Recently, a T2*-weighted template and probabilistic atlas of the white and gray matter (WM, GM) of the spinal cord (SC) have been reported. Such template can be used as tissue-priors for automated WM/GM segmentation but can also provide a common reference and normalized space for group studies. Here, a new template has been created (AMU40), and accuracy of automatic template-based WM/GM segmentation was quantified. The feasibility of tensor-based morphometry (TBM) for studying voxel-wise morphological differences of SC between young and elderly healthy volunteers was also investigated. Sixty-five healthy subjects were divided into young (n=40, age<40years old, mean age 28±5years old) and elderly (n=25, age>50years old, mean age 57±5years old) groups and scanned at 3T using an axial high-resolution T2*-weighted sequence. Inhomogeneity correction and affine intensity normalization of the SC and cerebrospinal fluid (CSF) signal intensities across slices were performed prior to both construction of the AMU40 template and WM/GM template-based segmentation. The segmentation was achieved using non-linear spatial normalization of T2*-w MR images to the AMU40 template. Validation of WM/GM segmentations was performed with a leave-one-out procedure by calculating DICE similarity coefficients between manual and automated WM/GM masks. SC morphological differences between young and elderly healthy volunteers were assessed using the same non-linear spatial normalization of the subjects' MRI to a common template, derivation of the Jacobian determinant maps from the warping fields, and a TBM analysis. Results demonstrated robust WM/GM automated segmentation, with mean DICE values greater than 0.8. Concerning the TBM analysis, an anterior GM atrophy was highlighted in elderly volunteers, demonstrating thereby, for the first time, the feasibility of studying local structural alterations in the SC using tensor-based morphometry. This holds great promise for studies of morphological impairment occurring in several central nervous system pathologies.
Keywords: Atrophy; Gray matter; MRI; Segmentation; Spinal cord; Template; Tensor-based morphometry.
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