White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - the MRI-GENIE Study

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  • dc.contributor.author Schirmer, Markus D.
  • dc.contributor.author Jimenez-Conde, Jordi
  • dc.contributor.author Roquer, Jaume
  • dc.contributor.author Rost, Natalia
  • dc.contributor.author MRI-GENIE Investigators
  • dc.date.accessioned 2020-03-05T08:25:02Z
  • dc.date.available 2020-03-05T08:25:02Z
  • dc.date.issued 2019
  • dc.description.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.
  • dc.description.sponsorship This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 753896 (M.D. Schirmer). This study was supported by NIH-NINDS (MRI-GENIE: R01NS086905 - PI N.Rost, K23NS064052, R01NS082285 - N. Rost, SiGN: U01 NS069208 - J. Rosand, S. Kittner, R01NS059775, R01NS063925, R01NS082285,P50NS051343, R01NS086905, U01 NS069208 - O. Wu), NIH NIBIBNAC (P41EB015902–P. Golland).
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Schirmer MD, Dalca AV, Sridharan R, Giese AK, Donahue KL, Nardin MJ. Et al. White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - the MRI-GENIE Study. Neuroimage Clin. 2019; 23: 101884. DOI: 10.1016/j.nicl.2019.101884
  • dc.identifier.doi http://dx.doi.org/10.1016/j.nicl.2019.101884
  • dc.identifier.issn 2213-1582
  • dc.identifier.uri http://hdl.handle.net/10230/43805
  • dc.language.iso eng
  • dc.publisher Elsevier
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/753896
  • dc.rights Copyright © 2019 The Authors. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.other Malalties cerebrovasculars
  • dc.title White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - the MRI-GENIE Study
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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