Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/20950
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dc.contributor.authorSchirmer, Markus D-
dc.contributor.authorDalca, Adrian V-
dc.contributor.authorSridharan, Ramesh-
dc.contributor.authorGiese, Anne-Katrin-
dc.contributor.authorDonahue, Kathleen L-
dc.contributor.authorNardin, Marco J-
dc.contributor.authorMocking, Steven J T-
dc.contributor.authorMcIntosh, Elissa C-
dc.contributor.authorFrid, Petrea-
dc.contributor.authorWasselius, Johan-
dc.contributor.authorCole, John W-
dc.contributor.authorHolmegaard, Lukas-
dc.contributor.authorJern, Christina-
dc.contributor.authorJimenez-Conde, Jordi-
dc.contributor.authorLemmens, Robin-
dc.contributor.authorLindgren, Arne G-
dc.contributor.authorMeschia, James F-
dc.contributor.authorRoquer, Jaume-
dc.contributor.authorRundek, Tatjana-
dc.contributor.authorSacco, Ralph L-
dc.contributor.authorSchmidt, Reinhold-
dc.contributor.authorSharma, Pankaj-
dc.contributor.authorSlowik, Agnieszka-
dc.contributor.authorThijs, Vincent N-
dc.contributor.authorWoo, Daniel-
dc.contributor.authorVagal, Achala-
dc.contributor.authorXu, Huichun-
dc.contributor.authorKittner, Steven J-
dc.contributor.authorMcArdle, Patrick F-
dc.contributor.authorMitchell, Braxton D-
dc.contributor.authorRosand, Jonathan-
dc.contributor.authorWorrall, Bradford B-
dc.contributor.authorWu, Ona-
dc.contributor.authorGolland, Polina-
dc.contributor.authorRost, Natalia S-
dc.date2019-05-29-
dc.date.accessioned2019-06-19T06:28:50Z-
dc.date.available2019-06-19T06:28:50Z-
dc.date.issued2019-05-29-
dc.identifier.citationNeuroImage. Clinical 2019; 23: 101884en_US
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/20950-
dc.description.abstractWhite matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of -2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.en_US
dc.language.isoeng-
dc.titleWhite matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study.en_US
dc.typeJournal Articleen_US
dc.identifier.journaltitleNeuroImage. Clinicalen_US
dc.identifier.affiliationDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAen_US
dc.identifier.affiliationDepartment of Neurology, Austin Health, Heidelberg, Victoria, Australiaen_US
dc.identifier.affiliationInstitute of Cardiovascular Research, St Peter's and Ashford Hospitals, Royal Holloway University of London (ICR2UL), Egham, UKen_US
dc.identifier.affiliationDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Lab, MIT, USAen_US
dc.identifier.affiliationDepartment of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germanyen_US
dc.identifier.affiliationAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USAen_US
dc.identifier.affiliationCenter for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USAen_US
dc.identifier.affiliationProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USAen_US
dc.identifier.affiliationStroke Division,The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australiaen_US
dc.identifier.affiliationDepartment of Clinical Sciences Lund, Neurology, Lund University, Lund, Swedenen_US
dc.identifier.affiliationDepartment of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Swedenen_US
dc.identifier.affiliationComputer Science and Artificial Intelligence Lab, MIT, USAen_US
dc.identifier.affiliationDepartments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USAen_US
dc.identifier.affiliationDivision of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USAen_US
dc.identifier.affiliationDepartment of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USAen_US
dc.identifier.affiliationDepartment of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USAen_US
dc.identifier.affiliationDepartment of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USAen_US
dc.identifier.affiliationDepartment of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USAen_US
dc.identifier.affiliationAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USAen_US
dc.identifier.affiliationComputer Science and Artificial Intelligence Lab, MIT, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USAen_US
dc.identifier.affiliationDepartment of Neurology, Mayo Clinic, Jacksonville, FL, USAen_US
dc.identifier.affiliationDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAen_US
dc.identifier.affiliationDepartment of Clinical Sciences Lund, Neurology, Lund University, Lund, Swedenen_US
dc.identifier.affiliationDepartment of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden; Department of Radiology, Neuroradiology, Skåne University Hospital, Malmö, Swedenen_US
dc.identifier.affiliationInstitute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Swedenen_US
dc.identifier.affiliationInstitute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Swedenen_US
dc.identifier.affiliationDepartment of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spainen_US
dc.identifier.affiliationDepartment of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven - University of Leuven, Leuven, Belgium; VIB, Vesalius Research Center, Laboratory of Neurobiology, Department of Neurology, University Hospitals Leuven, Leuven, Belgiumen_US
dc.identifier.affiliationDepartment of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spainen_US
dc.identifier.affiliationDepartment of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austriaen_US
dc.identifier.affiliationDepartment of Neurology, Jagiellonian University Medical College, Krakow, Polanden_US
dc.identifier.affiliationThe Florey Institute of Neuroscience and Mental Healthen_US
dc.identifier.doi10.1016/j.nicl.2019.101884en_US
dc.type.contentTexten_US
dc.identifier.orcid0000-0002-6614-8417en_US
dc.identifier.pubmedid31200151-
dc.type.austinJournal Article-
local.name.researcherThijs, Vincent N
item.languageiso639-1en-
item.grantfulltextnone-
item.openairetypeJournal Article-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.deptNeurology-
crisitem.author.deptThe Florey Institute of Neuroscience and Mental Health-
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