Automated morphometric magnetic resonance imaging analysis for the detection of periventricular nodular heterotopia

Purpose: To describe a novel magnetic resonance imaging (MRI) postprocessing technique for the detection of periventricular nodular heterotopia (PNH) and to evaluate its diagnostic value. The method is a further development of voxel‐based morphometric analysis with focus on a region of interest around the lateral ventricles to increase the sensitivity and specificity for automated detection of abnormally located gray matter in this area. Methods: T₁‐weighted MRI volume data sets were normalized and segmented in statistical parametric mapping (SPM 5 software), and the distribution of gray matter was compared to a normal database. As a new approach, individual masks derived from segmentation of the lateral ventricles were used to restrict the search for ectopic gray matter to the periventricular area. PNH were automatically detected by localizing the maximum deviation from the normal database in this area, provided that the z‐score exceeded a certain threshold. The optimal z‐score threshold for maximum sensitivity and specificity was determined by a receiver operating characteristic (ROC) curve analysis. The method was applied in 40 patients with PNH and 400 controls. Key Findings: PNH were detected in 37 of 40 patients, and false positives were found in 34 of 400 controls, amounting to 92.5% sensitivity and 91.5% specificity. In 17 of the patients in whom PNH could be identified, these lesions had been overlooked in the past, and in 8 patients even in the high‐resolution MRI subsequently used for postprocessing. Significance: The results suggest that automated morphometric MRI analysis with focus on ectopic gray matter in the periventricular areas facilitates the evaluation of MRI data and increases the sensitivity for the detection of PNH.