Rationale and objectives: Diffusion tensor imaging (DTI) has been used extensively in determining morphology and connectivity of the brain; however, similar analysis in the spinal cord has proven difficult. The objective of this study was to improve the delineation of gray and white matter in the spinal cord by applying signal processing techniques to the eigenvalues of the diffusion tensor. Our approach involved creating anisotropy indices based on the difference between eigenvalues and mean diffusivity then using a fuzzy inference system (FIS) to delineate between gray and white matter in the human cervical spinal cord.
Materials and methods: DTI was performed on the cervical spinal cord in five neurologically intact subjects. Distributions were extracted for regions of gray and white matter through the use of a digitized histologic template. Fuzzy membership functions were created based on these distributions. Detectability index and receiver operating characteristic (ROC) analysis was performed on traditional DTI indices and FIS classified regions.
Results: A significantly higher contrast between gray and white matter was observed using fuzzy classification compared with traditionally used DTI indices based on the detectability index (P < .001) and trends in the ROC analysis. Reconstructed images from the FIS qualitatively showed a better anatomical representation of the spinal cord compared with traditionally used DTI indices.
Conclusions: Diffusion tensor imaging using an FIS for tissue classification provides high contrast between spinal gray and white matter compared with traditional DTI indices and may provide a noninvasive technique to quantify the integrity and morphology of the human spinal cord following injury.