing Xin
- Animal Imaging and Technology Core; Center for Biomedical Imaging; École Polytechnique Fédérale de Lausanne; Lausanne Switzerland
| | - Frédéric Grouiller
- Swiss Center for Affective Sciences; University of Geneva; Geneva Switzerland
| | | | - Margitta Seeck
- Division of Neurology; Geneva University Hospitals; Geneva Switzerland
| | - François Lazeyras
- Division of Radiology of Geneva University Hospitals and CIBM; Geneva Switzerland
| | - Serge Vulliémoz
- Division of Neurology; Geneva University Hospitals; Geneva Switzerland
| | - Maria Isabel Vargas
- Division of Neuroradiology of Geneva University Hospitals and Geneva University; Geneva Switzerland
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Sinnecker T, Granziera C, Wuerfel J, Schlaeger R. Future Brain and Spinal Cord Volumetric Imaging in the Clinic for Monitoring Treatment Response in MS. Curr Treat Options Neurol 2018; 20:17. [PMID: 29679165 DOI: 10.1007/s11940-018-0504-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
PURPOSE OF REVIEW Volumetric analysis of brain imaging has emerged as a standard approach used in clinical research, e.g., in the field of multiple sclerosis (MS), but its application in individual disease course monitoring is still hampered by biological and technical limitations. This review summarizes novel developments in volumetric imaging on the road towards clinical application to eventually monitor treatment response in patients with MS. RECENT FINDINGS In addition to the assessment of whole-brain volume changes, recent work was focused on the volumetry of specific compartments and substructures of the central nervous system (CNS) in MS. This included volumetric imaging of the deep brain structures and of the spinal cord white and gray matter. Volume changes of the latter indeed independently correlate with clinical outcome measures especially in progressive MS. Ultrahigh field MRI and quantitative MRI added to this trend by providing a better visualization of small compartments on highly resolving MR images as well as microstructural information. New developments in volumetric imaging have the potential to improve sensitivity as well as specificity in detecting and hence monitoring disease-related CNS volume changes in MS.
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Affiliation(s)
- Tim Sinnecker
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Medical Image Analysis Center Basel AG, Basel, Switzerland
- NeuroCure Clinical Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center Basel AG, Basel, Switzerland
- NeuroCure Clinical Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
- Berlin Ultrahigh Field Facility, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Regina Schlaeger
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
- Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
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Wang Y, Wang Y, Zhang Z, Xiong Y, Zhang Q, Yuan C, Guo H. Segmentation of gray matter, white matter, and CSF with fluid and white matter suppression using MP2RAGE. J Magn Reson Imaging 2018; 48:1540-1550. [DOI: 10.1002/jmri.26014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Accepted: 03/01/2018] [Indexed: 11/10/2022] Open
Affiliation(s)
- Yishi Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering; School of Medicine, Tsinghua University; Beijing China
| | - Yajie Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering; School of Medicine, Tsinghua University; Beijing China
| | - Zhe Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering; School of Medicine, Tsinghua University; Beijing China
| | - Yuhui Xiong
- Center for Biomedical Imaging Research, Department of Biomedical Engineering; School of Medicine, Tsinghua University; Beijing China
| | - Qiang Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering; School of Medicine, Tsinghua University; Beijing China
| | - Chun Yuan
- Center for Biomedical Imaging Research, Department of Biomedical Engineering; School of Medicine, Tsinghua University; Beijing China
- Vascular Imaging Laboratory, Department of Radiology; University of Washington; Seattle Washington USA
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering; School of Medicine, Tsinghua University; Beijing China
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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: 135] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [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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Haast RAM, Ivanov D, Uludağ K. The impact of B1+ correction on MP2RAGE cortical T 1 and apparent cortical thickness at 7T. Hum Brain Mapp 2018; 39:2412-2425. [PMID: 29457319 PMCID: PMC5969159 DOI: 10.1002/hbm.24011] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 02/09/2018] [Accepted: 02/10/2018] [Indexed: 01/06/2023] Open
Abstract
Determination of cortical thickness using MRI has often been criticized due to the presence of various error sources. Specifically, anatomical MRI relying on T1 contrast may be unreliable due to spatially variable image contrast between gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). Especially at ultra‐high field (≥ 7T) MRI, transmit and receive B1‐related image inhomogeneities can hamper correct classification of tissue types. In the current paper, we demonstrate that residual
B1+ (transmit) inhomogeneities in the T1‐weighted and quantitative T1 images using the MP2RAGE sequence at 7T lead to biases in cortical thickness measurements. As expected, post‐hoc correction for the spatially varying
B1+ profile reduced the apparent T1 values across the cortex in regions with low
B1+, and slightly increased apparent T1 in regions with high
B1+. As a result, improved contrast‐to‐noise ratio both at the GM‐CSF and GM‐WM boundaries can be observed leading to more accurate surface reconstructions and cortical thickness estimates. Overall, the changes in cortical thickness ranged between a 5% decrease to a 70% increase after
B1+ correction, reducing the variance of cortical thickness values across the brain dramatically and increasing the comparability with normative data. More specifically, the cortical thickness estimates increased in regions characterized by a strong decrease of apparent T1 after
B1+ correction in regions with low
B1+ due to improved detection of the pial surface. The current results suggest that cortical thickness can be more accurately determined using MP2RAGE data at 7T if
B1+ inhomogeneities are accounted for.
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Affiliation(s)
- Roy A M Haast
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.,Maastricht Centre for Systems Biology, Maastricht University, Maastricht, Netherlands
| | - Dimo Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Kâmil Uludağ
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
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