Surface-Wise Texture Patch Analysis of Combined MRI and PET to Detect MRI-Negative Focal Cortical Dysplasia

Focal cortical dysplasia (FCD) is intrinsically epileptogenic, and is a well-recognized cause of refractory epilepsy. Surgical resection can lead to seizure-freedom, and quantitative analyses of MRI have been an excellent presurgical evaluation tool. Yet many of the FCDs are small or “MRI-negative” at the visual evaluation, for which positron emission tomography (PET) may help identify an MRI-negative FCD. We proposed a pipeline that optimized cortical surface sampling of combined MRI and PET features and detection of subtle or visually negative FCDs. To this end, we extracted individual cortical surfaces and designed an adapted between-image modalities registration that conveyed the surface mesh into each image’s native space. To further improve detection accuracy, we integrated a framework of the patch library construction and label fusion that have been widely used for the brain structural segmentation into a surface-based classification. Our study demonstrated superior sensitivity in FCD detection using combined feature sampling of both MRI and PET, compared to MRI alone (93 vs 86%) while identifying no false positives (FP) in controls. Our classifier using the new patch analysis further outperformed a recently developed surface-based approach in terms of sensitivity (93 vs 64%), and FP rate (0 vs 0.1%). No FP vertices were found in controls: our classifier yet identified extralesional clusters in FCD patients. Patients with a higher prevalence of extralesional vertices had a lower chance of positive surgical outcome compared to those with a lower prevalence (67% vs 93%; p < 0.05).

Our study showed that the advance in techniques for multimodal feature sampling is the key to improve lesion detection and understand the pattern of brain abnormalities in FCD.