White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study
Schirmer, Markus D. (Massachusetts General Hospital (Boston))
Dalca, Adrian Vasile ![ORCID Identifier](/img/uab/orcid.ico)
(Massachusetts Institute of Technology)
Sridharan, Ramesh (Massachusetts Institute of Technology)
Giese, Anne-Katrin (Massachusetts General Hospital (Boston))
Donahue, Kathleen L. (Massachusetts General Hospital (Boston))
Nardin, Marco J. (Massachusetts General Hospital (Boston))
Mocking, Steven J. T. (Massachusetts General Hospital (Boston))
McIntosh, Elissa C. (Massachusetts General Hospital (Boston))
Frid, Petrea
(Lund University)
Wasselius, Johan (Lund University)
Cole, John W.
(University of Maryland School of Medicine)
Holmegaard, Lukas (University of Gothenburg)
Jern, Christina (University of Gothenburg)
Jimenez-Conde, Jordi
(Institut Hospital del Mar d'Investigacions Mèdiques)
Lemmens, Robin
(Leuven Research Institute for Neuroscience and Disease)
Lindgren, Arne G.
(Lund University)
Meschia, James F.
(Mayo Clinic (Jacksonville, Estats Units d'Amèrica))
Roquer González, Jaume
(Institut Hospital del Mar d'Investigacions Mèdiques)
Rundek, Tatjana
(University of Miami. Miller School of Medicine)
Sacco, Ralph L. (University of Miami. Miller School of Medicine)
Schmidt, Reinhold (Medical University Graz)
Sharma, Pankaj (Royal Holloway University of London)
Slowik, Agnieszka (Jagiellonian University Medical College)
Thijs, Vincent
(Florey Institute of Neuroscience and Mental Health)
Woo, Daniel (University of Cincinnati College of Medicine)
Vagal, Achala (University of Cincinnati College of Medicine)
Xu, Huichun
(University of Maryland School of Medicine)
Kittner, Steven J. (University of Maryland School of Medicine)
McArdle, Patrick F. (University of Maryland School of Medicine)
Mitchell, Braxton D.
(University of Maryland School of Medicine)
Rosand, Jonathan (Massachusetts General Hospital (Boston))
Worrall, Bradford B.
(University of Virginia)
Wu, Ona (Massachusetts General Hospital (Boston))
Golland, Polina (Massachusetts Institute of Technology)
Rost, Natalia S. (Massachusetts General Hospital (Boston))
Universitat Autònoma de Barcelona
Date: |
2019 |
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. |
Grants: |
European Commission 753896
|
Rights: |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. ![Creative Commons](/img/licenses/by.ico) |
Language: |
Anglès |
Document: |
Article ; recerca ; Versió publicada |
Published in: |
NeuroImage. Clinical, Vol. 23 (may 2019) , ISSN 2213-1582 |
DOI: 10.1016/j.nicl.2019.101884
PMID: 31200151
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Record created 2020-07-06, last modified 2024-06-03