Automatic detection of subtle focal cortical dysplasia using surface-based features on MRI

Focal cortical dysplasia (FCD) is an important cause of pharmacoresistant epilepsy. Small FCD lesions are difficult to distinguish from non-lesional cortex and remain often overlooked on radiological MRI inspection. This paper presents a method to detect small FCD lesions on Tl-MRI relying on surface-based features: cortical thickness, gradient magnitude at the white-matter / grey-matter interface, cortical signal intensity, curvature and depth of inner-cortical surface. These features best describe the visual and morphometric characteristics of small FCD, and allow differentiating it from healthy tissues. The automatic detection was performed by a neural-network bagging trained on manual labels. The method was tested on 19 patients with small FCD and identified the lesion in 89% (17/19) of cases. Cluster analysis demonstrated that the lesional cluster was the largest in 76% (13/17) of identified cases. This new approach may assist the presurgical evaluation of patients with intractable epilepsy, especially those with "MRI-negative" epilepsy.