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Abstract #1306

Multiparametric Characterization of Focal Cortical Dysplasia Using Three-Dimensional MR Fingerprinting

Ting-Yu Su1,2, Joon Yul Choi1,3, Siyuan Hu2, Xiaofeng Wang4, Ingmar Blümcke1,5, Katherine Chiprean1, Balu Krishnan1, Zheng Ding1,2, Ken Sakaie6, Hiroatsu Murakami1, Imad Najm1, Stephen Jones6, Dan Ma2, and Zhong Irene Wang1
1Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States, 2Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 3Biomedical Engineering, Yonsei University, Wonju, Korea, Republic of, 4Quantitative Health Science, Cleveland Clinic, Cleveland, OH, United States, 5Neuropathology, University Hospital Erlangen, Erlangen, Germany, 6Imaging Institute, Cleveland Clinic, Cleveland, OH, United States

Synopsis

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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Keywords