ArticleBMC neuroscience2014
White matter lesion filling improves the accuracy of cortical thickness measurements in multiple sclerosis patients: a longitudinal study.
Article in BMC neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 46 citations in OpenAlex.
- Effect of in-painting on cortical thickness measurements in multiple sclerosis: A large cohort study.Human brain mapping · 2015Trial
- Nomograms integrating cortical morphometric metrics with 3D multi-parametric MRI radiomics for predicting disability progression and cognitive worsening in relapsing-remitting multiple sclerosis: a multi-center validation study.Journal of neurology · 2025Article
- The effect of lesion filling on brain age estimation in multiple sclerosis.BMC medical imaging · 2025Article
- Intrathecal Inflammatory Profile and Gray Matter Damage Predict Progression Independent of Relapse Activity in Early Multiple Sclerosis.Neurology(R) neuroimmunology & neuroinflammation · 2025Article
- FastSurfer-LIT: Lesion inpainting tool for whole-brain MRI segmentation with tumors, cavities, and abnormalities.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Early regional cerebral grey matter damage predicts long-term cognitive impairment phenotypes in multiple sclerosis: a 20-year study.Brain communications · 2024Article
- Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome.Frontiers in robotics and AI · 2022Article
- Longitudinal changes of deep gray matter shape in multiple sclerosis.NeuroImage. Clinical · 2022Article
- Central nervous system atrophy predicts future dynamics of disability progression in a real-world multiple sclerosis cohort.European journal of neurology · 2021Article
- Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance.Human brain mapping · 2021Article
- Gray Matter Morphometry Correlates with Attentional Efficiency in Young-Adult Multiple Sclerosis.Brain sciences · 2021Article
- Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort.Journal of neurology · 2020Article
- Robust Multiple Sclerosis Lesion Inpainting with Edge Prior.Machine learning in medical imaging. MLMI (Workshop) · 2020Article
- Longitudinal patterns of cortical thinning in multiple sclerosis.Human brain mapping · 2020Article
- Volume loss in the deep gray matter and thalamic subnuclei: a longitudinal study on disability progression in multiple sclerosis.Journal of neurology · 2020Article
- Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions.Frontiers in neuroscience · 2020Article
- Damage of the lateral geniculate nucleus in MS: Assessing the missing node of the visual pathway.Neurology · 2019Article
- Leptomeningeal Contrast Enhancement Is Related to Focal Cortical Thinning in Relapsing-Remitting Multiple Sclerosis: A Cross-Sectional MRI Study.AJNR. American journal of neuroradiology · 2019Article
- Brief Computer-Based Information Processing Measures are Linked to White Matter Integrity in Pediatric-Onset Multiple Sclerosis.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2019Article
- Relationship between episodic memory and volume of the brain regions of two functional cortical memory systems in multiple sclerosis.Journal of neurology · 2018Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrevious studies have demonstrated that white matter (WM) lesions bias automated brain tissue classifications and cerebral volume measurements. However, filling WM lesions using the intensity of neighbouring normal-appearing WM has been shown to increase the accuracy of automated volume measurements in the brain. In the present study, we investigate the influence of WM lesions on cortical thickness (CTh) measures and assessed the impact of lesion filling on both cross-sectional/longitudinal and global/regional measurements of CTh in multiple sclerosis (MS) patients.
methodsFifty MS patients were studied at baseline as well as after three and six years of follow-up. CTh was estimated using a fully automated pipeline (CIVET) on T1-weighted magnetic resonance images data acquired at 1.5 Tesla without (original) and with WM lesion filling (filled). WM lesions were semi-automatically segmented and then filled with the mean intensity of the neighbouring voxels. For both original and filled T1 images we investigated and compared the main CIVET's steps: tissue classification, surfaces generation and CTh measurement.
resultsOn the original T1 images, the majority of WM lesion volume (72%) was wrongly classified as gray matter (GM). After lesion filling the accuracy of WM lesions classification improved significantly (p < 0.001, 94% of WM lesion volume correctly classified) as well as the WM surface generation (p < 0.0001). The mean CTh computed on the original T1 images, overall time points, was significantly thinner (p < 0.001) compared the CTh estimated on the filled T1 images. The vertex-wise longitudinal analysis performed on the filled T1 images showed an increased number of vertices in the fronto-temporal region with a significantly decrease of CTh over time compared the analysis performed on the original images.
conclusionThese results indicate that WM lesions bias the CTh estimation both cross-sectionally as well as longitudinally. The lesion filling approach significantly improved the accuracy of the regional CTh estimation and has an impact also on the global estimation of CTh.
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