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Neuroradiology. 2017 Oct;59(10):951-962. doi: 10.1007/s00234-017-1892-1. Epub 2017 Aug 16.

Metric to quantify white matter damage on brain magnetic resonance images.

Author information

1
Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK. mvhernan@staffmail.ed.ac.uk.
2
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK. mvhernan@staffmail.ed.ac.uk.
3
Edinburgh Dementia Research Centre, UK Dementia Research Institute, London, UK. mvhernan@staffmail.ed.ac.uk.
4
Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
5
Edinburgh Dementia Research Centre, UK Dementia Research Institute, London, UK.
6
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
7
Department of Cardiovascular Sciences, University of Sheffield, Sheffield, UK.
8
Department of Psychology, University of Edinburgh, Edinburgh, UK.

Abstract

PURPOSE:

Quantitative assessment of white matter hyperintensities (WMH) on structural Magnetic Resonance Imaging (MRI) is challenging. It is important to harmonise results from different software tools considering not only the volume but also the signal intensity. Here we propose and evaluate a metric of white matter (WM) damage that addresses this need.

METHODS:

We obtained WMH and normal-appearing white matter (NAWM) volumes from brain structural MRI from community dwelling older individuals and stroke patients enrolled in three different studies, using two automatic methods followed by manual editing by two to four observers blind to each other. We calculated the average intensity values on brain structural fluid-attenuation inversion recovery (FLAIR) MRI for the NAWM and WMH. The white matter damage metric is calculated as the proportion of WMH in brain tissue weighted by the relative image contrast of the WMH-to-NAWM. The new metric was evaluated using tissue microstructure parameters and visual ratings of small vessel disease burden and WMH: Fazekas score for WMH burden and Prins scale for WMH change.

RESULTS:

The correlation between the WM damage metric and the visual rating scores (Spearman ρ > =0.74, p < 0.0001) was slightly stronger than between the latter and WMH volumes (Spearman ρ > =0.72, p < 0.0001). The repeatability of the WM damage metric was better than WM volume (average median difference between measurements 3.26% (IQR 2.76%) and 5.88% (IQR 5.32%) respectively). The follow-up WM damage was highly related to total Prins score even when adjusted for baseline WM damage (ANCOVA, p < 0.0001), which was not always the case for WMH volume, as total Prins was highly associated with the change in the intense WMH volume (p = 0.0079, increase of 4.42 ml per unit change in total Prins, 95%CI [1.17 7.67]), but not with the change in less-intense, subtle WMH, which determined the volumetric change.

CONCLUSION:

The new metric is practical and simple to calculate. It is robust to variations in image processing methods and scanning protocols, and sensitive to subtle and severe white matter damage.

KEYWORDS:

Brain; Cerebrovascular disorders; Leukoencephalopathies; MRI; Neuroimaging; White matter hyperintensities

PMID:
28815362
PMCID:
PMC5596039
DOI:
10.1007/s00234-017-1892-1
[Indexed for MEDLINE]
Free PMC Article

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