College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.
The Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, China.
CNS Neurosci Ther. 2022 Mar;28(3):354-363. doi: 10.1111/cns.13773. Epub 2021 Dec 23.
Epilepsy is a serious hazard to human health. Minimally invasive surgery is an extremely effective treatment to refractory epilepsy currently if the location of epileptic foci is given. However, it is challenging to locate the epileptic foci since a multitude of patients are MRI-negative. It is well known that DKI (diffusion kurtosis imaging) can analyze the pathological changes of local tissues and other regions of epileptic foci at the molecular level. In this article, we propose a new localization way for epileptic foci based on machine-learning method with kurtosis tensor in DKI.
We recruited 59 children with hippocampus epilepsy and 70 age- and sex-matched normal controls; their T1-weighted images and DKI were collected simultaneously. Then, the hippocampus in DKI is segmented based on a mask as a local brain region, and DKE is utilized to estimate the kurtosis tensor of each subject's hippocampus. Finally, the kurtosis tensor is fed into SVM (support vector machine) to identify epilepsy.
The classifier produced 95.24% accuracy for patient versus normal controls, which is higher than that obtained with FA (fractional anisotropy) and MK (mean kurtosis). Experimental results show that the kurtosis tensor is a kind of remarkable feature to identify epilepsy, which indicates that DKI images can act as an important biomarker for epilepsy from the view of clinical diagnosis.
Although the classification task for epileptic patients and normal controls discussed in this article did not directly achieve the location of epileptic foci and only identified epilepsy on certain brain region, the epileptic foci can be located with the results of identifying results on other brain regions.
癫痫是一种严重危害人类健康的疾病。如果明确了癫痫灶的位置，微创外科手术是目前治疗耐药性癫痫的一种非常有效的方法。然而，对于大量 MRI 阴性的患者来说，癫痫灶的定位仍然具有挑战性。众所周知，DKI（扩散峰度成像）可以在分子水平上分析癫痫灶局部组织和其他区域的病理变化。在本文中，我们提出了一种基于机器学习方法的新的癫痫灶定位方法，该方法利用 DKI 中的峰度张量。
我们招募了 59 名海马癫痫儿童和 70 名年龄和性别匹配的正常对照者；同时采集他们的 T1 加权图像和 DKI。然后，基于掩模对 DKI 中的海马体进行分割，作为局部脑区，并利用 DKE 估计每个受试者海马体的峰度张量。最后，将峰度张量输入 SVM（支持向量机）以识别癫痫。
分类器对患者与正常对照组的识别准确率为 95.24%，高于 FA（各向异性分数）和 MK（平均峰度）的识别准确率。实验结果表明，峰度张量是一种识别癫痫的显著特征，表明从临床诊断的角度来看，DKI 图像可以作为癫痫的重要生物标志物。
虽然本文讨论的癫痫患者和正常对照组的分类任务并没有直接实现癫痫灶的定位，而只是在特定脑区识别癫痫，但可以根据其他脑区的识别结果来定位癫痫灶。
