White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study

Markus D Schirmer; Adrian V Dalca; Ramesh Sridharan; Anne-Katrin Giese; Kathleen L Donahue; Marco J Nardin; Steven J T Mocking; Elissa C McIntosh; Petrea Frid; Johan Wasselius; John W Cole; Lukas Holmegaard; Christina Jern; Jordi Jimenez-Conde; Robin Lemmens; Arne G Lindgren; James F Meschia; Jaume Roquer; Tatjana Rundek; Ralph L Sacco; Reinhold Schmidt; Pankaj Sharma; Agnieszka Slowik; Vincent Thijs; Daniel Woo; Achala Vagal; Huichun Xu; Steven J Kittner; Patrick F McArdle; Braxton D Mitchell; Jonathan Rosand; Bradford B Worrall; Ona Wu; Polina Golland; Natalia S Rost

doi:10.1016/j.nicl.2019.101884

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White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study

Journal article

Open access

Peer reviewed

White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study

Markus D Schirmer, Adrian V Dalca, Ramesh Sridharan, Anne-Katrin Giese, Kathleen L Donahue, Marco J Nardin, Steven J T Mocking, Elissa C McIntosh, Petrea Frid, Johan Wasselius, …

NeuroImage clinical, Vol.23, pp.101884-101884

2019

DOI: https://doi.org/10.1016/j.nicl.2019.101884

PMCID: PMC6562316

PMID: 31200151

Abstract

Neuroimaging - methods

Stroke - diagnostic imaging

Humans

Middle Aged

Magnetic Resonance Imaging - methods

Male

White Matter - diagnostic imaging

Aged, 80 and over

Adult

Female

Aged

Brain Ischemia - diagnostic imaging

Cohort Studies

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.

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 1 Clinical & Life Sciences; 1.52 Neurodegenerative Diseases; 1.52.60 Dementia
Web Of Science research areas: Neuroimaging
ESI research areas: Neuroscience & Behavior

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Title: White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study
Creators: Markus D Schirmer - Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Lab, MIT, USA; Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany. Electronic address: mschirmer1@mgh.harvard.edu
Adrian V Dalca - Computer Science and Artificial Intelligence Lab, MIT, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
Ramesh Sridharan - Computer Science and Artificial Intelligence Lab, MIT, USA
Anne-Katrin Giese - Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Kathleen L Donahue - Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Marco J Nardin - Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Steven J T Mocking - Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
Elissa C McIntosh - Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
Petrea Frid - Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
Johan Wasselius - Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden; Department of Radiology, Neuroradiology, Skåne University Hospital, Malmö, Sweden
John W Cole - Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
Lukas Holmegaard - Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
Christina Jern - Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
Jordi Jimenez-Conde - Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
Robin Lemmens - 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
Arne G Lindgren - Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden; Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden
James F Meschia - Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
Jaume Roquer - Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
Tatjana Rundek - Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
Ralph L Sacco - Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
Reinhold Schmidt - Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
Pankaj Sharma - Institute of Cardiovascular Research, St Peter's and Ashford Hospitals, Royal Holloway University of London (ICR2UL), Egham, UK
Agnieszka Slowik - Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
Vincent Thijs - Stroke Division, Australia and Department of Neurology, Austin Health, Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia
Daniel Woo - Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Achala Vagal - Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Huichun Xu - Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
Steven J Kittner - Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
Patrick F McArdle - Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
Braxton D Mitchell - Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
Jonathan Rosand - Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
Bradford B Worrall - Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USA
Ona Wu - Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
Polina Golland - Computer Science and Artificial Intelligence Lab, MIT, USA
Natalia S Rost - Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Publication Details: NeuroImage clinical, Vol.23, pp.101884-101884
Publisher: Netherlands
Grant note: R01 NS063925 / NINDS NIH HHS K23 NS064052 / NINDS NIH HHS P50 NS051343 / NINDS NIH HHS R01 NS086905 / NINDS NIH HHS R01 NS082285 / NINDS NIH HHS R01 NS100178 / NINDS NIH HHS U01 NS069208 / NINDS NIH HHS R01 NS059775 / NINDS NIH HHS
Academic Unit: Leadership Department; Miller School of Medicine; UMMG Department of Neurology
Language: English
Resource Type: Journal article
PMID: 31200151
PMCID: PMC6562316
Record Identifier: 991031576683002976