Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness

Cuixia Feng,Miaomiao Lu,Maoyu Tian,Hulin Zhao,Junhai Wen

doi:10.1186/s12938-020-0757-8

Cuixia Feng, Miaomiao Lu + Show 3 more

Open Access

https://doi.org/10.1186/s12938-020-0757-8

Copy DOI

Abstract

BackgroundFocal cortical dysplasia (FCD) is a neuronal migration disorder and is a major cause of drug-resistant epilepsy. However, many focal abnormalities remain undetected during routine visual inspection, and many patients with histologically confirmed FCD have normal fluid-attenuated inversion recovery (FLAIR-negative) images. The aim of this study was to quantitatively evaluate the changes in cortical thickness with magnetic resonance (MR) imaging of patients to identify FCD lesions from FLAIR-negative images.MethodsWe first used the three-dimensional (3D) Laplace method to calculate the cortical thickness for individuals and obtained the cortical thickness mean image and cortical thickness standard deviation (SD) image based on all 32 healthy controls. Then, a cortical thickness extension map was computed by subtracting the cortical thickness mean image from the cortical thickness image of each patient and dividing the result by the cortical thickness SD image. Finally, clusters of voxels larger than three were defined as the FCD lesion area from the cortical thickness extension map.ResultsThe results showed that three of the four lesions that occurred in non-temporal areas were detected in three patients, but the detection failed in three patients with lesions that occurred in the temporal area. The quantitative analysis of the detected lesions in voxel-wise on images revealed the following: specificity (99.78%), accuracy (99.76%), recall (67.45%), precision (20.42%), Dice coefficient (30.01%), Youden index (67.23%) and area under the curve (AUC) (83.62%).ConclusionOur studies demonstrate an effective method to localize lesions in non-temporal lobe regions. This novel method automatically detected FCD lesions using only FLAIR-negative images from patients and was based only on cortical thickness feature. The method is noninvasive and more effective than a visual analysis for helping doctors make a diagnosis.

Highlights

Focal cortical dysplasia (FCD) is a neuronal migration disorder and is a major cause of drug-resistant epilepsy
Focal cortical dysplasia (FCD) is a malformation of cortical development (MCD) and is one of the most common causes of intractable epilepsy that was defined by Taylor et al in 1971 [1]
FCD lesions can be found by comparing cortical thickness in patients to normal controls

Summary

Introduction

Focal cortical dysplasia (FCD) is a neuronal migration disorder and is a major cause of drug-resistant epilepsy. The aim of this study was to quantitatively evaluate the changes in cortical thickness with magnetic resonance (MR) imaging of patients to identify FCD lesions from FLAIR-negative images. Several conventional methods for the detection of epileptic foci include the voxelbased morphometry algorithm (VBM) [2,3,4,5], the surface-based morphometry algorithm (SBM) [6, 7] and the postprocessing method [8, 9] based on voxel feature extraction. The SBM technique is mainly used to reconstruct the cerebral cortex, extract effective features, and use a machine learning method to classify and find the lesion area. There are two novel studies examining FCD lesion recognition and location using an advanced convolutional neural network (CNN), which have achieved effective results [10, 11]

Objectives

Methods

Results

Discussion

Conclusion