Textural Characterisation on Regions of Interest: A Useful Tool for the Study of Small Vessel Disease

Proponemos un marco para investigar las propiedades de los tejidos aparentemente normales en las imágenes de resonancia magnética de la estructura cerebral de pacientes con enfermedad de vasos pequeños (SVD). Implica la extracción de entidades texturales en regiones de interés (ROI) obtenidas a partir de una plantilla anatómicamente relevante, combinada con un análisis estadístico que considere la distribución relativa de marcadores SVD (por ejemplo, microsangrados, espacios perivasculares e hiperintensidades de materia blanca) con respecto a las características texturales de las regiones de interés, en los territorios arteriales derivados de otra plantilla. Aplicamos este enfoque a los datos de 42 pacientes de un estudio de accidente cerebrovascular leve para investigar si los tejidos normales en diferentes regiones cerebrales son homogéneos independientemente de la presencia de marcadores y variedades de SVD específicos en las manifestaciones de la patología (accidente cerebrovascular en diferentes territorios arteriales). Nuestros resultados sugieren que este no es el caso: que los tejidos normales son heterogéneos y que las variaciones locales (representadas por la entropía) están asociadas con marcadores SVD, de acuerdo con los informes clínicos

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

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