Objective

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.

Methods

Standardized MPRAGE, T₂ 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.

Results

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.

Significance

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.

中文翻译：

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区分 II 型局灶性皮质发育不良与正常皮质：一种新的规范建模方法

客观的

局灶性皮质发育不良 (FCD) 是明显非病变性耐药局灶性癫痫的常见原因。MRI 上细微 FCD 的视觉检测在临床上很重要，而且通常具有挑战性。在这项研究中，我们实施了一组 3D 局部图像过滤器，这些过滤器改编自计算机视觉应用程序，以表征灰白色交界处周围正常皮层的外观。我们创建了一个规范模型，作为自动 FCD 检测的新型多变量约束异常值方法的基础。

方法

在 15 名经放射学或组织学诊断为 FCD 的患者和 30 名健康志愿者中获得了标准化的 MPRAGE、T ₂和 FLAIR MR 图像。为每个 MR 对比度计算多尺度 3D 局部图像过滤器，然后采样到灰白色交界表面。使用迭代高斯化程序，我们创建了健康志愿者皮层变异性的规范模型，允许识别异常区域并估计正常皮层和 FCD 病变的相似性。我们使用局部归一化后的约束异常值方法来根据平均 FCD 特征向量的投影自动检测 FCD 病变。

结果

FCD 以及一些正常的皮层区域，如初级感觉运动和旁边缘区域，显示为异常值。诸如旁边缘区和前岛叶等区域与 FCD 具有相似的特征。我们的约束异常值方法允许以 80% 的灵敏度和 70% 的特异性进行自动 FCD 检测。

意义

使用多尺度局部图像过滤器的规范模型可用于描述正常的皮层变异性。尽管 FCD 与某些皮质区域（例如前脑岛和旁边缘皮质）相似，但可以使用约束异常值检测方法来识别它们。我们检测异常值和估计相似性的方法是通用的，可以扩展到识别其他类型的病变或非典型皮质区域。