From 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.).
Radiology. 2023 Jun;307(5):e220927. doi: 10.1148/radiol.220927. Epub 2023 May 2.
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.
局灶性癫痫是一种常见且严重的神经系统疾病。神经影像学旨在确定致痫区(EZ),最好是作为宏观结构病变。对于大约三分之一的慢性药物难治性局灶性癫痫患者,使用标准的 3.0-T MRI 无法精确确定 EZ。这可能是由于 EZ 在影像学上不可检测,或者癫痫发作是由生理异常引起的,而不是结构性病变。最近已经证明,计算图像处理有助于放射学评估,并通过增强可疑区域的视觉显著性来提高发现可疑区域的成功率。虽然结构图像分析处于 EZ 检测的前沿,但生理图像分析也被证明可以提供有关 EZ 位置的有价值信息。本文综述总结并解释了目前用于图像分析的最先进的计算方法,并展示了它们在 EZ 检测中的潜力。讨论了这些方法的当前局限性和可能的未来方向,以增强 EZ 检测。