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Abstract #3341

Multiparametric Characterization of Focal Cortical Dysplasia using MR Fingerprinting

Zhong Irene Wang1, Joon Yul Choi1, Siyuan Hu2, Yingying Tang1, TingYu Su1,2, Ingmar Blümcke1,3, Stephen Jones4, Ken Sakaie4, Imad Najm1, and Dan Ma2
1Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States, 2Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 3Neuropathology, University of Erlangen, Erlangen, Germany, 4Imaging Institute, Cleveland Clinic, Cleveland, OH, United States


Focal cortical dysplasia (FCD) is a common pathology underlying medically intractable focal epilepsies. Conventional MRI can be limited in characterizing subtle FCD, due to the lack of quantitative measurements for tissue properties. Here, we proposed a multiparametric machine learning (ML) approach based on high-resolution 3D MR fingerprinting (MRF) to characterize FCD lesions. The ML model showed robust accuracy of 96%, 89%, and 79% to separate FCD from normal cortex, FCD type I from type II, and FCD type IIa from type IIb, respectively. Our findings suggest the usefulness of the multiparametric MRF ML approach to improve noninvasive epilepsy presurgical evaluation.

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