Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/20950
Title: White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study.
Authors: Schirmer, Markus D;Dalca, Adrian V;Sridharan, Ramesh;Giese, Anne-Katrin;Donahue, Kathleen L;Nardin, Marco J;Mocking, Steven J T;McIntosh, Elissa C;Frid, Petrea;Wasselius, Johan;Cole, John W;Holmegaard, Lukas;Jern, Christina;Jimenez-Conde, Jordi;Lemmens, Robin;Lindgren, Arne G;Meschia, James F;Roquer, Jaume;Rundek, Tatjana;Sacco, Ralph L;Schmidt, Reinhold;Sharma, Pankaj;Slowik, Agnieszka;Thijs, Vincent N;Woo, Daniel;Vagal, Achala;Xu, Huichun;Kittner, Steven J;McArdle, Patrick F;Mitchell, Braxton D;Rosand, Jonathan;Worrall, Bradford B;Wu, Ona;Golland, Polina;Rost, Natalia S
Affiliation: Stroke Division,The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Lab, MIT, USA
Institute of Cardiovascular Research, St Peter's and Ashford Hospitals, Royal Holloway University of London (ICR2UL), Egham, UK
Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden
Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
Department of Neurology, Austin Health, Heidelberg, Victoria, Australia
Computer Science and Artificial Intelligence Lab, MIT, USA
Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USA
Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
Computer Science and Artificial Intelligence Lab, MIT, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden; Department of Radiology, Neuroradiology, Skåne University Hospital, Malmö, Sweden
Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
Department 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, Belgium
Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
Issue Date: 29-May-2019
EDate: 2019-05-29
Citation: NeuroImage. Clinical 2019; 23: 101884
Abstract: White 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.
URI: http://ahro.austin.org.au/austinjspui/handle/1/20950
DOI: 10.1016/j.nicl.2019.101884
ORCID: 0000-0002-6614-8417
PubMed URL: 31200151
Type: Journal Article
Appears in Collections:Journal articles

Files in This Item:
There are no files associated with this item.


Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.