Abstract #0994

Automated Surfaced-based Detection of Focal Cortical Dysplasia using MR Fingerprinting

Ting-Yu Su^1,2, Siyuan Hu², Xiaofeng Wang³, Sophie Adler⁴, Konrad Wagstyl⁵, Zheng Ding^1,2, Joon Yul Choi¹, Ken Sakaie⁶, Ingmar Blümcke^1,7, Hiroatsu Murakami¹, Stephen Jones⁶, Imad Najm¹, 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, ³Quantitative Health Science, Cleveland Clinic, Cleveland, OH, United States, ⁴University College London Great Ormond Street Institute for Child Health, London, United Kingdom, ⁵Wellcome Centre for Human Neuroimaging, London, United Kingdom, ⁶Imaging Institute, Cleveland Clinic, Cleveland, OH, United States, ⁷Neuropathology, University Hospitals Erlangen, Erlangen, Germany

Synopsis

Keywords: Epilepsy, MR FingerprintingFocal cortical dysplasia (FCD) is a common pathology in medically intractable focal epilepsy and often difficult to detect by visual inspection of conventional MRI. We developed a framework for automatic FCD detection using surface-based processing of conventional MRI and MR fingerprinting data. Thirty-six patients with FCD and 48 healthy controls were included. Improved vertex-wise and cluster-wise performance was seen when MRF and FLAIR features were added to T1w data. A second-stage cluster-wise classifier showed efficacy to reduce false-positive clusters. Interim results of patient-level sensitivity of 76% and low false-positive clusters in controls supported potential clinical applicability of the proposed framework.

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