Current concepts of analysis of cerebral white matter hyperintensities on magnetic resonance imaging

Abstract

Cerebrovascular disease is common and associated with cognitive deficits and increased risk for dementia. Until recently, only limited attention has focused on advances in imaging techniques to better define and quantify the spectrum of asymptomatic cerebrovascular disease commonly seen on magnetic resonance imaging, such as abnormal white matter signals. Abnormal signals in cerebral white matter, although nonspecific, are increased in prevalence and severity in association with aging and cerebrovascular risk factors among older individuals. The ubiquitous occurrence of these abnormal white matter signals commonly referred to as white matter hyperintensities (WMHs) and the association with cerebrovascular risk and cognitive impairment among older individuals make scientific evaluation of WMHs an important and much needed avenue of research. In this section, we review current methods of WMH analysis. Strengths and limitation of both quantitative and qualitative methods are discussed initially, followed by a brief review of current magnetic resonance imaging segmentation and mapping techniques that make it possible to assess the anatomical location of WMHs. We conclude by discussing future analytic methods designed to better understand the pathophysiology and cognitive consequences of WMHs.

FIGURE 1

Flowchart for mapping the subject’s WMH onto the template image. In this figure, the subject FLAIR image (lower right) is segmented to produce a WMH mask (lower middle). The FLAIR image is linearly aligned and resliced to the subject’s T1 image (upper middle), and the T1 image is nonlinearly warped using the MDT as a template (upper left). The alignment and warp parameters are finally used to transform the WMH mask into the space of the template (lower left). Adapted from DeCarli.

FIGURE 2

Left, T1 image showing WMH areas with reduced voxel intensities. Right, areas of voxel intensity replacement following the locations of the WMH voxels in the WMH segmented map.

FIGURE 3

The role of the MDT template in white matter mapping. Left, Representative slice from MDT template. The parameters computed from normalizing a subject to the MDT are then used to inverse-warp each subject’s T1 image onto the MDT. In addition, the subject’s accompanying coregistered FLAIR image and WMH mask are also transformed onto the MDT. Middle, A sample image of 1 subject’s WMH mask after transforming and overlaying on the MDT template. Right, The composite WMH frequency map for population of 88 subjects.

FIGURE 4

Minimal deformation template and MNI. Composite frequency map for population of 88 subjects, made to reach the threshold at 20% and overlaid as a color-coded map onto template images. Left, MDT template. Right, MNI template. Coordinate transformation between the MDT and MNI images was obtained by high-dimensional B-spline warp between MNI and MDT.

FIGURE 5

Color-coded images of group WMH frequency maps overlaid on MDT. Each frequency map in MDT has been made to reach the threshold at 30%. Left, Subjects with dementia. Middle, Subjects with MCI. Right, Cognitively healthy subjects. The variation, by group, of the location and extent of high frequency (above 30%) areas is readily visible from these images.

FIGURE 6

Upper row, 3-dimensional cutaway views to show the relative location of the 10% thresholded WMH frequency map (orange) with reference to the ventricles (gray) of the MDT. Second row, similar views showing the ROI demarcation on the 3-dimensional WMH frequency maps. Areas chosen as ROIs are in red, and the frequency map is light gray.