Abstract
Perivascular spaces are fluid-filled tubular spaces that follow the course of cerebral penetrating vessels, thought to be a key part in the brain’s circulation and glymphatic drainage system. Their enlargement and abundance have been found associated with cerebral small vessel disease. Thus, their quantification is essential for establishing their relationship with neurological diseases. Previous works in the field have designed visual rating scales for assessing the presence of perivascular spaces and proposed segmentation techniques to reduce flooring and ceiling effects of qualitative visual scales, processing times, and inter-observer variability. Nonetheless, their application depends on the acquisition quality. In this paper, we propose a framework for improving perivascular spaces quantification using both texture analysis and total variation filtering. Texture features were considered for evaluating the image quality and determining automatically whether filtering was needed. We tested our work using data from a cohort of patients with mild stroke (\(n=60\)) with different extents of small vessel disease features and image quality. Our results demonstrate the potential of our proposal for improving perivascular spaces assessments.
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Acknowledgements
This work is supported by: the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK MRC, Alzheimer’s Society and Alzheimer’s Research UK; the Fondation Leducq Network for the Study of Perivascular Spaces in Small Vessel Disease (16 CVD 05); Stroke Association ‘Small Vessel Disease-Spotlight on Symptoms (SVD-SOS)’ (SAPG 19\(\backslash \)100068); The Row Fogo Charitable Trust Centre for Research into Aging and the Brain (MVH) (BRO-D.FID3668413); Stroke Association Garfield Weston Foundation Senior Clinical Lectureship (FND) (TSALECT 2015/04); NHS Research Scotland (FND); British Heart Foundation Edinburgh Centre for Research Excellence (RE/18/5/34216); a British Heart Foundation Chair award (RMT) (CH/12/4/29762); NHS Lothian Research and Development Office (MJT); European Union Horizon 2020, PHC-03-15, project No666881, ‘SVDs@Target’ (MS); Chief Scientist Office of Scotland Clinical Academic Fellowship (UC) (CAF/18/08); Stroke Association Princess Margaret Research Development Fellowship (UC) (2018); MRC Doctoral Training Programme in Precision Medicine (JB); Alzheimer Nederland (ACCJ). The Research MR scanners are supported by the Scottish Funding Council through the Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration; the 3 T scanner is funded by the Wellcome Trust (104916/Z/14/Z), Dunhill Trust (R380R/1114), Edinburgh and Lothians Health Foundation (2012/17), Muir Maxwell Research Fund, and the University of Edinburgh. We thank the participants, their families, radiographers at Edinburgh Imaging Facility Royal Infirmary of Edinburgh, and the Stroke Research Network at the University of Edinburgh.
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Bernal, J. et al. (2020). A Framework for Jointly Assessing and Reducing Imaging Artefacts Automatically Using Texture Analysis and Total Variation Optimisation for Improving Perivascular Spaces Quantification in Brain Magnetic Resonance Imaging. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_14
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