Analysis of dynamic texture and spatial spectral descriptors of dynamic contrast-enhanced brain magnetic resonance images for studying small vessel disease

J Bernal* (Corresponding Author), MDC Valdés-Hernández* (Corresponding Author), J Escudero, L Viksne, AK Heye, PA Armitage, S Makin, RM Touyz, JM Wardlaw

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
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Cerebral small vessel disease (SVD) comprises various pathological processes affecting small brain vessels and damaging white and grey matter. In this paper, we propose a framework comprising region of interest sampling, dynamic spectral and texture description, functional principal component analysis, and statistical analysis to study exogenous contrast agent distribution over time in various brain regions in patients with recent mild stroke and SVD features.We compared our results against current semi-quantitative surrogates of dysfunction such as signal enhancement area and slope. Biological sex, stroke lesion type and overall burden of white matter hyperintensities (WMH) were significant predictors of intensity, spectral, and texture features extracted from the ventricular region (p-value < 0.05), explaining between a fifth and a fourth of the data variance (0.20 ≤Adj.R2 ≤ 0.25). We observed that spectral feature reflected more the dysfunction compared to other descriptors since the overall WMH burden explained consistently the power spectra variability in blood vessels, cerebrospinal fluid, deep grey matter and white matter. Our preliminary results show the potential of the framework for the analysis of dynamic contrast-enhanced brain magnetic resonance imaging acquisitions in SVD since significant variation in our metrics was related to the burden of SVD features. Therefore, our proposal may increase sensitivity to detect subtle features of small vessel dysfunction. A public version of the code will be released on our research website.
Original languageEnglish
Pages (from-to)240-247
Number of pages8
JournalMagnetic Resonance Imaging
Early online date13 Nov 2019
Publication statusPublished - Feb 2020

Bibliographical note

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 at the University of Edinburgh (Wardlaw programme). The authors thank participants in the study, the radiographers and staff at the Edinburgh Imaging Facilities, the UK Dementia Research Institute at the University of Edinburgh.


  • Dynamic descriptors
  • Principal Component Analysis
  • ynamic brain magnetic resonance image
  • cerebral small vessel disease
  • Dynamic brain magnetic resonance image
  • Cerebral small vessel disease
  • Principal component analysis
  • MRI


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