Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias.

Academic Article


  • OBJECTIVE: Focal cortical dysplasias (FCDs) often cause pharmacoresistant epilepsy, and surgical resection can lead to seizure-freedom. Magnetic resonance imaging (MRI) and positron emission tomography (PET) play complementary roles in FCD identification/localization; nevertheless, many FCDs are small or subtle, and difficult to find on routine radiological inspection. We aimed to automatically detect subtle or visually-unidentifiable FCDs by building a classifier based on an optimized cortical surface sampling of combined MRI and PET features. METHODS: Cortical surfaces of 28 patients with histopathologically-proven FCDs were extracted. Morphology and intensity-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface, and fed to a 2-step (Support Vector Machine and patch-based) classifier. Classifier performance was assessed compared to manual lesion labels. RESULTS: Our classifier using combined feature selections from MRI and PET outperformed both quantitative MRI and multimodal visual analysis in FCD detection (93% vs 82% vs 68%). No false positives were identified in the controls, whereas 3.4% of the vertices outside FCD lesions were also classified to be lesional ("extralesional clusters"). Patients with type I or IIa FCDs displayed a higher prevalence of extralesional clusters at an intermediate distance to the FCD lesions compared to type IIb FCDs (p < 0.05). The former had a correspondingly lower chance of positive surgical outcome (71% vs 91%). CONCLUSIONS: Machine learning with multimodal feature sampling can improve FCD detection. The spread of extralesional clusters characterize different FCD subtypes, and may represent structurally or functionally abnormal tissue on a microscopic scale, with implications for surgical outcomes.
  • Published In

  • NeuroImage  Journal
  • Keywords

  • FCD detection, FDG-PET, Focal cortical dysplasia, MRI, Patch analysis, Surface-based feature modeling, Adolescent, Adult, Child, Child, Preschool, Epilepsy, Temporal Lobe, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Malformations of Cortical Development, Middle Aged, Multimodal Imaging, Positron-Emission Tomography, Support Vector Machine, Young Adult
  • Digital Object Identifier (doi)

    Author List

  • Tan Y-L; Kim H; Lee S; Tihan T; Ver Hoef L; Mueller SG; Barkovich AJ; Xu D; Knowlton R
  • Start Page

  • 10
  • End Page

  • 18
  • Volume

  • 166