Summary: Purpose: Focal cortical dysplasia (FCD) is a frequent cause of partial epilepsy. Its diagnosis by visual evaluation of magnetic resonance images (MRIs) remains difficult. The purpose of this study was to apply a novel automated and observer-independent voxel-based technique for the analysis of 3-dimensional (3-D) MRI to detect and localize FCD.
Methods: The technique was based on algorithms of the SPM99 software and included the spatial normalization of 3-D MRI data sets to a common stereotaxic space and the segmentation of cortical grey matter. The resulting data sets represented grey-matter density maps where each voxel encoded the grey-matter concentration at the corresponding position in the original MRI. A normal database was set up by calculating and averaging the grey-matter density maps of 30 healthy volunteers. The MRI data sets of seven epilepsy patients with FCD were evaluated retrospectively for dysplastic lesions by voxelwise subtraction of the mean grey-matter density map of the normal database and searching automatically for local and global maxima in the resulting data set.
Results: In all patients, the results of voxel-based 3-D MRI analysis corresponded both to the location of the dysplastic lesions in conventional MRI and to seizure semiology and EEG findings. In one case, surgery was performed, and the diagnosis FCD was supported by histology.
Conclusions: The technique of voxel-based 3-D MRI analysis and comparison with a normal database seems to provide a valuable additional screening tool for the detection of FCD.