White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study
MetadataShow full item record
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 Authors
SponsorsThis 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).
Identifier to cite or link to this itemhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85067075360&doi=10.1016%2fj.nicl.2019.101884&partnerID=40&md5=a6d7c0ac9207a0a1b453bd0c306e7c7f; http://hdl.handle.net/10713/10819
- Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI.
- Authors: Moeskops P, de Bresser J, Kuijf HJ, Mendrik AM, Biessels GJ, Pluim JPW, Išgum I
- Issue date: 2018
- White matter hyperintensity burden in acute stroke patients differs by ischemic stroke subtype.
- Authors: Giese AK, Schirmer MD, Dalca AV, Sridharan R, Donahue KL, Nardin M, Irie R, McIntosh EC, Mocking SJT, Xu H, Cole JW, Giralt-Steinhauer E, Jimenez-Conde J, Jern C, Kleindorfer DO, Lemmens R, Wasselius J, Lindgren A, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Thijs V, Worrall BB, Woo D, Kittner SJ, McArdle PF, Mitchell BD, Rosand J, Meschia JF, Wu O, Golland P, Rost NS, International Stroke Genetics Consortium and the MRI-GENIE Investigators.
- Issue date: 2020 Jul 7
- White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks.
- Authors: Guerrero R, Qin C, Oktay O, Bowles C, Chen L, Joules R, Wolz R, Valdés-Hernández MC, Dickie DA, Wardlaw J, Rueckert D
- Issue date: 2018
- Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging.
- Authors: Igwe KC, Lao PJ, Vorburger RS, Banerjee A, Rivera A, Chesebro A, Laing K, Manly JJ, Brickman AM
- Issue date: 2021 Oct 15
- White matter hyperintensity reduction and outcomes after minor stroke.
- Authors: Wardlaw JM, Chappell FM, Valdés Hernández MDC, Makin SDJ, Staals J, Shuler K, Thrippleton MJ, Armitage PA, Muñoz-Maniega S, Heye AK, Sakka E, Dennis MS
- Issue date: 2017 Sep 5