Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference

Abstract

White matter hyperintensities (WMH), also known as white matter lesions, are localised white matter areas that appear hyperintense on MRI scans. WMH commonly occur in the ageing population, and are often associated with several factors such as cognitive disorders, cardiovascular risk factors, cerebrovascular and neurodegenerative diseases. Despite the fact that some links between lesion location and parametric factors such as age have already been established, the relationship between voxel-wise spatial distribution of lesions and these factors is not yet well understood. Hence, it would be of clinical importance to model the distribution of lesions at the population-level and quantitatively analyse the effect of various factors on the lesion distribution model.In this work we compare various methods, including our proposed method, to generate voxel-wise distributions of WMH within a population with respect to various factors. Our proposed Bayesian spline method models the spatio-temporal distribution of WMH with respect to a parametric factor of interest, in this case age, within a population. Our probabilistic model takes as input the lesion segmentation binary maps of subjects belonging to various age groups and provides a population-level parametric lesion probability map as output. We used a spline representation to ensure a degree of smoothness in space and the dimension associated with the parameter, and formulated our model using a Bayesian framework.We tested our algorithm output on simulated data and compared our results with those obtained using various existing methods with different levels of algorithmic and computational complexity. We then compared the better performing methods on a real dataset, consisting of 1000 subjects of the UK Biobank, divided in two groups based on hypertension diagnosis. Finally, we applied our method on a clinical dataset of patients with vascular disease.

Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholding

“…We modelled the PPLPM with respect to age within a population consisting of 474 subjects, as described in our previous work (Sundaresan et al, 2018, Preprint, Figure 1: Parametric lesion probability maps at two example points in the parametric dimension, corresponding to two age groups (all the images are shown at z = 45 in MNI space, (a); in the younger age group (29 -31 years, b) the lesion probability is very low throughout the brain, while in the older age group (59 -61 years, c) the lesion probability is higher, especially in the periventricular regions.…”