Texture Analysis of T1 weighted and fluid attenuated inversion recovery images detects abnormalities which correlate with disease progression in small vessel disease

23 Aug 2018

Background and Purpose MRI techniques may be useful to assess disease severity in cerebral small vessel disease (SVD), identify those individuals who are most likely to progress to dementia, monitor disease progression and act as surrogate markers to test new therapies. Texture analysis extracts information on the relationship between signal intensities of neighbouring voxels. A potential advantage over some techniques used to characterise SVD, such as diffusion tensor imaging, is that it can be used on routine clinically obtained MR sequences. We determined whether texture parameters (TP) were abnormal in SVD, correlated with cognitive impairment, or predict cognitive decline. Methods In the prospective SCANS study we assessed TP in 121 individuals with symptomatic SVD at baseline, 99 of whom attended for repeat MRI and cognitive testing performed yearly for 3 years after which cognitive testing alone was performed for a further 2 years. Conversion to dementia was recorded for all subjects over the 5 years period. Texture analysis was performed on FLAIR and T1-weighted images. The SVD cohort was compared with 54 age matched controls scanned on the same system. Results There were highly significant differences in a number of TP between SVD cases and controls. Within the SVD population TP were highly correlated to other MRI parameters (brain volume, white matter lesion volume, lacune count). TP predicted executive function and global function at baseline and predicted conversion to dementia, after controlling for age, gender, pre-morbid IQ and other MR parameters. Conclusions TP, which can be obtained from sequences used in routine clinical imaging, are abnormal in SVD and the degree of abnormality correlates with executive dysfunction and global cognition at baseline and decline over five years. TP may be useful to assess disease severity in clinically collected data. This needs testing in data clinically acquired across multiple sites.