title: White matter lesion filling improves the accuracy of cortical thickness measurements in multiple sclerosis patients: a longitudinal study.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland. stefano.magon@usb.ch.
authors.forename: Stefano
authors.initials: S
authors.lastname: Magon
authors.forename: Laura
authors.initials: L
authors.lastname: Gaetano
authors.forename: M Mallar
authors.initials: MM
authors.lastname: Chakravarty
authors.forename: Jason P
authors.initials: JP
authors.lastname: Lerch
authors.forename: Yvonne
authors.initials: Y
authors.lastname: Naegelin
authors.forename: Christoph
authors.initials: C
authors.lastname: Stippich
authors.forename: Ludwig
authors.initials: L
authors.lastname: Kappos
authors.forename: Ernst-Wilhelm
authors.initials: EW
authors.lastname: Radue
authors.forename: Till
authors.initials: T
authors.lastname: Sprenger
mesh_terms: D000328:Adult; D002540:Cerebral Cortex / Q000473:pathology*; D003430:Cross-Sectional Studies; D005260:Female; D005500:Follow-Up Studies; D066128:Gray Matter / Q000473:pathology; D006801:Humans; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000473:pathology*; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods*; D012189:Retrospective Studies; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D066127:White Matter / Q000473:pathology*; D055815:Young Adult