title: Automated measurement of local white matter lesion volume.
abstract: It has been hypothesized that white matter lesions at different locations may have different etiology and clinical consequences. Several approaches for the quantification of local white matter lesion load have been proposed in the literature, most of which rely on a distinction between lesions in a periventricular region close to the ventricles and a subcortical zone further away. In this work we present a novel automated method for local white matter lesion volume quantification in magnetic resonance images. The method segments and measures the white matter lesion volume in 43 regions defined by orientation and distance to the ventricles, which allows a more spatially detailed study of lesion load. The potential of the method was demonstrated by analyzing the effect of blood pressure on the regional white matter lesion volume in 490 elderly subjects taken from a longitudinal population study. The method was also compared to two commonly used techniques to assess the periventricular and subcortical lesion load. The main finding was that high blood pressure was primarily associated with lesion load in the vascular watershed area that forms the border between the periventricular and subcortical regions. It explains the associations found for both the periventricular and subcortical load computed for the same data, and that were reported in the literature. But the proposed method can localize the region of association with greater precision than techniques that distinguish between periventricular and subcortical lesions only.
authors: Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands. f.vanderlijn@erasmusmc.nl
authors: Fedde
authors: F
authors: van der Lijn
authors: Benjamin F J
authors: BF
authors: Verhaaren
authors: M Arfan
authors: MA
authors: Ikram
authors: Stefan
authors: S
authors: Klein
authors: Marleen
authors: M
authors: de Bruijne
authors: Henri A
authors: HA
authors: Vrooman
authors: Meike W
authors: MW
authors: Vernooij
authors: Alexander
authors: A
authors: Hammers
authors: Daniel
authors: D
authors: Rueckert
authors: Aad
authors: A
authors: van der Lugt
authors: Monique M B
authors: MM
authors: Breteler
authors: Wiro J
authors: WJ
authors: Niessen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001331:Automation; D001927:Brain Diseases / Q000473:pathology*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged