title: Subcortical gray matter segmentation and voxel-based analysis using transverse relaxation and quantitative susceptibility mapping with application to multiple sclerosis.
abstract: OBJECTIVE
  To investigate subcortical gray matter segmentation using transverse relaxation rate (R2 *) and quantitative susceptibility mapping (QSM) and apply it to voxel-based analysis in multiple sclerosis (MS).
  
  
  METHODS
  Voxel-based variation in R2 * and QSM within deep gray matter was examined and compared to standard whole-structure analysis using 37 MS subjects and 37 matched controls. Deep gray matter nuclei (caudate, putamen, globus pallidus, and thalamus) were automatically segmented and morphed onto a custom atlas based on QSM and standard T1 -weighted images. Segmentation accuracy and scan-rescan reliability were tested.
  
  
  RESULTS
  When considering only significant regions as returned by the multivariate voxel-based analysis, increased R2 * and QSM was found in MS subjects compared to controls in portions of all four nuclei studied (P < 0.002). For R2 *, regional analysis yielded at least 66-fold improved P-value significance in all nuclei over standard whole-structure analysis, while for QSM only thalamus benefited, with 5-fold improvement in significance. Improved segmentation over standard methods, particularly for globus pallidus (2.8 times higher Dice score), was achieved by incorporating high-contrast QSM into the atlas. Voxel-based reliability was highest for QSM (<1% variation).
  
  
  CONCLUSIONS
  Automatic segmentation of iron-rich deep gray matter can be improved by incorporating QSM. Voxel-based evaluation yielded increased R2 * and QSM in MS subjects in all four nuclei studied with R2 *, benefiting the most from localized analysis over whole-structure measures.
authors.affiliation: Biomedical Engineering, University of Alberta, Edmonton, Canada.|Computing Science, University of Alberta, Edmonton, Canada.
authors.forename: Dana
authors.initials: D
authors.lastname: Cobzas
authors.affiliation: Biomedical Engineering, University of Alberta, Edmonton, Canada.
authors.forename: Hongfu
authors.initials: H
authors.lastname: Sun
authors.affiliation: Biomedical Engineering, University of Alberta, Edmonton, Canada.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Walsh
authors.affiliation: Biomedical Engineering, University of Alberta, Edmonton, Canada.
authors.forename: R Marc
authors.initials: RM
authors.lastname: Lebel
authors.affiliation: Division of Neurology, University of Alberta, Edmonton, Canada.
authors.forename: Gregg
authors.initials: G
authors.lastname: Blevins
authors.affiliation: Biomedical Engineering, University of Alberta, Edmonton, Canada.
authors.forename: Alan H
authors.initials: AH
authors.lastname: Wilman
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D005260:Female; D066128:Gray Matter / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D055815:Young Adult
keywords: R2*; brain iron; multiple sclerosis; quantitative susceptibility mapping; segmentation; subcortical gray matter