Voxel-based 3D-MRI-analysis for the detection of focal cortical dysplasia

Focal cortical dysplasia (FCD), i.e., neuronal derangement due to developmental malformation, is increasingly recognized as an underlying cause of formerly cryptogenic focal epilepsy. However, in subtle cases, its diagnosis by visual evaluation of magnetic resonance images (MRI) remains difficult. Here, we present three novel techniques for postprocessing of 3-dimensional (3D) MRI which may improve lesion detection by enhancing image properties not readily accessible by visual analysis. Following the principles of voxel-based morphometry a T₁-weighted MRI volume data set (MPRAGE) is normalized and segmented using algorithms of SPM99 (Statistical Parametric Mapping, Wellcome Department of Imaging Neuroscience, London). Then, the distribution of gray and white matter is analyzed on a voxel-wise basis and compared with a normal database consisting of the MR images of 53 healthy subjects. Based on this analysis, 3-dimensional maps called 'thickness image', 'extension image', and 'junction image', are created which characterize three different features of FCD, i.e., abnormal thickness of the cortical ribbon, abnormal extension of gray matter into the white matter, and blurring of the gray-white matter junction. These methods were applied to the MRI data of 25 epilepsy patients with histologically proven FCD. In each of the new feature maps the locations of the five highest maxima (corresponding to the maximum deviations from the mean of the normal database) were automatically determined and compared with the sites of the lesions in the conventional MR images or-in case of cryptogenic epilepsy-with the resection areas in the post-operative MRI. This approach was able to detect 15/25 lesions in the thickness image and 18/25 lesions in the junction and extension image, respectively. With all feature maps combined, 23 out of 25 dysplastic lesions were detected. Among these cases there were also four patients in whom the dysplastic lesion itself or at least an essential part of it had not been recognized on conventional MR images despite acquisition and assessment in a tertiary epilepsy center. The novel techniques for automated post-processing of MRI presented here facilitate the detection and localization of FCD and increase the sensitivity of MR imaging. Thereby, they provide a valuable additional diagnostic tool in the presurgical evaluation of epilepsy patients and improve the therapeutic options especially in cases of cryptogenic epilepsy.