EEG Section, Office of the Clinical Director, NINDS, National Institutes of Health, United States.
Cadwell, Kennewick, WA, United States.
Neuroimage Clin. 2021;30:102565. doi: 10.1016/j.nicl.2021.102565. Epub 2021 Jan 19.
Focal cortical dysplasias (FCDs) are a common cause of apparently non-lesional drug-resistant focal epilepsy. Visual detection of subtle FCDs on MRI is clinically important and often challenging. In this study, we implement a set of 3D local image filters adapted from computer vision applications to characterize the appearance of normal cortex surrounding the gray-white junction. We create a normative model to serve as the basis for a novel multivariate constrained outlier approach to automated FCD detection.
Standardized MPRAGE, T and FLAIR MR images were obtained in 15 patients with radiologically or histologically diagnosed FCDs and 30 healthy volunteers. Multiscale 3D local image filters were computed for each MR contrast then sampled onto the gray-white junction surface. Using an iterative Gaussianization procedure, we created a normative model of cortical variability in healthy volunteers, allowing for identification of outlier regions and estimates of similarity in normal cortex and FCD lesions. We used a constrained outlier approach following local normalization to automatically detect FCD lesions based on projection onto the mean FCD feature vector.
FCDs as well as some normal cortical regions such as primary sensorimotor and paralimbic regions appear as outliers. Regions such as the paralimbic regions and the anterior insula have similar features to FCDs. Our constrained outlier approach allows for automated FCD detection with 80% sensitivity and 70% specificity.
A normative model using multiscale local image filters can be used to describe the normal cortical variability. Although FCDs appear similar to some cortical regions such as the anterior insula and paralimbic cortices, they can be identified using a constrained outlier detection approach. Our method for detecting outliers and estimating similarity is generic and could be extended to identification of other types of lesions or atypical cortical areas.
局灶性皮质发育不良（FCD）是一种常见的无明显病变致药物难治性局灶性癫痫的原因。MRI 上对细微 FCD 的视觉检测具有重要的临床意义，且通常具有挑战性。在本研究中，我们实施了一组来自计算机视觉应用的 3D 局部图像滤波器，以描述围绕灰白交界的正常皮质的外观。我们创建了一个规范模型，作为一种新的多元约束异常值方法用于自动 FCD 检测的基础。
对 15 例经放射学或组织学诊断为 FCD 和 30 例健康志愿者的标准 MPRAGE、T 和 FLAIR MR 图像进行了研究。对每个 MR 对比度进行了多尺度 3D 局部图像滤波，然后对灰白交界表面进行采样。使用迭代高斯化过程，我们创建了一个健康志愿者皮质变异性的规范模型，允许识别异常区域以及正常皮质和 FCD 病变的相似性估计。我们使用局部归一化后的约束异常值方法，根据对 FCD 特征向量的投影自动检测 FCD 病变。
FCD 以及一些正常皮质区域（如初级感觉运动和边缘皮质区域）表现为异常值。边缘皮质区域和前岛叶等区域与 FCD 具有相似的特征。我们的约束异常值方法可以实现 80%的敏感性和 70%的特异性的自动 FCD 检测。
使用多尺度局部图像滤波器的规范模型可用于描述正常皮质的变异性。虽然 FCD 与前岛叶和边缘皮质等一些皮质区域相似，但可以使用约束异常值检测方法来识别它们。我们用于检测异常值和估计相似性的方法是通用的，可扩展用于识别其他类型的病变或非典型皮质区域。
