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dc.contributor.authorSchirmer, M.D.
dc.contributor.authorDalca, A.V.
dc.contributor.authorSridharan, R.
dc.date.accessioned2019-09-13T17:02:33Z
dc.date.available2019-09-13T17:02:33Z
dc.date.issued2019
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85067075360&doi=10.1016%2fj.nicl.2019.101884&partnerID=40&md5=a6d7c0ac9207a0a1b453bd0c306e7c7f
dc.identifier.urihttp://hdl.handle.net/10713/10819
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. Copyright 2019 The Authorsen_US
dc.description.sponsorshipThis study was supported by NIH-NINDS (MRI-GENIE: R01NS086905, K23NS064052 , R01NS082285, SiGN: U01 NS069208, R01NS059775, R01NS063925 , R01NS082285 , P50NS051343 , R01NS086905 , U01 NS069208), NIH NIBIB NAC ( P41EB015902).en_US
dc.description.urihttps://doi.org/10.1016/j.nicl.2019.101884en_US
dc.language.isoen-USen_US
dc.publisherElsevier Inc.en_US
dc.relation.ispartofNeuroImage: Clinical
dc.subjectacute ischemic strokeen_US
dc.subjectwhite matter hypersensitivityen_US
dc.subject.meshMagnetic Resonance Imagingen_US
dc.titleWhite matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE studyen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.nicl.2019.101884
dc.identifier.pmid31200151


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