ArticleNeuroImage2019
Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Probabilistic maps of white matter hyperintensity in a Chinese aging cohort and application for clinical diagnosis.European radiology · 2026Article
- Deep Learning Analysis of White Matter Hyperintensity and its Association with Comprehensive Vascular Factors in Two Large General Populations.Journal of imaging informatics in medicine · 2025Article
- Biomarkers of aging.Science China. Life sciences · 2023Review
- Article
- Correlation between 24-Hour Ambulatory Blood Pressure Variability and White Matter Lesions in Patients with Cerebral Small Vascular Disease: A Cross-Sectional Study.Computational and mathematical methods in medicine · 2022Article
- Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework.NeuroImage · 2021Article
- [A review on the application of UK Biobank in neuroimaging].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Review
- Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding.NeuroImage · 2019Article
- Mathematical modeling for the prediction of cerebral white matter lesions based on clinical examination data.PloS one · 2019Article
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Authors and funding
8 authors.
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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. On simulated dataset, the results from our algorithm showed a mean square error (MSE) value of 7.27×10
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