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J Magn Reson Imaging. 2013 Oct;38(4):774-85. doi: 10.1002/jmri.24047. Epub 2013 Feb 25.

Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images: a systematic review.

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Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom; Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), University of Edinburgh, Edinburgh, United Kingdom; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) collaboration, Scotland, United Kingdom.


Enlarged perivascular spaces (EPVS), visible in brain MRI, are an important marker of small vessel disease and neuroinflammation. We systematically evaluated the literature up to June 2012 on possible methods for their computational assessment and analyzed confounds with lacunes and small white matter hyperintensities. We found six studies that assessed/identified EPVS computationally by seven different methods, and four studies that described techniques to automatically segment similar structures and are potentially suitable for EPVS segmentation. T2-weighted MRI was the only sequence that identified all EPVS, but FLAIR and T1-weighted images were useful in their differentiation. Inconsistency within the literature regarding their diameter and terminology, and overlap in shape, intensity, location, and size with lacunes, conspires against their differentiation and the accuracy and reproducibility of any computational segmentation technique. The most promising approach will need to combine various MR sequences and consider all these features for accurate EPVS determination.


MRI; Virchow-Robin spaces; brain; computational assessment; perivascular spaces

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