CN111369558A - Child epilepsy positioning method based on multi-modal brain images - Google Patents

Abstract

Translated fromChinese

本发明公开了一种基于多模态脑影像的儿童癫痫定位方法，涉及癫痫技术领域；它的定位方法为：在创建基于iEEG与sEEG正问题模型之前，首先需对多模态脑影像进行预处理，其次，为结合iEEG与sEEG创建其正问题模型；为明确源信号与sEEG和iEEG的关系，基于临界元方法求解传导矩阵，再基于最小范数估计方法创建结合sEEG与iEEG的正问题模型；最后，基于Tikhonov正则化方法求解逆问题，其中通过L曲线准则选取合适的正则化参数，估算出源信号后对其进行平滑与归一化处理，再通过源成像与阈值定位出癫痫灶；本发明在定位致痫灶的过程中，通过融合多模态影像，创建基于sEEG和iEEG的正问题模型，求解逆问题估算源信号，最终定位致痫灶。

The invention discloses a method for locating epilepsy in children based on multimodal brain images, and relates to the technical field of epilepsy. Second, create a positive problem model for combining iEEG and sEEG; in order to clarify the relationship between the source signal and sEEG and iEEG, solve the conduction matrix based on the critical element method, and then create a positive problem model combining sEEG and iEEG based on the minimum norm estimation method ; Finally, the inverse problem is solved based on the Tikhonov regularization method, in which the appropriate regularization parameters are selected through the L-curve criterion, the source signal is estimated and then smoothed and normalized, and then the epilepsy focus is located by source imaging and threshold; In the process of locating the epileptogenic foci, the present invention creates a positive problem model based on sEEG and iEEG by fusing multimodal images, solves the inverse problem to estimate the source signal, and finally locates the epileptogenic foci.

Description

Translated fromChinese

一种基于多模态脑影像的儿童癫痫定位方法A method for localizing epilepsy in children based on multimodal brain imaging

技术领域technical field

本发明属于癫痫技术领域，具体涉及一种基于多模态脑影像的儿童癫痫定位方法。The invention belongs to the technical field of epilepsy, and in particular relates to a method for locating epilepsy in children based on multimodal brain images.

背景技术Background technique

癫痫是一种大脑神经元突发性异常放电导致短暂的大脑功能障碍的慢性疾病。据中国最新流行病学资料显示，儿童癫痫发病率约为5‰，约有600万儿童癫痫患者。另外，据调查，中国约有900万左右的癫痫患者，同时每年新增加癫痫患者约40万，在中国癫痫已经成为神经科仅次于头痛的第二大常见病，且半数以上的成年癫痫源于儿童时期，癫痫明确的患病机制尚未研究透彻，病因繁杂多样，有研究表明是遗传和后天因素（脑部的创伤、中风等）的单独的作用或多重作用导致的，其中大约60%的儿童患者是无法给出明确的病因。所以，针对癫痫的治疗是有很大的挑战的，当前的治疗方法主要有药物治疗、手术切除和电刺激等。癫痫以目前的医学水平是无法完全治愈，70%的儿童癫痫患者可以通过抗癫痫的药物的治疗得到有效的控制，但是任有20-30%的儿童患者是难治性癫痫，需要手术切除有希望成为无癫痫发作的儿童。但任有部分患者切除后仍未消除癫痫，目前的研究专家一致认为是致痫灶定位不精确导致的，因此精准的定位儿童致痫灶不经可以提高手术成功率，而且可以降低术后带来的损伤。Epilepsy is a chronic disease in which the sudden abnormal discharge of brain neurons leads to transient brain dysfunction. According to the latest epidemiological data in China, the incidence of epilepsy in children is about 5‰, and there are about 6 million children with epilepsy. In addition, according to the survey, there are about 9 million epilepsy patients in China, and about 400,000 new epilepsy patients are added every year. Epilepsy has become the second most common disease in neurology after headache in China, and more than half of the adult epilepsy is the source of epilepsy. In childhood, the clear pathogenesis of epilepsy has not been thoroughly studied, and the etiology is complex and diverse. Studies have shown that it is caused by the single or multiple effects of genetic and acquired factors (brain trauma, stroke, etc.), of which about 60%. A clear etiology cannot be given in children. Therefore, the treatment of epilepsy is a great challenge. The current treatment methods mainly include drug therapy, surgical resection and electrical stimulation. Epilepsy cannot be completely cured at the current medical level. 70% of children with epilepsy can be effectively controlled by anti-epileptic drugs, but any 20-30% of children with epilepsy have refractory epilepsy and require surgical resection. Want to be a seizure-free child. However, some patients have not yet eliminated epilepsy after excision. Current research experts agree that it is caused by inaccurate localization of epileptogenic foci. Therefore, accurate localization of epileptogenic foci in children can improve the success rate of surgery and reduce postoperative complications. damage to come.

临床诊断致痫灶通常需要集中神经内科和神经外科癫痫专家，结合病人的发病临床表现，以及医学成像技术进行综合的判断，如基于sEEG与MRI定位方法空间覆盖率高但是定位精度不够、基于iEEG定位方法定位精度高但空间覆盖率小，人工致痫灶定位耗时耗力。The clinical diagnosis of epilepsy foci usually requires centralized neurology and neurosurgery epilepsy experts, combined with the clinical manifestations of the patient, and medical imaging technology to make a comprehensive judgment. For example, sEEG and MRI positioning methods have high spatial coverage but insufficient positioning accuracy, and iEEG-based positioning methods have high spatial coverage. The positioning method has high positioning accuracy but small spatial coverage, and artificial epileptogenic foci positioning is time-consuming and labor-intensive.

国内外的研究现状：Research status at home and abroad:

近些年，随着微电子工艺，计算机信息处理技术、癫痫的临床研究的发展，利用信号处理手段致痫灶定位的研究正在逐步兴起，国内外许多研究机构都在对该领域进行研究与探索，例如美国的明尼苏达大学，芝加哥大学、比利时的根特大学，中国的华南理工，中科院苏州医工所，复旦大学附属儿科医院等。接下来介绍目前较为流行的致痫灶定位方法。In recent years, with the development of microelectronic technology, computer information processing technology, and clinical research on epilepsy, research on the location of epileptogenic foci using signal processing methods is gradually emerging, and many research institutions at home and abroad are conducting research and exploration in this field. For example, the University of Minnesota in the United States, the University of Chicago, the University of Ghent in Belgium, the South China University of Technology in China, the Suzhou Medical Institute of the Chinese Academy of Sciences, the Pediatric Hospital of Fudan University, etc. Next, the more popular localization methods of epileptogenic foci are introduced.

发明内容SUMMARY OF THE INVENTION

为解决现有的问题；本发明的目的在于提供一种基于多模态脑影像的儿童癫痫定位方法。In order to solve the existing problems, the purpose of the present invention is to provide a method for locating epilepsy in children based on multimodal brain images.

本发明的一种基于多模态脑影像的儿童癫痫定位方法，它的定位方法为：在创建基于iEEG与sEEG正问题模型之前，首先需对多模态脑影像进行预处理，预处理包括：由于MRI与CT在不同机器采集，因此对其进行重定向与重采样使其统一规格；为去除iEEG与sEEG测量时的脑电噪声，对其进行滤波与基线校正等处理；由于成人与儿童之间的大脑结构存在差异，因此采用基于边界元方法与分布式源建模方法来创建个性的头模型与源模型；其次，为结合iEEG与sEEG创建其正问题模型，需要将sEEG、iEEG、MRI、CT相融合；基于刚体变换方法配准与融合MRI与CT、再通过手动标定MRI&CT中iEEG电极从而使iEEG与MRI&CT相融合；再通过头模型与sEEG手动进行坐标矫正，将sEEG与MRI&CT相融合，从而使多模态脑影像相互融合；然后，为明确源信号与sEEG和iEEG的关系，基于临界元方法求解传导矩阵，再基于最小范数估计方法创建结合sEEG与iEEG的正问题模型；最后，基于Tikhonov正则化方法求解逆问题，其中通过L曲线准则选取合适的正则化参数，估算出源信号后对其进行平滑与归一化处理，再通过源成像与阈值定位出癫痫灶。A method for locating epilepsy in children based on multimodal brain images of the present invention, the positioning method is as follows: before creating a positive problem model based on iEEG and sEEG, the multimodal brain images need to be preprocessed first, and the preprocessing includes: Since MRI and CT are collected in different machines, they are re-directed and re-sampled to make them uniform; in order to remove the EEG noise during iEEG and sEEG measurement, they are processed by filtering and baseline correction; due to the difference between adults and children There are differences in the brain structure between the two, so the boundary element based method and the distributed source modeling method are used to create the head model and source model of individuality; secondly, in order to combine iEEG and sEEG to create its positive problem model, it is necessary to combine sEEG, iEEG, MRI Fusion of MRI and CT; registration and fusion of MRI and CT based on the rigid body transformation method, and fusion of iEEG and MRI&CT by manually calibrating the iEEG electrodes in MRI&CT; Then, in order to clarify the relationship between the source signal and sEEG and iEEG, the conduction matrix was solved based on the critical element method, and then the positive problem model combining sEEG and iEEG was created based on the minimum norm estimation method. , based on the Tikhonov regularization method to solve the inverse problem, in which the appropriate regularization parameters are selected through the L-curve criterion, the source signal is estimated and then smoothed and normalized, and then the epilepsy focus is located through source imaging and threshold.

与现有技术相比，本发明的有益效果为：在定位致痫灶的过程中，通过融合多模态影像，创建基于sEEG和iEEG的正问题模型，求解逆问题估算源信号，最终定位致痫灶。Compared with the prior art, the beneficial effects of the present invention are: in the process of locating the epileptogenic foci, by fusing multimodal images, a positive problem model based on sEEG and iEEG is created, the inverse problem is solved to estimate the source signal, and finally the source signal is located. Epilepsy focus.

附图说明Description of drawings

为了易于说明，本发明由下述的具体实施及附图作以详细描述。For ease of description, the present invention is described in detail by the following specific implementations and accompanying drawings.

图1为本发明的流程图；Fig. 1 is the flow chart of the present invention;

图2为本发明中预处理的流程图；Fig. 2 is the flow chart of preprocessing in the present invention;

图3为本发明中个性头模型的示意图。FIG. 3 is a schematic diagram of a personality head model in the present invention.

具体实施方式Detailed ways

为使本发明的目的、技术方案和优点更加清楚明了，下面通过附图中示出的具体实施例来描述本发明。但是应该理解，这些描述只是示例性的，而并非要限制本发明的范围。此外，在以下说明中，省略了对公知结构和技术的描述，以避免不必要地混淆本发明的概念。In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described below through the specific embodiments shown in the accompanying drawings. It should be understood, however, that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concepts of the present invention.

在此，还需要说明的是，为了避免因不必要的细节而模糊了本发明，在附图中仅仅示出了与根据本发明的方案密切相关的结构和/或处理步骤，而省略了与本发明关系不大的其他细节。Here, it should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only the structures and/or processing steps closely related to the solution according to the present invention are shown in the drawings, and the related structures and/or processing steps are omitted. Other details not relevant to the invention.

如图1、图2所示，本具体实施方式采用以下技术方案：它的定位方法如下：As shown in Figure 1 and Figure 2, this specific embodiment adopts the following technical solutions: its positioning method is as follows:

在创建基于iEEG与sEEG正问题模型之前，首先需对多模态脑影像进行预处理，预处理包括：由于MRI与CT在不同机器采集，因此对他们进行重定向与重采样使其统一规格；为去除iEEG与sEEG测量时的脑电噪声，对其进行滤波与基线校正等处理；由于成人与儿童之间的大脑结构存在差异，因此采用基于边界元（BEM）方法与分布式源建模方法来创建个性的头模型与源模型。其次，为结合iEEG与sEEG创建其正问题模型，需要将sEEG、iEEG、MRI、CT相融合；基于刚体变换方法配准与融合MRI与CT、再通过手动标定MRI&CT中iEEG电极从而使iEEG与MRI&CT相融合；再通过头模型与sEEG手动进行坐标矫正，将sEEG与MRI&CT相融合，从而使多模态脑影像相互融合。然后，为明确源信号与sEEG和iEEG的关系，基于临界元方法求解传导矩阵，再基于最小范数估计（MNE）方法创建结合sEEG与iEEG的正问题模型。最后，基于Tikhonov正则化方法求解逆问题，其中通过L曲线准则选取合适的正则化参数，估算出源信号后对其进行平滑与归一化处理，再通过源成像与阈值定位出癫痫灶。Before creating a positive problem model based on iEEG and sEEG, the multimodal brain images need to be preprocessed first. The preprocessing includes: since MRI and CT are acquired on different machines, they are redirected and resampled to make them uniform; In order to remove the EEG noise during iEEG and sEEG measurement, filtering and baseline correction are performed. Due to the differences in brain structure between adults and children, the boundary element (BEM)-based method and distributed source modeling method are adopted. to create individual head models and source models. Secondly, in order to combine iEEG and sEEG to create a positive problem model, it is necessary to fuse sEEG, iEEG, MRI, and CT; register and fuse MRI and CT based on the rigid body transformation method, and then manually calibrate iEEG electrodes in MRI&CT to make iEEG and MRI&CT possible Then, the coordinates are corrected manually through the head model and sEEG, and the sEEG and MRI&CT are fused, so that the multimodal brain images are fused with each other. Then, in order to clarify the relationship between the source signal and sEEG and iEEG, the conduction matrix is solved based on the critical element method, and then the positive problem model combining sEEG and iEEG is created based on the minimum norm estimation (MNE) method. Finally, the inverse problem is solved based on the Tikhonov regularization method, in which the appropriate regularization parameters are selected through the L-curve criterion, the source signal is estimated and then smoothed and normalized, and then the epilepsy focus is located by source imaging and threshold.

本实施例主要是对儿童致痫灶定位方法进行研究，通过分析儿童癫痫现有方法的问题与优势以及影像学定位进展等方面的研究，结合多模态脑影像数据更精确的定位致痫灶。在定位致痫灶的过程中，通过融合多模态影像，创建基于sEEG和iEEG的正问题模型，求解逆问题估算源信号，最终定位致痫灶。本实验所采用的数据，来自复旦大学附属儿科医院，包括原始的MRI、CT，sEEG、iEEG等多模态数据。This example mainly studies the method for locating epileptogenic foci in children. By analyzing the problems and advantages of existing methods for children with epilepsy and research on imaging localization progress, combined with multimodal brain imaging data, the epileptogenic foci can be more accurately located. . In the process of locating the epileptogenic foci, a positive problem model based on sEEG and iEEG was created by fusing multimodal images, and the inverse problem was solved to estimate the source signal, and finally the epileptogenic foci was located. The data used in this experiment came from the Pediatric Hospital of Fudan University, including original MRI, CT, sEEG, iEEG and other multimodal data.

本实施例研究的主要内容如下：The main contents of this example study are as follows:

首先为分析基于sEEG源成像儿童致痫灶定位方法。本文基于MNE算法对sEEG进行源成像。首先为明确sEEG与脑内源信号之间的关系，基于MNE算法求解正问题，然后为反演脑内源信号，基于Tikhonov正则化求解逆问题，最后通过仿真实验分析该方法的优劣。The first is to analyze the localization method of epileptogenic foci in children based on sEEG source imaging. In this paper, source imaging of sEEG is performed based on the MNE algorithm. Firstly, in order to clarify the relationship between sEEG and brain endogenous signals, the positive problem is solved based on the MNE algorithm, and then the inverse problem is solved based on Tikhonov regularization to invert the brain endogenous signals. Finally, the advantages and disadvantages of this method are analyzed through simulation experiments.

其次提出基于多模态脑影像数据的儿童致痫灶定位，由于MRI、CT等多种模态下的原始影像存在方向混乱，体素及体积大小不一致等问题，对MRI、CT进行处理，iEEG与sEEG在采集时受外界与脑内影响，对其进行去尾迹等处理，由于儿童在不同年龄脑结构有一定的差距，因此要创建个性头模型提高定位精度。Secondly, the location of epileptogenic foci in children based on multimodal brain image data is proposed. Since the original images in MRI, CT and other modalities have problems such as direction confusion, inconsistent voxel and volume size, etc., MRI, CT are processed, iEEG Unlike sEEG, which is affected by the outside world and the brain during acquisition, it is processed by de-wake and other processing. Since there is a certain gap in the brain structure of children at different ages, it is necessary to create a personality head model to improve the positioning accuracy.

多模态脑影像融合：Multimodal brain image fusion:

由于创建基于iEEG与sEEG正问题模型，首先是确定iEEG与sEEG在大脑中的位置，再通过求解传导矩阵，明确脑电电极电位与源信号的关系，进而建立基于iEEG与sEEG的正问题模型；因此需将MRI，CT，sEEG，iEEG等多模态脑影像相融合，在通过位置矫正或手动标定等方式获取iEEG与sEEG的位置。首先基于刚体转换方法将MRI与CT进行配准与融合；而sEEG电极是根据国际标准128通道的结构定位在头皮上，所以通过个性的头模型（如图3所示）对sEEG进行矫正即可使sEEG与MRI&CT融合；与sEEG不同的是iEEG并没有从任何标准化的技术中获益，因为它在不同的病例和病人之间有所不同。因此，sEEG的电极放置是个性化的，并且依赖于主体。植入策略包括植入前假设，该假设考虑了患者解剖图像中的可见病变，如磁共振成像（MRI）图像、更可能的发作结构、早期和晚期扩散区域以及与功能网络的相互作用，需要足够数量的记录通道才能有一个信息丰富的sEEG系统。另一方面，电极数量过多会增加植入的风险和费用，在脑组织中植入15根以上的电极棒通常是很少见的。因此根据复旦大学附属儿科医院的真实sEEG植入策略进行放置，其中共8根电极棒主要位于左额叶和左颞叶，每个柄由16个触点组成，恒定的触点间距离为3.5 mm。由于iEEG在CT中显示较为明显，因此通过手动标定MRI&CT中的iEEG，进而将iEEG与MRI&CT融合。由此CT，MRI，iEEG，sEEG多模态影像都以相互融合，从而可定位出iEEG，sEEG在大脑中的精确位置。Due to the creation of a positive problem model based on iEEG and sEEG, the first step is to determine the position of iEEG and sEEG in the brain, and then by solving the conduction matrix, the relationship between the EEG electrode potential and the source signal is clarified, and then a positive problem model based on iEEG and sEEG is established; Therefore, it is necessary to fuse multimodal brain images such as MRI, CT, sEEG, and iEEG, and obtain the positions of iEEG and sEEG through position correction or manual calibration. First, the MRI and CT are registered and fused based on the rigid body conversion method; the sEEG electrodes are positioned on the scalp according to the structure of the international standard 128 channels, so the sEEG can be corrected through the personalized head model (as shown in Figure 3). Fusion of sEEG with MRI &CT; unlike sEEG iEEG does not benefit from any standardized technique as it varies from case to case and patient to patient. Therefore, electrode placement for sEEG is individualized and subject-dependent. Implantation strategies include a preimplantation hypothesis that takes into account visible lesions in patient anatomical images such as magnetic resonance imaging (MRI) images, more likely seizure structures, areas of early and late spread, and interactions with functional networks, requiring A sufficient number of recording channels is required to have an informative sEEG system. On the other hand, too many electrodes can increase the risk and cost of implantation, and implantation of more than 15 electrode rods in brain tissue is usually rare. Therefore, the placement is based on the real sEEG implantation strategy of the Pediatric Hospital of Fudan University, in which a total of 8 electrode rods are mainly located in the left frontal lobe and left temporal lobe, each stalk consists of 16 contacts, and the constant inter-contact distance is 3.5 mm. Since iEEG is more obvious in CT, the iEEG in MRI&CT is manually calibrated, and then iEEG is fused with MRI&CT. As a result, CT, MRI, iEEG, and sEEG multimodal images are all fused with each other, so that the precise location of iEEG and sEEG in the brain can be located.

其次，由于基于sEEG源成像的定位精度低，因此基于传统最小范数估计（MNE）方法，引入iEEG约束项，创建以iEEG为约束项的基于iEEG和sEEG的正问题模型；求解逆问题时，需要结合sEEG约束项，因此本文通过双参数正则化方法求解逆问题，根据L曲线准则选取双参数正则化的参数；再使用双正则化计算逆问题；Secondly, due to the low positioning accuracy based on sEEG source imaging, based on the traditional minimum norm estimation (MNE) method, iEEG constraints are introduced to create a positive problem model based on iEEG and sEEG with iEEG as constraints; when solving the inverse problem, The sEEG constraint term needs to be combined, so this paper solves the inverse problem by the two-parameter regularization method, selects the parameters of the two-parameter regularization according to the L-curve criterion, and then uses the double regularization to calculate the inverse problem;

最后通过逆问题估算源信号后，由于癫痫是神经元异常放电，为反演更接近真实的放电过程进行对源信号进行平滑处理；为方便设置阈值定位致痫灶，对源信号进行归一化处理，最后设置阈值，定位异常的源信号的源点，就可定位出致痫灶。再通过仿真实验对比两种方法的定位效果。Finally, after estimating the source signal through the inverse problem, since epilepsy is the abnormal discharge of neurons, the source signal is smoothed to invert the discharge process closer to the real; in order to set the threshold to locate the epileptogenic foci, the source signal is normalized After processing, and finally setting the threshold, and locating the source point of the abnormal source signal, the epileptogenic foci can be located. Then, the positioning effects of the two methods are compared through simulation experiments.

对于本领域技术人员而言，显然本发明不限于上述示范性实施例的细节，而且在不背离本发明的精神或基本特征的情况下，能够以其他的具体形式实现本发明。因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本发明的范围由所附权利要求而不是上述说明限定，因此旨在将落在权利要求的等同要件的含义和范围内的所有变化囊括在本发明内。It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, but that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of the invention is to be defined by the appended claims rather than the foregoing description, which are therefore intended to fall within the scope of the claims. All changes within the meaning and scope of the equivalents of , are included in the present invention.

此外，应当理解，虽然本说明书按照实施方式加以描述，但并非每个实施方式仅包含一个独立的技术方案，说明书的这种叙述方式仅仅是为清楚起见，本领域技术人员应当将说明书作为一个整体，各实施例中的技术方案也可以经适当组合，形成本领域技术人员可以理解的其他实施方式。In addition, it should be understood that although this specification is described in terms of embodiments, not each embodiment only includes an independent technical solution, and this description in the specification is only for the sake of clarity, and those skilled in the art should take the specification as a whole , the technical solutions in each embodiment can also be appropriately combined to form other implementations that can be understood by those skilled in the art.

Claims (1)

Translated fromChinese

1.一种基于多模态脑影像的儿童癫痫定位方法，其特征在于：它的定位方法为：在创建基于iEEG与sEEG正问题模型之前，首先需对多模态脑影像进行预处理，预处理包括：由于MRI与CT在不同机器采集，因此对其进行重定向与重采样使其统一规格；为去除iEEG与sEEG测量时的脑电噪声，对其进行滤波与基线校正等处理；由于成人与儿童之间的大脑结构存在差异，因此采用基于边界元方法与分布式源建模方法来创建个性的头模型与源模型；其次，为结合iEEG与sEEG创建其正问题模型，需要将sEEG、iEEG、MRI、CT相融合；基于刚体变换方法配准与融合MRI与CT、再通过手动标定MRI&CT中iEEG电极从而使iEEG与MRI&CT相融合；再通过头模型与sEEG手动进行坐标矫正，将sEEG与MRI&CT相融合，从而使多模态脑影像相互融合；然后，为明确源信号与sEEG和iEEG的关系，基于临界元方法求解传导矩阵，再基于最小范数估计方法创建结合sEEG与iEEG的正问题模型；最后，基于Tikhonov正则化方法求解逆问题，其中通过L曲线准则选取合适的正则化参数，估算出源信号后对其进行平滑与归一化处理，再通过源成像与阈值定位出癫痫灶。1. a method for locating epilepsy in children based on multimodal brain images, is characterized in that: its positioning method is: before creating a positive problem model based on iEEG and sEEG, the multimodal brain images need to be preprocessed first, The processing includes: since MRI and CT are acquired on different machines, they are redirected and resampled to make them uniform; in order to remove the EEG noise during iEEG and sEEG measurement, filtering and baseline correction are performed on them; There are differences in brain structure between children and children, so the boundary element based method and distributed source modeling method are used to create the head model and source model of personality; secondly, in order to combine iEEG and sEEG to create its positive problem model, it is necessary to combine sEEG, sEEG, Fusion of iEEG, MRI, and CT; registration and fusion of MRI and CT based on rigid body transformation method, and manual calibration of iEEG electrodes in MRI&CT to fuse iEEG and MRI&CT; MRI & CT fusion, so that multimodal brain images can be fused with each other; then, in order to clarify the relationship between the source signal and sEEG and iEEG, the conduction matrix is solved based on the critical element method, and the positive problem combining sEEG and iEEG is created based on the minimum norm estimation method. Finally, the inverse problem is solved based on the Tikhonov regularization method, in which the appropriate regularization parameters are selected through the L-curve criterion, the source signal is estimated and then smoothed and normalized, and then the epilepsy focus is located through source imaging and threshold. .

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