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.
Highlights
Previous studies have demonstrated that white matter (WM) lesions bias automated brain tissue classifications and cerebral volume measurements
On T1- weighted (T1w) images (MR sequences used in typical clinical neuroscientific research settings), WM lesions are characterized by Magnetic resonance (MR) signal intensities close to gray matter (GM) and cerebrospinal fluid (CSF) introducing a possible bias on tissue classification
The lesions correctly classified as WM, instead, were characterized by a mean intensity that was only 1.05 ± 1.28% lower than Normal-appearing white matter (NAWM)
Summary
Previous studies have demonstrated that white matter (WM) lesions bias automated brain tissue classifications and cerebral volume measurements. The quality of MR data (e.g. intensity inhomogeneity or partial volume averaging due to low resolution), differences of mathematical algorithms and brain tissue alterations due to pathologies may contribute to reduce the accuracy of brain tissue classification [1,2,3,4] In this regard, the influence of the white matter (WM) lesions as observed in multiple sclerosis (MS) patients has been previously investigated. Sdika and Pelletier [7] argued that, the segmentation, and the image registration step could be affected by WM lesions For this reason, they tested three different lesion filling methods: 1) they filled the lesions from their border to their center with an average of neighbouring voxels; 2) using only the value of the surrounding NAWM; 3) and using the mean intensity of the NAWM over the whole brain. The latter approach significantly improved the accuracy of the tissue classification and brain volume measurements computed by SIENAX [8]
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