White matter lesion filling improves the accuracy of cortical thickness measurements in multiple sclerosis patients: a longitudinal study

BMC Neuroscience

Till Sprenger⁵  Ernst-Wilhelm Radue⁵  Ludwig Kappos¹  Christoph Stippich²  Yvonne Naegelin¹  Jason P Lerch⁴  M Mallar Chakravarty³  Laura Gaetano¹  Stefano Magon¹ 

[1] Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland;Department of Radiology and Nuclear Medicine, Division of Diagnostic and Interventional Neuroradiology, University Hospital Basel, Basel, Switzerland;Department of Psychiatry and the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada;Department of Medical Biophysics, University of Toronto, Toronto, Canada;Medical Image Analysis Center, University Hospital Basel, Basel, Switzerland

关键词: Longitudinal analysis;    Lesion filling;    Cortical thickness;    Multiple sclerosis;   

Others  :  1091111
DOI  :  10.1186/1471-2202-15-106

 received in 2014-04-25, accepted in 2014-08-28,  发布年份 2014

【 摘 要 】

Background

Previous 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.

Methods

Fifty 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.

Results

On 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.

Conclusion

These 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.

【 授权许可】

   
2014 Magon et al.; licensee BioMed Central Ltd.

【 图 表 】

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