Keywords: Epilepsy, MR Fingerprinting, Surgery, Focal cortical dysplasia
Motivation: Focal cortical dysplasia (FCD) is a common pathology in medically intractable focal epilepsy. Detecting and subtyping FCD through visual inspection of conventional MRI can be challenging.
Goal(s): We aimed to develop a multiparametric, quantitative approach for FCD characterization, based on MR fingerprinting (MRF).
Approach: High-resolution 3D MRF scans were performed in 33 epilepsy patients with FCD, 60 normal controls and 26 disease controls. A machine-learning (ML) framework based on MRF was developed to automatically classify FCD from normal cortex and separating FCD subtypes.
Results: MRF-based ML models showed high accuracies, with performances superior to the yields of clinical review.
Impact: Our approach contributes to noninvasive epilepsy presurgical evaluation, as well as an integrated clinical-pathological-imaging understanding of the FCD spectrum.
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