Abstract
Cerebral small vessel disease (SVD) comprises all the pathological processes affecting small brain vessels and, consequently, damaging white and grey matter. Although the cause of SVD is unknown, there seems to be a dysfunction of the small vessels. In this paper, we propose a framework comprising tissue segmentation, spatial spectral feature extraction, and statistical analysis to study intravenous contrast agent distribution over time in cerebrospinal fluid, normal-appearing and abnormal brain regions in patients with recent mild stroke and SVD features. Our results show the potential of the power spectrum for the analysis of dynamic contrast-enhanced brain MRI acquisitions in SVD since significant variation in the data was related to vascular risk factors and visual clinical variables that characterise the burden of SVD features. Thus, our proposal may increase sensitivity to detect subtle features of small vessel dysfunction. A public version of our framework can be found at https://github.com/joseabernal/DynamicBrainMRIAnalysis.git.
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Acknowledgements
JB holds an MRC Precision Medicine Doctoral Training Programme studentship from the University of Edinburgh. This work was supported by the Row Fogo Charitable Trust (MVH) grant no. BRO-D.FID3668413, Wellcome Trust (patient recruitment, scanning, primary study Ref No. WT088134/Z/09/A), Fondation Leducq (Perivascular Spaces Transatlantic Network of Excellence), and EU Horizon 2020 (SVDs@Target) and the MRC UK Dementia Research Institute (Wardlaw programme). The authors thank participants in the study, the radiographers and staff at the Edinburgh Imaging Facilities (www.ed.ac.uk/clinical-sciences/edinburgh-imaging/research/facilities-and-equipment/edinburgh-imaging-facilities), the UK Dementia Research Institute at the University of Edinburgh, and the Row Fogo Centre for Ageing and the Brain.
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Bernal, J. et al. (2020). Analysis of Spatial Spectral Features of Dynamic Contrast-Enhanced Brain Magnetic Resonance Images for Studying Small Vessel Disease. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_24
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DOI: https://doi.org/10.1007/978-3-030-39343-4_24
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