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Published on: September 20, 2024
Advances in Image Processing for Epileptogenic Zone Detection with MRI
Daniel Uher1, Gerhard S Drenthen1, Olaf E M G Schijns1
1From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.).
Abstract:
Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.
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