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White Matter Hyperintensity Quantification in Large-scale Clinical Acute Ischemic Stroke Cohorts - The MRI-GENIE Study

Institution:
1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
2Computer 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.
3Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany. Electronic address: mschirmer1@mgh.harvard.edu.
Publisher:
Elsevier Science
Publication Date:
May-2019
Journal:
Neuroimage Clin
Volume Number:
23
Pages:
101884
Citation:
Neuroimage Clin. 2019 May 29;23:101884.
PubMed ID:
31200151
PMCID:
PMC6562316
Appears in Collections:
NAC, SPL
Sponsors:
K23 NS064052/NS/NINDS NIH HHS/United States
R01 NS082285/NS/NINDS NIH HHS/United States
R01 NS086905/NS/NINDS NIH HHS/United States
P50 NS051343/NS/NINDS NIH HHS/United States
U01 NS069208/NS/NINDS NIH HHS/United States
R01 NS063925/NS/NINDS NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
R01 NS059775/NS/NINDS NIH HHS/United States
Generated Citation:
Schirmer M.D., Dalca A.V., Sridharan R., Giese A-K., Donahue K.L., Nardin M.J., Mocking S.J.T., McIntosh E.C., Frid P., Wasselius J., Cole J.W., Holmegaard L., Jern C., Jimenez-Conde J., Lemmens R., Lindgren A.G., Meschia J.F., Roquer J., Rundek T., Sacco R.L., Schmidt R., Sharma P., Slowik A., Thijs V., Woo D., Vagal A., Xu H., Kittner S.J., McArdle P.F., Mitchell B.D., Rosand J., Worrall B.B., Wu O., Golland P., Rost N.S. White Matter Hyperintensity Quantification in Large-scale Clinical Acute Ischemic Stroke Cohorts - The MRI-GENIE Study. Neuroimage Clin. 2019 May 29;23:101884. PMID: 31200151. PMCID: PMC6562316.
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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.