⏲️癫痫神经成像的计算分析:特征和方法综述

2016; 11: 515–529. 神经影像临床，2016; 11:515-529。2016年2月23日在线发布 doi: 10.1016/j.nicl. 2016.02.013Abstract摘要

Epilepsy affects 65 million people worldwide, a third of whom have seizures that are resistant to anti-epileptic medications. Some of these patients may be amenable to surgical therapy or treatment with implantable devices, but this usually requires delineation of discrete structural or functional lesion(s), which is challenging in a large percentage of these patients.

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全世界有6500万人患有癫痫，其中三分之一的癫痫发作对抗癫痫药物具有抗药性。其中一些患者可能愿意接受外科疗法或植入式装置的治疗，但这通常需要描绘离散的结构或机能性损害，这在很大比例的患者中是具有挑战性的。

Advances in neuroimaging and machine learning allow semi-automated detection of malformations of cortical development (MCDs), a common cause of drug resistant epilepsy. A frequently asked question in the field is what techniques currently exist to assist radiologists in identifying these lesions, especially subtle forms of MCDs such as focal cortical dysplasia (FCD) Type I and low grade glial tumors. Below we introduce some of the common lesions encountered in patients with epilepsy and the common imaging findings that radiologists look for in these patients. We then review and discuss the computational techniques introduced over the past 10years for quantifying and automatically detecting these imaging findings. Due to large variations in the accuracy and implementation of these studies, specific techniques are traditionally used at individual centers, often guided by local expertise, as well as selection bias introduced by the varying prevalence of specific patient populations in different epilepsy centers. We discuss the need for a multi-institutional study that combines features from different imaging modalities as well as computational techniques to definitively assess the utility of specific automated approaches to epilepsy imaging. We conclude that sharing and comparing these different computational techniques through a common data platform provides an opportunity to rigorously test and compare the accuracy of these tools across different patient populations and geographical locations. We propose that these kinds of tools, quantitative imaging analysis methods and open data platforms for aggregating and sharing data and algorithms, can play a vital role in reducing the cost of care, the risks of invasive treatments, and improve overall outcomes for patients with epilepsy.

神经影像学和机器学习的进步使得半自动检测皮层发育畸形(MCD)成为可能，这是耐药性癫痫的常见原因。该领域经常被问到的一个问题是，目前有哪些技术可以帮助放射科医师鉴别这些病变，特别是微妙形式的 MCD，如局灶性皮质发育不良(FCD) I 型和低级别胶质瘤。下面我们介绍一些癫痫患者常见的病变，以及放射科医生在这些患者中寻找的常见成像。然后，我们回顾和讨论过去10年中为量化和自动检测这些成像结果而引入的计算技术。由于这些研究的准确性和实施方面的巨大差异，传统上在个别中心使用特定的技术，通常由当地的专业知识指导，以及由不同癫痫中心特定患者群体的不同流行率引入的选择偏倚。我们讨论需要一个多机构研究，结合不同的成像模式和计算技术的特点，以明确评估特定的自动化方法对癫痫成像的效用。我们的结论是，通过一个共同的数据平台共享和比较这些不同的计算技术提供了一个机会，严格测试和比较这些工具在不同患者人群和地理位置的准确性。我们建议，这些工具、定量成像分析方法和用于聚集和共享数据和算法的开放数据平台，可以在降低护理成本、侵入性治疗风险和改善癫痫患者的整体结果方面发挥重要作用。

Keywords: Multimodal neuroimaging, Epilepsy, Drug resistant epilepsy, Focal cortical dysplasia, Malformations of cortical development, Machine learning关键词: 多模式神经影像学，癫痫，耐药性癫痫，局灶性皮质发育不良，皮质发育畸形，机器学习Abbreviations: FCD, focal cortical dysplasia; GM, gray matter; WM, white matter; T1W, T1-weighted MRI; T2W, T2-weighted MRI; DRE, drug resistant epilepsy; FLAIR, fluid-attenuated inversion recovery; VBM, voxel-based morphometry; SBM, surface-based morphometry; DWI, diffusion weighted imaging; DTI, diffusion tensor imaging; PET, positron emission tomography; GW, gray-white junction; HARDI, high angular resolution diffusion imaging; MEG, magnetoencephalography; MRS, magnetic resonance spectroscopy imaging; PNH, periventricular nodular heterotopia缩写: FCD，局灶性皮质发育不良; GM，灰质; WM，白质; T1W，T1加权 MRI; T2W，T2加权 MRI; DRE，抗药性癫痫; FLAIR，液体衰减反转恢复; VBM，基于体素的形态测定法; SBM，基于表面的形态测定法; DWI，弥散加权成像; DTI，弥散张量成像; PET，正电子发射计算机断层扫描;Go to:浏览:1. Introduction1. 引言

Epilepsy affects 65 million people in the world and has been estimated to cost the US upwards of $12.5 billion annually, based on a 1995 epidemiology study (Schachter, 2015, Kwan et al., 2011, Begley et al., 2000). Patients with drug resistant epilepsy (DRE) account for only 20–40% of patients with epilepsy but contribute a large portion of the epilepsy-associated cost due to risk of premature death, seizure-related injuries, psychosocial dysfunction and general reduction in quality of life measures (Kwan et al., 2011).

根据1995年的一项流行病学研究(Schachter，2015，Kwan et al。 ，2011，Begley et al。 ，2000) ，全世界有6500万人受癫痫影响，估计美国每年要花费125亿美元。耐药性癫痫(DRE)患者仅占癫痫患者的20-40% ，但由于过早死亡，癫痫相关伤害，社会心理功能不良和生活质量普遍下降的风险，占癫痫相关成本的很大一部分(Kwan et al。 ，2011)。

Resective surgical therapy has been the mainstay of therapy, but surgical candidacy depends on the clinical team"s ability to identify and fully delineate structural and functional lesions, such as regions of dysplastic cortex. Overall, the odds of seizure freedom after surgery for epilepsy are 2–3 times higher in cases that exhibit an identifiable lesion on histopathology or MRI (Téllez-Zenteno et al., 2010). Thus, the overall goal of neuroimaging in epilepsy is to monitor therapy and identify biomarkers of disease, candidates for surgery, and predictors of post-surgical outcomes (Bernasconi and Bernasconi, 2014).

切除外科疗法一直是治疗的主要手段，但是手术候选人取决于临床团队识别和完整描述结构性和功能性病变的能力，例如发育不良皮层区域。总体而言，癫痫手术后癫痫自由发作的几率是在组织病理学或 MRI 上显示可识别病变的病例的2-3倍(Téllez-Zenteno et al。 ，2010)。因此，癫痫神经影像学的总体目标是监测治疗并确定疾病的生物标志物，手术候选者和术后结局的预测因子(Bernasconi 和 Bernasconi，2014)。

Currently, the gold standard for outlining lesions in epilepsy patients is through identifying the epileptogenic zone, defined as the region recruited to seize on EEG, either measured on the scalp or in conjunction with invasive intracranial monitoring utilizing subdural strips, grids, depth or stereo EEG electrodes (Najm et al., 2002). The irritative zone is defined as the region near the structural or functional lesion that generates interictal epileptiform discharges identified by ECoG and fMRI (Koepp and Woermann, 2005). In these cases, the location of the epileptogenic zone, determined by electrophysiology, is compared with the irritative zone, determined by possible lesions discovered on imaging, to guide therapy. A majority of these are caused by malformations of cortical development.

目前，描述癫痫患者病变的黄金标准是通过识别致癫痫区，定义为在头皮上测量或结合使用硬膜下条带、栅格、深度或立体脑电图电极进行侵入性颅内监测(Najm et al。 ，2002)。刺激性区域被定义为靠近结构或机能性损害的区域，通过 ECoG 和 fMRI (Koepp 和 Woermann，2005)鉴定，该区域产生发作间期癫痫样放电。在这些病例中，致癫痫区的位置(由电生理学决定)与刺激区(由成像上可能发现的病变决定)进行比较，以指导治疗。其中大部分是由皮质发育畸形引起的。

1.1. Malformations of cortical development1.1皮质发育畸形

Malformations of cortical development (MCD), which describe a variety of structural and metabolic abnormalities of brain arising during gestation, were traditionally thought to cause a significant proportion of epilepsy (~15%) (Sisodiya, 2000, Lerner et al., 2009). Some lesions remain undetected, even at high resolution MRI, and are only discovered on histopathology after resective surgery (Sisodiya, 2000). As a result, previous estimates of the incidence of MCD have been low, and now at least 25% of all cases are thought to be due to MCD lesions. Histopathology of resected lesions show that these are mostly focal cortical dysplasias (45%), gliosis (22%), and hippocampal sclerosis (13%) (Wang et al., 2013).

皮质发育畸形(mCD)描述了妊娠期间出现的各种结构和代谢脑异常，传统上被认为引起相当比例的癫痫(约15%)(Sisodiya，2000，Lerner 等，2009)。一些病变仍然未被发现，甚至在高分辨磁共振成像中也是如此，只有在切除手术后的组织病理学才能被发现(Sisodiya，2000)。因此，以前对 MCD 发病率的估计很低，现在至少有25% 的病例被认为是由 MCD 病变引起的。切除病灶的组织病理学显示主要为局灶性皮质发育不良(45%)、胶质增生(22%)及海马硬化(13%)(Wang et al。 ，2013)。

Table 1 shows the distribution of malformations of cortical development and their incidence. Few studies have looked at the incidence of the different possible malformations, but focal cortical dysplasias is considered to account for the majority of the cases (Wang et al., 2013, Raymond et al., 1995). Focal cortical dysplasias (FCD) are a heterogeneous group of disorders that have are classified in three tiers: FCD type I, FCD type II and FCD type III. FCD type I is caused by abnormal neuronal migration, FCD type II is caused by abnormal neural proliferation or apoptosis, and FCD type III are dysplasias associated with hippocampal sclerosis, vascular malformations (1–2%), tumors (10%) and other principal lesions (Barkovich et al., 2012, Jackson and Badawy, 2011). These tiers are listed in order of how readily they are visualized on imaging. For instance, FCD type II, which histopathologically resemble tuberous sclerosis lesions (Kumar et al., 2011), are more easily visible on conventional MRI imaging compared to the milder FCD type I (Krsek et al., 2008). T1-weighted imaging (T1WI) is abnormal in FCD type II showing altered cortical thickness and gray-white junction blurring. Fig. 1 shows an example MRI from a patient with type II FCD. For the other types of FCDs, structural MRI is insufficient to diagnose a substantial proportion cortical dysplasias, particularly those associated with FCD type I (Hauptman and Mathern, 2012). Even within FCD type II, MRI features may be insufficient to detect more specific histopathological subforms, such as FCD type IIA (Colombo et al., 2012). This makes delineating these lesions difficult.

表1显示了皮质发育畸形的分布及其发生率。很少有研究关注不同可能的畸形的发生率，但局灶性皮质发育不良被认为是大多数病例的原因(Wang et al。 ，2013，Raymond et al。 ，1995)。局灶性皮质发育不良(FCD)是一组异质性疾病，分为三个层次: FCD I 型，FCD II 型和 FCD III 型。1型是由神经元异常迁移引起，2型是由神经元异常增殖或凋亡引起，3型是与海马硬化、血管畸形(1-2%)、肿瘤(10%)及其他主要病变相关的发育异常(Barkovich 等，2012，Jackson and Badawy，2011)。这些层次是按照它们在成像上的可视化程度排列的。例如，组织病理学类似于结节性硬化症病变的 fCD II 型(Kumar et al。 ，2011)在常规 MRI 成像上比较轻微的 fCD I 型(Krsek et al。 ，2008)更容易看到。T1加权成像(T1WI)在 FCD II 型中呈现异常，表现为皮质厚度改变和灰白色连接模糊。图1显示了来自 II 型 FCD 患者的 MRI 示例。对于其他类型的 FCD，结构性核磁共振成像不足以诊断相当比例的皮层发育不良，特别是那些与第一型 FCD 相关的(豪普特曼和 Mathern，2012)。即使在 FCD II 型中，MRI 特征也可能不足以检测更具体的组织病理学亚型，如 FCD IIA 型(Colombo 等，2012)。这使得描绘这些病变变得困难。

100">Fig. 1图1

Sample T1-weighted (left) and T2-weighted (right) axial MRI images taken from a 21-year old male epilepsy patient. The focal cortical dysplasia (red arrows) present as loss of gray-white contrast on T1-weighted imaging as well as a hyperintensity on T2-weighted imaging.

样本 T1加权(左)和 T2加权(右)轴向 MRI 图像采取的21岁男性癫痫患者。局灶性皮质发育不良(红色箭头)表现为 T1加权成像灰白对比度的丧失以及 T2加权成像的高信号。

Table 1表1

Incidence of different malformations of cortical development organized by groupings (Barkovich et al., 2012). Group 1 includes malformations due to abnormal cell proliferation, Group 2 includes malformations due to abnormal cell proliferation, and Group 3 includes malformations due to abnormal cortical organization. These incidence data are adapted from Papayannis et al. (2012).

不同皮质发育畸形的发生率(Barkovich 等，2012)。第一组包括由于细胞增殖异常引起的畸形，第二组包括由于细胞增殖异常引起的畸形，第三组包括由于皮质组织异常引起的畸形。这些发病率数据改编自 Papayannis 等人(2012)。

Group I (49%)第一组(49%)
Focal cortical dysplasia (Type I and II)局灶性皮质发育不良(I 型和 II 型)	48%
Focal cortical dysplasia+glioneural tumors局灶性皮质发育不良 + 神经胶质瘤	14%
Dual or triple pathology: focal cortical dysplasia+tumors+hippocampal sclerosis双重或三重病理: 局灶性皮质发育不良 + 肿瘤 + 海马硬化	14%
Glioneural tumors神经胶质瘤	10%
Tuberous sclerosis结节性硬化症	10%
Hemimegalencephaly半巨脑畸形	1%
Focal hemimegalencephaly versus possible focal cortical dysplasia局灶性半巨脑畸形与可能的局灶性皮质发育不良	3%
Group II (40%)第二组(40%)
Periventricular nodular heterotopia脑室周围结节性异位	55%
Subcortical heterotopia皮质下异位症	18%
Mixed forms of heterotopia混合形式的异托邦	10%
Dual pathology: periventricular nodular heterotopia+hippocampal sclerosis双重病理: 脑室周围结节性异位 + 海马硬化	13%
Double cortex or subcortical band heterotopia双皮层或皮层下带异位症	5%
Group III (11%)第三组(11%)
Schizencephaly脑裂畸形	37%
Polymicrogyria (bilateral)多小脑回(双侧)	26%
Polymicrogyria (unilateral)多小脑回(单侧)	37%

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There is strong evidence suggesting that patients with a focal lesion on MRI have a better outcome after epilepsy surgery compared to patients with no abnormal findings on MRI. When complete resections are performed on patients with cortical dysplasias, 80% of the patients become seizure-free compared to 20–50% in patients with incomplete resections performed due to lack of imaging findings or involvement of eloquent cortex (Lerner et al., 2009). The key to reducing the extent of resection while optimizing clinical outcomes, including minimizing side effects of surgery, is accurately mapping the brain region to be resected and minimizing the resection of eloquent or uninvolved regions (Okonma et al., 2011). A different set of studies of patients with FCDs and low grade neuroglial tumors showed that determining the extent of the lesion is important, since its complete removal correlates with good surgical outcome (83% seizure-free (Engel class I) outcome) (Chassoux and Daumas-Duport, 2013, Cossu et al., 2013, Rowland et al., 2012). This target region for resection can be defined by electrocorticography findings, neuroimaging & computational identification of abnormalities (Begley et al., 2000), histology and (Téllez-Zenteno et al., 2010) metabolic imaging in cases where structural imaging is normal (Okonma et al., 2011). Function of these targets can be difficult to assess a-priori. Patient risk of deficits from surgery is minimized by electrical stimulation testing, functional imaging, and advanced structural imaging (such as HARDI-DTI) though these techniques may not adequately image fibers of passage through the zone of resection (Okonma et al., 2011). Thus, it is important for current imaging and computational techniques to identify, assess and delineate epileptogenic lesions.

有强有力的证据表明，MRI 检查有局灶性损害的患者在癫痫手术后比 MRI 检查无异常的患者有更好的预后。当对皮层发育不良患者进行完全切除时，80% 的患者无癫痫发作，而由于缺乏成像发现或皮层受累而进行不完全切除的患者为20-50% (Lerner et al。 ，2009)。在减少手术切除范围的同时，减少临床结果(包括尽量减少手术的副作用)的关键是精确绘制需要切除的脑区域，并尽量减少切除雄辩或未涉及的脑区域(Okonma et al。 ，2011)。对 FCD 和低级别神经胶质瘤患者的一组不同的研究表明，确定病变的程度是重要的，因为其完全切除与良好的手术结果(83% 无癫痫发作(Engel I 类)结果)相关(Chassoux 和 Daumas-Duport，2013，Cossu 等，2013，Rowland 等，2012)。这个切除的目标区域可以通过皮质电描记法发现，神经影像学和异常的计算识别(Begley 等，2000) ，组织学和(Téllez-Zenteno 等，2010)代谢成像在结构成像正常的情况下(Okonma 等，2011)。这些目标的功能可能难以事先评估。通过电刺激测试，功能性成像和高级结构性成像(如 HARDI-dTI) ，手术缺陷的患者风险降至最低，尽管这些技术可能不足以成像通过切除区的纤维(Okonma 等，2011)。因此，对于目前的成像和计算技术来说，识别、评估和描绘癫痫病变是非常重要的。

Despite recent improvements in imaging technology and computational methods (Madan and Grant, 2009), the ability to detect focal lesions has significant room for improvement. MRI findings are abnormal in only 50–70% of patients with MCD. Some modalities such as PET imaging become sensitive only when fused with MRI (Lerner et al., 2009). In addition, re-examination of MRI images demonstrated lesions that were missed during initial interpretation in some cases. This highlights the urgent need for advances in imaging and computational techniques that can detect subtle epileptic pathologies in MRI-negative epilepsies (Rosenow and Lüders, 2001). Standard clinical imaging protocol is limited in its efficacy.

尽管最近成像技术和计算方法有所改进(Madan 和 Grant，2009) ，但是检测局灶性病变的能力还有很大的改进空间。只有50-70% 的 MCD 患者的 MRI 表现异常。一些模式如 PET 成像只有在与 MRI 融合时才变得敏感(Lerner et al。 ，2009)。此外，重新检查的 MRI 图像显示，病变是在最初的解释错过了一些情况下。这突出表明，迫切需要在成像和计算技术方面取得进展，以检测核磁共振阴性癫痫的细微癫痫病理(Rosenow and Lüders，2001)。标准的临床成像疗法效果有限。

In the next two sections, we discuss important radiological features and the latest computational techniques used to identify and delineate lesions in patients with epilepsy.

在接下来的两个部分，我们讨论重要的放射学特征和最新的计算技术用于确定和描绘病变的癫痫患者。

Go to:浏览:2. What are features radiologists look for in imaging?2. 放射科医生在成像中寻找什么特征？

Neuroradiologists look for certain distinct image biomarkers in order to diagnose the focal lesion contributing to a patient"s refractory epilepsy. Most epilepsy centers use an imaging protocol, typically involving fluid attenuated inversion recovery (FLAIR), T2W, T1W, and hemosiderin/calcification-sensitive sequences. The T1W image should be acquired in three-dimensional technique at 1mm isotropic voxels size. For T2W and FLAIR, at least two slice orientations are needed to image at an angulation perpendicular to the long axis of the hippocampus (Wellmer et al., 2013). Slice thickness for T2W and FLAIR must not exceed 3mm in order to best visualize this angulation. In addition, some institutions use different imaging modalities such as FDG-PET, SPECT of ictal-interictal cerebral blood flow (SISCOM), and MEG to isolate the focus of the seizure. Higher field imaging can also improve detection of key image findings in MCD (Mellerio et al., 2014a). This section gives an overview of these different imaging modalities and image used in the diagnosis of MCD.

神经放射学家寻找某些独特的图像生物标志物，以诊断导致患者难治性癫痫的局灶性损害。大多数癫痫中心使用成像治疗方案，通常涉及液体衰减反转恢复(FLAIR)、 T2W、 T1W 和含铁血黄素/钙化敏感序列。T1W 图像应采用三维成像技术，各向同性体素尺寸为1mm。对于 T2W 和 FLAIR，至少需要两个切片方向来成像垂直于海马长轴的成角(Wellmer et al。 ，2013)。T2W 和 FLAIR 的膜厚度不能超过3毫米，以便最好地显示这个角度。此外，一些机构使用不同的成像模式，如 FDG-PET、发作间期分散观测脑血流量(SISCOM)和 MEG 来分离癫痫发作的焦点。更高的视场成像还可以改善对 MCD 中关键图像结果的检测(Mellerio et al。 ，2014a)。本节概述了这些不同的成像形式和图像用于诊断多发性硬化症。

2.1. T1W/T2W imaging2.1. T1W/T2W 成像

Neuroradiologists look for the following common findings to assist them in the diagnosis of MCD in epilepsy patients:

神经放射科医生寻找以下共同的发现，以帮助他们诊断癫痫患者的 MCD:

·Abnormal signal hyperintensity/hypointensity异常信号高信号/低信号·Subcortical presence of abnormal gray matter (see Fig. 2)皮质下出现异常灰质(见图2)100">Fig. 2图2

Sample T1-weighted (left) and T2-weighted (right) axial and sagittal images taken from a patient with a smaller right hemisphere and periventricular nodular heterotopia (red arrow). Note that the heterotopia is located on the temporal horn and has subcortical abnormal gray matter in areas where usually only white matter is found.

样本 T1加权(左)和 T2加权(右)轴位和矢状位图像采取的病人较小的右半球和室周结节性异位症(红色箭头)。注意异位位于颞角，在通常只有灰质白质的区域有皮质下异常。

·Increased cortical thickness/pseudo-thickness皮层厚度增加/伪厚度·Gray-white junction blurring灰白色模糊·Sulcal abnormalities such as increased sulcal depth牙龈异常，如牙龈深度增加·Structural/lobar atrophy – this includes hippocampal sclerosis and atrophy结构/叶萎缩-包括海马硬化和萎缩·Abnormal gyrification pattern异常旋转模式·Abnormal asymmetry in structural patterns结构图案中不正常的不对称性·Diffuse or multifocal occurrence of any of the above features上述任何一种特征的弥漫性或多焦点发生

In MRI images of patients with cortical dysplasia, 60% will show thickening or pseudo-thickening of gray matter, 74% will show blurring of the gray-white junction, 63% will have increased T2W signal in white matter, 19% will have structural atrophy visible, and 34% will have other signal changes (Lerner et al., 2009). These changes include the transmantle sign, which is a hyperintensity (on T2-weighted images) of subcortical white matter often tapering towards the ventricle (Colombo et al., 2003). These features occur together and make the lesion more visible. A combination of cortical thickening, gray-white junction blur, and transmantle sign were found in 64% of patients with FCD type II (Mellerio et al., 2012). In fact, there is a significant association between the presence of cortical thickening (p=0.002) and the “transmantle sign” (p<0.001) and a correct MRI diagnosis (Colombo et al., 2012).

在皮质发育不良患者的 MRI 图像中，60% 显示灰质增厚或假性增厚，74% 显示灰白色连接模糊，63% 显示白质 T2W 信号增强，19% 显示结构性萎缩，34% 显示其他信号改变(Lerner 等，2009)。这些变化包括跨外套征，这是一种皮质下白质的高信号(在 T2加权图像上) ，通常逐渐向心室逐渐减弱(colmbo et al。 ，2003)。这些特征同时出现，使病变更加明显。64% 的 FCD II 型患者出现皮质增厚、灰白色连接模糊和跨套膜征(Mellerio et al。 ，2012)。事实上，皮质增厚(p = 0.002)和“跨套征”(p < 0.001)与正确的 MRI 诊断之间存在显着相关性(Colombo 等，2012)。

FCD type I lesions, on the other hand, tend to have regional reduction of the white matter (Krsek et al., 2009). For instance, lobar hypoplasia/atrophy are reported to be most frequent (Freeman et al., 2004). Mild blurring at the GM (gray matter)/WM (white matter) junction with normal cortical thickness and abnormal gyral/sulcal patterns can also be present (Krsek et al., 2008).

另一方面，FCD I 型病变往往会导致白质局部减少(Krsek et al。 ，2009)。例如，肺叶发育不良/萎缩据报道是最常见的(Freeman et al。 ，2004)。GM (灰质)/WM (白质)交界处的轻度模糊，皮质厚度正常，脑回/脑沟图案异常(Krsek et al。 ，2008)。

Some FCDs can have abnormal sulcal patterns, such as a cleft dimple with CSF or an unusual central sulcal pattern that resembles a “power” button symbol (Bronen et al., 2000, Mellerio et al., 2014b). These lesions have been shown to be located near deep sulci where the mean and maximum depth of sulci is higher than that of the corresponding sulci in controls (Besson et al., 2008a). These deep sulcal dysplasias often have a positive transmantle sign, hyperintensity on T2/FLAIR, and abnormal gyral patterns (Hofman et al., 2011).

一些 FCD 可能具有异常的沟槽图案，例如脑脊液裂窝或类似于“电源”按钮符号的不寻常的中央沟槽图案(Bronen 等，2000，Mellerio 等，2014b)。这些病变已被证明位于靠近深沟的位置，其中沟的平均和最大深度高于对照组中相应的沟的深度(Besson 等，2008a)。这些深沟发育不良通常具有正的跨套膜征，T2/FLAIR 高信号和异常的脑回模式(Hofman 等，2011)。

Sometimes, they can present as a multifocal occurrence (Fauser et al., 2009). Neuroradiologists aim to identify any abnormal image findings in all regions of the cortex and subcortical volumes. New imaging sequences such as double inversion recovery and MP2RAGE have been proposed to reduce signal from CSF and provide high T1 weighting, allowing for improved contrast in the cortex and detection of subtle lesions (Rugg-Gunn et al., 2006, Winston et al., 2014, Pardoe and Kuzniecky, 2014).

有时，它们可以表现为多焦点发生(Fauser et al。 ，2009)。神经放射学家的目标是确定任何异常的影像发现，在所有区域的皮质和皮质下体积。已经提出了新的成像序列，如双反转恢复和 MP2RAGE，以减少来自 CSF 的信号并提供高的 T1加权，允许改善皮层的对比度和检测微小病变(Rugg-Gunn 等，2006，Winston 等，2014，Pardoe 和 Kuzniecky，2014)。

These radiological features manifest because of the way tissue microstructure is affected by MCD. In FCD type II, these microstructural abnormalities include neuronal hypertrophy with compromised cell motility (Thom et al., 2005), presence of immature balloon cells, and pathologic myelin arborization (Shultz et al., 2014). In addition, pathophysiologic mechanisms such as gliosis and edema manifest as hyperintensities on T1WI and T2WI (Shultz et al., 2014). These features have been validated through histopathological studies of neuronal density, count, and aberrant neuronal patterns that contributed to gray-white blurring (Mühlebner et al., 2012). Some of the other MCDs, such as periventricular nodular heterotopia (PNH), have similar histopathological findings but present with gray matter signal in CSF and white matter regions on imaging.

这些放射学特征的表现是由于组织微观结构受到 MCD 的影响。在 FCD II 型中，这些显微结构异常包括细胞运动受损的神经元肥大(Thom et al。 ，2005) ，未成熟气球样细胞的存在和病理性髓鞘树枝化(Shultz et al。 ，2014)。此外，神经胶质增生和浮肿增生等病理生理机制在 T1WI 和 T2WI 上表现为高信号(Shultz et al。 ，2014)。这些特征已经通过神经元密度、计数和导致灰白色模糊的异常神经元模式的组织病理学研究得到验证(Mühlebner 等，2012)。其他一些 MCD，如脑室周围结节性异位症(PNH) ，也有类似的组织病理学表现，但在脑脊液和灰质白质区域存在成像信号。

Neuroglial tumors such as DNETs often have different imaging findings: the lesion is supratentorial, often well demarcated, and usually found in the temporal or frontal lobe (Velez-Ruiz and Klein, 2012). The characteristic MRI finding is increased signal on T2WI and decreased signal on T1WI. Contrast enhancement has been described in up to one-third of patients and is often located adjacent to a FCD (Velez-Ruiz and Klein, 2012). Certain subtypes often have cystic-liked appearance, are well-delineated, and are strongly hypointense on T1 (Chassoux and Daumas-Duport, 2013).

神经胶质细胞肿瘤如 dNETs 通常有不同的成像表现: 病灶位于幕上，分界清晰，通常发现于颞叶或额叶(Velez-Ruiz and Klein，2012)。特征性 MRI 表现为 T2WI 信号增强，T1WI 信号减弱。多达三分之一的患者已经描述了对比度增强，并且通常位于 FCD 附近(Velez-Ruiz and Klein，2012)。某些亚型通常具有囊状外观，轮廓清晰，在 T1上强烈低信号(Chassoux 和 Daumas-Duport，2013)。

Despite this extensive list of findings, MRI is only moderately sensitive in detecting these lesions regardless of histopathological subtype. No MRI abnormalities are found in 31–41% of patients with FCD type I and 21–33% of patients with FCD type II (Velez-Ruiz and Klein, 2012). In a retrospective study of all histopathologically confirmed FCDs at a regional pediatric comprehensive epilepsy center, the majority of cases (58%) did not have any MRI abnormalities highlighting the need for better imaging techniques (Leach et al., 2014).

尽管有这些广泛的发现，但是无论组织病理学亚型如何，核磁共振成像对于检测这些病变都只是中度敏感。在31-41% 的 FCD I 型患者和21-33% 的 FCD II 型患者中没有发现 MRI 异常(Velez-Ruiz and Klein，2012)。在一项对区域儿科综合癫痫中心所有经组织病理学证实的 fcd 的回顾性研究中，大多数病例(58%)没有任何 MRI 异常，突出表明需要更好的成像技术(Leach et al。 ，2014)。

2.2. Electrophysiology2.2电生理学

Electrophysiology is the current gold standard for isolating the epileptic networks and the seizure onset zone(s) during presurgical evaluation, especially invasive electrocorticography through the use of subdurally placed electrodes. This, in combination with imaging, is used to determine the resection zone. Additional studies such as magnetoencephalography (MEG) may be used to localize the epileptogenic zone, focusing primarily on interictal epileptiform activity, and to map eloquent cortex (Widjaja et al., 2008). In one study the surgical outcome following complete removal of areas containing clustered MEG sources and MR lesions were same, indicating MEG was approximately equally sensitive to MRI (Wilenius et al., 2013). It is important to note that the epileptic network, as defined by electrophysiology, may not exactly correspond to lesions on MRI, but also involve their microstructure and interface with more normal brain tissue. These border zones have been identified, by intracranial electrophysiology, to be at the border or just outside of the border of lesions defined by imaging, where microscopic pathology is often present (Mukae et al., 2014).

电生理学是目前在术前评估中隔离癫痫网络和癫痫发作区域的黄金标准，尤其是通过使用硬膜下放置的电极进行侵入性皮质电描记法。这个和成像一起用来确定切除区域。额外的研究，如脑磁波描记法(MEG)可以用来定位致癫痫区，主要集中在发作间期癫痫样活动，并绘制雄辩皮层(Widjaja et al。 ，2008)。在一项研究中，完全切除包含 MEG 来源和 MR 病变的区域后的手术结果是相同的，表明 MEG 对 MRI 几乎同样敏感(Wilenius 等，2013)。值得注意的是，由癫痫电生理学所定义的神经网络可能并不完全对应于 MRI 上的病变，但是也涉及到它们的微观结构和与更多正常脑组织的接口。通过颅内电生理学，已经确定这些边界区域位于由成像定义的病变的边界或刚好在边界之外，其中常常存在显微病理学(Mukae et al。 ，2014)。

2.3. PET imaging2.3 PET 成像

Positron emission tomography (PET) uses radiotracers to identify pathological metabolic and neuroinflammatory processes (Shultz et al., 2014). Local decrease and increase in radiotracer uptake are potential biomarkers for these processes. The most common radiotracer that is used is (Okonma et al., 2011)FDG-PET, though other radiotracers have been shown to be useful in imaging inflammation ((11C)-PK11195 PET (Butler et al., 2013)), abnormal metabolism of amino acids (Alpha-[11C]methyl-L-tryptophan (Natsume et al., 2008)) and epileptogenic tubers (CAMt (Asano et al., 2000, Kumar et al., 2011)). Cerebral glucose hypometabolism is associated with mitochondrial dysfunction in intractable epileptic patients (Tenney et al., 2014).

正电子发射计算机断层扫描(PET)使用放射性示踪剂来识别病理代谢和神经炎症过程(Shultz et al。 ，2014)。局部摄取放射示踪剂的减少和增加是这些过程的潜在生物标志物。使用的最常见的放射示踪剂是(Okonma 等，2011) FDG-PET，尽管其他放射性示踪剂已被证明在成像炎症((11C)-PK11195 PET (Butler 等，2013)) ，氨基酸异常代谢(α-[11C ]甲基-L-色氨酸(夏目漱石等，2008))和致痫块茎(CAMt (Asano 等，2000，Kumar 等，2011))。脑葡萄糖低代谢与难治性功能不良患者的线粒体癫痫有关(Tenney et al。 ，2014)。

Many studies have shown that FDG-PET/MRI coregistration improves detection of cortical dysplasia in patients, especially those where the MRI is read as normal (Salamon et al., 2008). FCDs visualized on MRI usually coincide with the epileptogenic zone but the full extent is not visible. This can be resolved by fusing together PET images with MRI (Colombo et al., 2009). The presence of regional interictal hypometabolism can help radiologists guide their search for a lesion (Velez-Ruiz and Klein, 2012). It is important to note that FDG-PET can show normal or even hypermetabolic regions corresponding to FCD for unclear reasons. This has been attributed by some investigators to localized epileptiform activity (Colombo et al., 2009).

许多研究表明，FDG-PET/MRI 共同配准改善了患者皮质发育不良的检测，特别是那些 MRI 读作正常的患者(Salamon 等，2008)。磁共振成像显示的 fcd 通常与致癫痫区相吻合，但完整范围不可见。这可以通过将 PET 图像与 MRI 结合来解决(Colombo et al。 ，2009)。区域性发作间低代谢的存在可以帮助放射科医师指导他们寻找病变(Velez-Ruiz and Klein，2012)。值得注意的是，FDG-PET 可以显示正常甚至高代谢区域对应于 FCD 的原因不明。这被一些研究者归因于局部癫痫样活动(科伦坡等，2009)。

2.4. Cerebral blood flow imaging2.4脑血流量成像

Another standard imaging modality is SPECT imaging, which measures cerebral blood flow during ictal and interictal periods. The difference of these blood flow maps, termed SISCOM when co-registered to MRI, is used to isolate the seizure onset zone. Studies have shown that complete resection of onset regions identified by SISCOM is at least as good as MRI and EEG in terms postsurgical outcome, though this requires rapid injection of tracer proximate to seizure onset and focal uptake of the SPECT tracer (Krsek et al., 2013). This technique is variably employed in academic centers due to the cost and technical challenges of having the radioactive tracer on hand waiting for spontaneous seizure onset in inpatient epilepsy monitoring units.

另一个标准显象方式是分散观测成像，它测量发作期和间歇期的脑血流量。这些血流量图的差异，当与 MRI 共同注册时称为 SISCOM，用于分离癫痫发作区域。研究表明，SISCOM 确定的发病区域的完全切除至少在术后结果方面与 MRI 和 EEG 一样好，尽管这需要快速注射临近癫痫发作的示踪剂和分散观测示踪剂的局灶性摄取(Krsek 等，2013)。由于成本和技术上的挑战，这种技术在学术中心的应用各不相同，因为在放射性示踪剂癫痫监测住院病人中，让病人在手边等待自发性癫痫发作的发作。

Some institutions also use arterial spin labeling (ASL) imaging fused with 3D FLAIR sequences in order to measure relative cerebral blood flow in regions of interest and their contralateral counterparts. This helps demarcate primary lesions such as glial tumors and vascular malformations associated with FCDs, though perfusion abnormalities are not always consistent across patient groups, especially those with mesial temporal sclerosis (Toledo et al., 2013). More generally, interictal ASL findings in focal epilepsy resemble those of PET; that is, focal hypoperfusion in ASL resemble hypometabolism on PET (Toledo et al., 2013).

一些研究机构也使用动脉自旋标记(ASL)成像与3 d fLAIR 序列融合，以测量感兴趣区域及其对侧对应区域的相对脑血流量。这有助于划分与 FCD 相关的原发性病变，如神经胶质瘤和血管畸形，尽管灌注异常并不总是在患者组之间一致，特别是那些内侧颞叶硬化症患者(Toledo 等，2013)。更一般地说，局灶性癫痫的发作间 ASL 表现类似于 PET; 也就是说，ASL 的局灶性低灌注类似于 PET 上的低代谢(Toledo 等，2013)。

2.5. Diffusion imaging2.5扩散成像

Unlike conventional structural MR imaging that provides images resulting from magnetic relaxation parameters, diffusion imaging (DWI and DTI) provides contrast images based upon the extent, directionality and organization of the motion of free (unbound) water (Colombo et al., 2009). Malformations of cortical development often affect the microstructure of underlying white matter tracts (Colombo et al., 2009). In addition, presence of heterotopic neurons, abnormal myelination (myelin pallor on histology), edema, axonal injury and gliosis affect the diffusion properties of tracts originating from the lesion (Colombo et al., 2009, Shultz et al., 2014). Often, there is significant increase in perpendicular diffusivity, increased apparent diffusion coefficient and a significant reduction in anisotropy within the white matter near the lesion (Eriksson et al., 2001, Rugg-Gunn et al., 2001, Lee et al., 2004, Gross et al., 2005, de la Roque et al., 2005, Widjaja et al., 2007). There is also a decrease in the volume of white matter bundles, even in patients who have shown normal T2W MRI (Lee et al., 2004, Gross et al., 2005). These features can also be found when compared with the contralateral hemisphere (Princich et al., 2012).

与传统的结构 MR 成像不同，扩散成像(dWI 和 dTI)提供了基于自由(未结合)水运动的范围、方向和组织的对比图像(colbo et al。 ，2009)。皮质发育畸形通常会影响微观结构下白质束的形成(colmbo et al。 ，2009)。此外，异位神经元的存在，髓鞘形成异常(组织学上髓鞘苍白) ，浮肿，轴突损伤和胶质增生影响起源于病变的神经束的扩散特性(Colombo 等，2009，Shultz 等，2014)。通常情况下，垂直扩散率明显增加，表观扩散系数增加，病灶附近白质内的各向异性明显减少(Eriksson 等，2001，Rugg-Gunn 等，2001，Lee 等，2004，Gross 等，2005，de la Roque 等，2005，Widjaja 等，2007)。白质束的体积也有所减少，即使在显示正常 T2W MRI 的患者中也是如此(Lee et al。 ，2004，Gross et al。 ，2005)。与对侧半球相比，也可以发现这些特征(Princich et al。 ，2012)。

Newer diffusion imaging sequences have the potential to better characterize lesions. High angular resolution diffusion imaging (HARDI-DTI), which resolves multiple intravoxel fiber populations (Tuch et al., 2002, Behrens et al., 2003), can more accurately perform fiber mapping and allow for the evaluation of white matter abnormalities near lesions. Advanced methods such as NODDI can give maps of neuronal density, though few studies have looked at the accuracy of detecting epileptogenic lesions (Winston et al., 2014). Other diffusion techniques such as diffusion kurtosis imaging (DKI) provides improved GM–WM contrast, and is sensitive to changes in GM (unlike DTI, as the apparent diffusion coefficient in GM is essentially isotropic) (Feindel, 2013).

较新的扩散成像序列有可能更好地表征病变。高角分辨率扩散成像(HARDI-dTI)可以解析多个体内纤维群(Tuch et al。 ，2002，Behrens et al。 ，2003) ，可以更准确地进行纤维定位，并允许评估病变附近的白质异常。先进的方法，如 NODDI 可以提供神经元密度图，尽管很少有研究关注检测癫痫病变的准确性(Winston et al。 ，2014)。其他扩散技术，如扩散峰成像(DKI)提供了改善的 GM-wM 对比度，并对 GM 的变化敏感(不同于 dTI，因为 GM 的表观扩散系数基本上是各向同性的)(Feindel，2013)。

2.6. Functional imaging using MRI and EEG2.6使用核磁共振和脑电图进行功能性成像

Functional MRI methods image temporal changes in blood flow (blood-oxygenation-level-dependent [BOLD] contrast imaging) (Kwong et al., 1992). Clinically, EEG is acquired during fMRI acquisition, and the timed EEG events are used to simultaneously map or signal average BOLD changes (Pardoe and Kuzniecky, 2014). These events are usually interictal epileptiform discharges, or spikes, because of the difficult logistics associated with recording seizures and ictal events in the MRI scanner. Studies have shown that BOLD response in EEG-fMRI can help delineate the epileptogenic zone and can quantify network changes in the brain (Pardoe and Kuzniecky, 2014, Zijlmans et al., 2007). The sensitivity of these techniques have ranged from 55% - 88% (Pardoe and Kuzniecky, 2014, Moeller et al., 2009). There are extensive studies that have used EEG-fMRI to help with lateralization of seizure onset, detection of lesions as well as assessment of neurocognitive battery tests. A combination of EEG-fMRI with other imaging modalities can potentially uncover structural and functional lesions that are often missed on standard imaging alone.

功能性磁共振成像方法成像血流量的时间变化(血氧水平依赖[ BOLD ]造影成像)(邝等人，1992)。临床上，脑电图是在功能磁共振成像采集期间获得的，并且定时脑电图事件被用于同时映射或信号平均 BOLD 变化(Pardoe 和 Kuzniecky，2014)。这些事件通常是发作间期癫痫样放电，或尖峰，因为困难的后勤与记录癫痫发作和发作事件在 MRI 扫描仪。研究表明，脑电图-功能磁共振成像中的 BOLD 反应可以帮助描绘致癫痫区，并可以量化大脑中的网络变化(Pardoe 和 Kuzniecky，2014，Zijlmans 等，2007)。这些技术的灵敏度在55% -88% 之间(Pardoe 和 Kuzniecky，2014，Moeller 等，2009)。已经有大量的研究使用脑电图-功能磁共振成像来帮助侧枝化癫痫发作，检测病变以及评估神经认知电池测试。脑电图-功能磁共振成像结合其他成像可能会发现结构性和功能性病变，而这些病变仅在标准成像上经常被忽略。

2.7. Network analysis using functional (fMRI/EEG/MEG) and structural imaging (DTI)使用功能性(fMRI/EEG/MEG)和结构性成像(dTI)进行网络分析

Brain networks in epilepsy have been increasingly investigated in recent years. Network analysis characterizes the organization of brain networks, either structural or functional, and studies the evolution of these networks interictally, preictally and ictally (Bullmore and Sporns, 2009, Ponten et al., 2007, van Diessen et al., 2013). There are other reviews geared towards clinicians as well as network scientists that introduce this paradigm of research and discuss future applications (van Diessen et al., 2013). Different modalities of brain mapping, including DTI, fMRI, EEG and MEG are used to correlate network characteristics of patients with epilepsy, such as those with FCDs (Bandt et al., 2014, Holmes and Tucker, 2013, Jeong et al., 2014, Caciagli et al., 2014, Bernhardt et al., 2011, Guye et al., 2010, Pedersen et al., 2015, Thornton et al., 2011, Englot et al., 2015). These connectivity measures have promising potential as an adjunctive tool to aid in identifying the epileptogenic zone as well as the extent of lesions in epilepsy patients being considered for resective surgery (Weaver et al., 2013). The questions remain as to what constraints exist between functional and structural networks and how this interplay guides seizure initiation and propagation. More studies need to consider network characteristics of structural networks (DTI, cortical thickness or gray matter derived graphs), and correlate them to network characteristics of functional networks, regional and global, derived from ECoG and fMRI. Highly informative network features will serve as predictive biomarkers for surgery outcome to clinicians.

近年来，癫痫的脑网络研究越来越多。网络分析表征了大脑网络的结构或功能的组织，并研究了这些网络的间歇性，前期和后期的进化(Bullmore 和 Sporns，2009，Ponten 等，2007，van Diessen 等，2013)。还有其他针对临床医生以及网络科学家的评论，介绍了这种研究范式并讨论了未来的应用(van Diessen et al。 ，2013)。不同的脑定位图模式，包括 dTI，fMRI，EEG 和 MEG 被用来关联癫痫患者的网络特征，例如 FCD 患者(Bandt 等，2014，Holmes 和 Tucker，2013，Jeong 等，2014，Caciagli 等，2014，Bernhardt 等，2011，Guye 等，2010，Pedersen 等，2015，Thornton 等，2011，Englot 等，2015)。这些结合性措施作为辅助工具，在确定癫痫患者的致癫痫区和病变范围方面具有很大的潜力(Weaver et al。 ，2013)。问题仍然是功能性和结构性网络之间存在哪些约束，以及这种相互作用如何指导癫痫发作的起始和传播。更多的研究需要考虑结构网络的网络特征(DTI，皮层厚度或灰质衍生图) ，并将它们与源自 ECoG 和 fMRI 的区域和全球功能网络的网络特征相关联。高度信息化的网络特征将作为预测临床医生手术结果的生物标志物。

2.8. Other modalities: CEST/MTI/MRS2.8. 其他模式: CEST/MTI/MRS

Other imaging acquisition sequences have been studied in epilepsy patients but very few of them are used routinely for clinical purposes. Magnetic resonance proton spectroscopy (MRS) can image metabolite concentrations throughout the brain and is sensitive to neuronal dysfunction by showing reduced NAA (nacetylaspartate) levels, increase in choline and other heterogeneous metabolic biomarkers in focal areas (Mueller et al., 2005). These image findings can also be asymmetric between hemispheres and can help lateralize the epileptogenic zone (Krsek et al., 2007). These act as biomarkers indicating mitochondrial dysfunction, total compartmental neurotransmitter concentrations, neuronal death and glial activation (Kuzniecky, 2004, Shultz et al., 2014). These can also separate etiologies since some such as FCD demonstrate metabolic abnormalities whereas heterotopias and polymicrogyrias do not often demonstrate these biomarkers (Kuzniecky et al., 1997). Other lesions, such as hypothalamic hamartomas tend to have reduced NAA and increased myo-inositol (Freeman et al., 2004). Another study has shown that postsurgical outcome was better if resected tissue was metabolically abnormal compared to resected tissues that were metabolically normal (Pan et al., 2013).

其他的成像采集序列已经在癫痫患者身上进行了研究，但是很少有这些序列被常规用于临床目的。磁共振质子光谱(MRS)能够显示整个大脑的代谢物浓度，并且通过显示焦点区域中降低的 nAA (naceylaspartate)水平、胆碱和其他异质代谢生物标志物的增加而对神经元功能不良敏感(Mueller et al。 ，2005)。这些图像发现也可能是两个半球之间的不对称，并且有助于使致癫痫区偏侧化(Krsek et al。 ，2007)。这些作为指示线粒体功能不良，总间隔神经递质浓度，神经元死亡和神经胶质活化的生物标志物(Kuzniecky，2004，Shultz 等，2014)。这些也可以分离病因，因为一些如 FCD 表现出代谢异常，而异位和多小脑回并不经常表现出这些生物标志物(Kuzniecky 等，1997)。其他病变，如下丘脑错构瘤倾向于降低 NAA 和增加肌醇(Freeman 等，2004)。另一项研究表明，与代谢正常的切除组织相比，如果切除组织代谢异常，术后结果更好(Pan et al。 ，2013)。

Other imaging sequences such as magnetization transfer imaging (MTI) can be very sensitive to detecting MCDs, though the specificity of MTI has not been well studied (Rugg-Gunn et al., 2003). Chemical exchange saturation techniques (CEST) can study specific neurotransmitter and macromolecular concentrations, such as glutamate (Cai et al., 2012). Areas with increased extracellular glutamate and with decrease glutamate-glutamine cycling have been associated with increase seizure likelihood, such as in areas of hippocampal sclerosis (Pan et al., 2008, Davis et al., 2015).

其他成像序列如磁化转移成像(MTI)对检测 MCD 非常敏感，尽管 MTI 的特异性尚未得到很好的研究(Rugg-Gunn 等，2003)。化学交换饱和技术(CEST)可以研究特定的神经递质和大分子浓度，如谷氨酸(Cai et al。 ，2012)。细胞外谷氨酸增加和谷氨酸-谷氨酰胺循环减少的区域与癫痫发作可能性增加有关，例如在海马硬化区域(Pan 等，2008，Davis 等，2015)。

2.9. Summary2.9摘要

Current clinical epilepsy imaging protocols are primarily structural and diffusion imaging modalities. Specifically, T1W, T2W, FLAIR, DWI and sometimes MRS sequences are used to identify possible epileptogenic lesions, usually malformations of cortical development. In addition, many institutions combined these structural images with functional imaging using PET, MEG, and SPECT. There have been many studies that have looked at quantitative MRI techniques, such as T2 mapping, HARDI-DTI, DKI, DIR and MP2RAGE, as possible ways to improve contrast-to-noise ratios especially in the gray matter but few studies have been able to improve sensitivity/specificity or correlate image findings with postsurgical seizure outcome.

目前的临床癫痫成像主要是结构性和扩散性成像。具体来说，T1W，T2W，FLAIR，DWI 和有时 MRS 序列被用来鉴别可能的致痫病变，通常是皮质发育畸形。此外，许多机构将这些结构图像与使用 PET、 MEG 和成像的功能性分散观测结合起来。已有许多研究关注定量 MRI 技术，如 T2定位、 HARDI-DTI、 DKI、 DIR 和 MP2RAGE，作为提高对比噪声比的可能方法，特别是在灰质，但很少有研究能够提高敏感性/特异性或将图像发现与术后癫痫发作结果相关联。

Go to:浏览:3. How are features computed by machine?3. 机器如何计算特征？

In recent years, improvements in imaging technology such as parallel imaging MRI and high field scanners have improved the detection of malformations of cortical development, a large proportion of which can lead to epilepsy. However, visual analysis of these images by radiologists is a challenging task and there is considerable variability in the interpretation of these images. Recent trends in medical computer vision have tried to semi-automate the computation of these features and detection of these lesions, primarily through the use of voxel-based morphometry (VBM) as well as surface-based morphometry. Some methods attempt to detect them using textural features. Most methods compute these features with respect to a nominal distribution (z score) or with respect to the contralateral hemisphere (asymmetry analysis (Yang et al., 2011)). An exhaustive list of the image features used by different papers is laid out in Table 2.

近年来，成像技术的进步，如平行成像核磁共振成像和高场扫描仪，已经改善了对皮层发育畸形的检测，其中很大一部分可以导致癫痫。然而，由放射科医师对这些图像进行视觉分析是一项具有挑战性的任务，而且在对这些图像的解释方面存在相当大的差异性。最近医学计算机视觉的发展趋势试图半自动化地计算这些特征并检测这些病变，主要是通过使用基于体素的形态测定法(vBM)和基于表面的形态测定法。一些方法试图利用纹理特征来检测它们。大多数方法根据名义分布(z 分数)或对侧半球(不对称分析(Yang et al。 ，2011))计算这些特征。表2详尽列出了不同论文所使用的图像特征。

Table 2表2

List of features and sample methods used to compute the features. Different combinations of these features were used to isolate and identify lesions (usually focal cortical dysplasias).

用于计算特征的特征列表和样本方法。这些特征的不同组合被用来分离和鉴别病变(通常是局灶性皮质发育不良)。

Computable features for detection of epileptogenic lesions可计算特征在癫痫病灶检测中的应用
Feature特写		Algorithms to compute feature计算特征的算法
Image intensity图像强度		Voxel-based morphometry (Ashburner and Friston, 2000), difference maps (Wilke et al., 2003), laplacian intensity gradient (Colliot et al., 2006a), other statistical measures (mean, median, variance, skewness, kurtosis, energy, entropy)基于体素的形态测定法(Ashburn 和 Friston，2000) ，差异图(Wilke 等，2003) ，拉普拉斯强度梯度(Colliot 等，2006a) ，其他统计指标(平均值，中位数，方差，偏度，峰度，能量，熵)
Cortical thickness皮层厚度		Diffeomorphic registration based cortical thickness (Tustison et al., 2014), distance between gray/white and pial isocontour surfaces (Dale et al., 1999a, Besson et al., 2008b)基于差异形态配准的皮层厚度(Tustison 等，2014) ，灰色/白色和软脑膜等高面之间的距离(Dale 等，1999a，Besson 等，2008b)
Gray-white blur灰白模糊		Gradient map using gaussian smoothing, identify areas with highest cortical thickness (Qu et al., 2013), MAP (Wagner et al., 2011), iterated local searches on neighborhood (Xiaoxia et al., 2014)使用高斯平滑的梯度图，识别具有最高皮层厚度的区域(瞿等，2013) ，MAP (Wagner 等，2011) ，迭代邻居的局部搜索(Xiasia 等，2014)
Sulcal reconstruction颅骨重建		Graph matching (Rivière et al., 2002), gyrification index (Dale et al., 1999), spherical wavelets (Yu et al., 2007, Nain et al., 2007)图形配合(Rivière et al。 ，2002) ，回旋指数(Dale et al。 ，1999) ，球形小波(Yu et al。 ，2007，内恩 et al。 ，2007)
Lobar or volume atrophy/enlargement叶或体积萎缩/增大		Deformation based morphometry, jacobian of heat equation vector field applied to spherical harmonics with a point distribution model (Kim et al., 2013, Bernhardt et al., 2015)基于变形的形态测定法，热方程向量场的雅可比变换应用于点分布模型的球形谐波(Kim et al。 ，2013，berhardt et al。 ，2015)
Curvature曲率		Gaussian intrinsic curvature (Kim et al., 2013, Pienaar et al., 2008), extrinsic curvature (Pienaar et al., 2008), integral measures of curvature (Van Essen and Drury, 1997), orientation fields from gradient structure tensors (Rieger and van Vliet, 2002, Rieger et al., 2004), area-minimizing flows to spherical registration (Besson et al., 2008b)高斯内禀曲率(Kim et al。 ，2013，Pienaar et al。 ，2008) ，外禀曲率(Pienaar et al。 ，2008) ，曲率的整体测量(埃森和德鲁里，1997) ，梯度结构张量(Rieger and Van Vliet，2002，Rieger et al。 ，2004)的方向场，面积最小化流向球面配准(Besson et al。 ，2008b)
Asymmetry analysis (Goffin et al., 2010)不对称性分析(Goffin 等，2010)		Asymmetry index, asymmetry analysis on cortical folding (Van Essen et al., 2006)不对称指数，皮质折叠的不对称分析(埃森等，2006)
Other cortical measures其他大脑皮层测量		Fractal analysis of the cortex (Bernasconi, 2004), metric distortions on spherical registration (Wisco et al., 2007)皮层的分形分析(Bernasconi，2004) ，球面配准的度量畸变(Wisco 等，2007)
Texture analysis纹理分析
3D texture analysis三维纹理分析	Directional Riesz wavelets (Jiménez Del Toro et al., 2013)方向 Riesz 小波(Jiménez Del Toro 等，2013)
Gray-level co-occurrence灰度共现	Contrast, homogeneity, inverse difference, energy, entropy对比，同质，反差，能量，熵	Haralick et al. algorithmHaralick 等人的算法
Gray-level run-length灰级游程长度	Short/long run emphasis, gray level distribution, run-length distribution短期/长期重点，灰色层次分布，游程长度分布	Haralick et al. (Haralick et al., 1973) algorithm (Haralick et al., 1973)Haralick 等(Haralick 等，1973)算法(Haralick 等，1973)

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Each of the above techniques has strengths and limitations, but the combination of such techniques could improve the detection of dysplastic lesions that are undetectable by MRI (Zhang et al., 2014). Still, some of them are easily missed, and the use of computational techniques is often not sufficient to detect them (Zhang et al., 2014). This section surveys the different methods that have been developed to outline the common image findings listed in the previous section. In most studies, sensitivity is defined as the fraction of patients who had lesions identified using computational analysis that went on to be resected. Specificity is defined as the fraction of normal healthy controls that failed to elicit any detection by these automated tools.

上述每一种技术都有优势和局限性，但是这些技术的结合可以改善 MRI 检测不到的发育异常病变的检测(Zhang et al。 ，2014)。尽管如此，其中一些很容易被忽略，计算技术的使用往往不足以检测到它们(Zhang et al。 ，2014)。本节概述了为概述上一节所列的共同图像调查结果而开发的不同方法。在大多数研究中，敏感性被定义为使用计算机分析确定病变并进一步切除的患者比例。特异性被定义为这些自动化工具未能引起任何检测的正常健康对照的分数。

3.1. Signal intensity change and subcortical presence of abnormal gray matter信号强度改变及皮质下出现异常灰质

The most common image finding is an abnormality like a hyper- or hypo-intensity on standard imaging sequences (T1W, T2W, FLAIR). The most common method to detect these abnormalities is a voxel-wise approach which involves calculating z- or t-score statistical maps using a nominal distribution from normal healthy controls (Bernasconi, 2004, Colliot et al., 2005, Colliot et al., 2006b, Colliot et al., 2006c, Colliot et al., 2006a). Scores are computed either on the intensity itself or, more often, on a computed relative intensity score (Colliot et al., 2005, Colliot et al., 2006c; Besson et al., 2008b, Colliot et al., 2006b). Another common method to detect signal abnormalities is a type of surface-based method. A recent surface-based approach computed surface-based features of FCD morphology and was able to detect abnormal gray matter in patients initially read as MRI-negative with high sensitivity and specificity (Hong et al., 2014). Other methods detect hyperintensities by calculating the difference between voxel intensities and voxels on the gray and white boundaries to find signal abnormalities at the gray-white junction (Antel et al., 2002).

最常见的图像发现是一种异常，比如标准成像序列(T1W，T2W，FLAIR)的高或低强度。检测这些异常的最常见方法是体素方法，其涉及使用来自正常健康对照的名义分布计算 z 或 t 分数统计图(Bernasconi，2004，Colliot 等，2005，Colliot 等，2006b，Colliot 等，2006c，Colliot 等，2006a)。分数是根据强度本身计算的，或者更常见的是根据计算的相对强度评分计算的(Colliot 等，2005，Colliot 等，2006c; Besson 等，2008b，Colliot 等，2006b)。另一种常用的检测信号异常的方法是基于表面的方法。最近的一种基于表面的方法计算了基于表面的 FCD 形态特征，并能够检测到最初被诊断为高灰质 MRI 阴性的患者的异常灵敏度和特异度(Hong et al。 ，2014)。其他方法通过计算体素强度和灰色和白色边界上的体素之间的差异来检测高信号，以发现灰白色交界处的信号异常(Antel 等，2002)。

These techniques can be adapted to identify other malformations, such as subcortical band heterotopia (“double cortex” syndrome) (Huppertz et al., 2008) or periventricular heterotopia, where ventricle masks are used to look for abnormal gray matter outlining the ventricles (92.5% sensitivity and 91.5% specificity) (Pascher et al., 2013). In addition, these methods have been sensitive to detecting co-occurring neoplasia with focal cortical dysplasia (87% sensitivity) (Bruggemann et al., 2007).

这些技术可用于识别其他畸形，如皮层下带异位(“双皮层”综合征)(Huppertz 等，2008)或脑室周围异位，其中心室面罩用于寻找心室轮廓异常的灰质(92.5% 灵敏度和91.5% 特异性)(Pascher 等，2013)。此外，这些方法已经敏感的检测共同发生的肿瘤与局灶性皮质发育不良(87% 的敏感性)(Bruggemann 等，2007年)。

Some important caveats to using voxel-based morphometry are: (Schachter, 2015) statistical maps depend on the control population used for analysis (Kwan et al., 2011), the results of VBM depend on the accuracy of intrasubject registration and normalization (Bookstein, 2001, Begley et al., 2000), there have been studies that showed a lack of correlation between gray matter probability values and the cortical neuropathological measures in normal-appearing gray matter, suggesting that intrinsic neuropathological cortical changes do not necessarily influence gray matter probability maps used for VBM analyses (Eriksson et al., 2009).

使用基于体素的形态测定法的一些重要警告是: (Schachter，2015)统计图取决于用于分析的对照人群(Kwan 等，2011) ，VBM 的结果取决于受试者内注册和标准化的准确性(Bookstein，2001，Begley 等，2000) ，有研究显示在正常出现的灰质中，灰质概率值与皮层神经病理学测量之间缺乏相关性，表明内在的神经病理学皮层变化不一定影响用于 VBM 分析的灰质概率图(Eriksson 等，2009)。

Other methods of detecting intensity change and abnormal presence of gray matter in white matter include computation of difference maps (Wilke et al., 2003), fractal analysis of the cortex (Bernasconi, 2004), analysis of intensity gradients (Colliot et al., 2006a), asymmetry analysis of intensities (Goffin et al., 2010), and analysis of textures (Bernasconi, 2004, El Azami et al., 2013). Studies using texture analysis compute cubic volume sampling around each voxel to calculate second and third order textural features, and compare them to the contralateral side (Yang et al., 2011, Jiménez Del Toro et al., 2013). These methods were able to easily identifiable lesions in 85% of patients in one study.

其他检测灰质强度变化和白质异常的方法包括差异图计算(Wilke 等，2003) ，皮层分形分析(Bernasconi，2004) ，强度梯度分析(Colliot 等，2006a) ，强度不对称分析(Goffin 等，2010)和纹理分析(Bernasconi，2004，El Azami 等，2013)。使用纹理分析的研究计算每个体素周围的立方体积采样，以计算二阶和三阶纹理特征，并将它们与对侧进行比较(Yang et al。 ，2011，Jiménez Del Toro et al。 ，2013)。在一项研究中，这些方法能够在85% 的患者中很容易地识别病变。

Signals can also be abnormal in other modalities, such as DTI ADC, when compared to controls. For instance, VBM on T2 mapping had 87% sensitivity (Rugg-Gunn et al., 2005) and VBM on FLAIR imaging had 88% sensitivity/96% specificity (Focke et al., 2008) in detecting hyperintensities. One study identified a lesion in a test patient using 7T VBM (Speck et al., 2009). The few studies that have applied VBM to DTI found reduced fractional anisotropy, increased trace of tensor eigenvalues, and elevation in perpendicular eigenvalues in lesional voxels (Widjaja et al., 2007). Another study looked at probabilistic reconstruction of PET-MR data using asymmetry as a way to detect hypo-/hyper-metabolic regions (Goffin et al., 2010). This study was able to detect lesions correctly in 71% of the patients though specificity was hard to determine since the other positive findings could not be verified as false positives. Fewer studies have reported segmentation accuracy in terms of coverage of lesional voxels but the study with the best results has reported 73% segmentation accuracy (Bergo et al., 2008). More research needs to be done in applying morphometric methods to newer modalities of imaging.

与对照组相比，其他形式的信号也可能是异常的，例如 DTI ADC。例如，T2定位的 VBM 具有87% 的灵敏度(Rugg-Gunn 等，2005) ，而 FLAIR 成像的 VBM 在检测高信号方面具有88% 的灵敏度/96% 的特异性(Focke 等，2008)。一项研究使用7 T VBM 确定了一个测试患者的病变(Speck et al。 ，2009)。应用 vBM 进行 dTI 的少数研究发现，病变体素的分数各向异性减少，张量特征值的迹象增加，垂直特征值升高(Widjaja et al。 ，2007)。另一项研究观察了 PET-MR 数据的概率重建，使用不对称作为检测低/高代谢区域的方法(Goffin 等，2010)。这项研究能够正确检测病变的71% 的患者，虽然特异性很难确定，因为其他阳性结果不能被证实为假阳性。较少的研究报道了在病变体素覆盖范围方面的分割准确性，但是最好的研究报道了73% 的分割准确性(Bergo et al。 ，2008)。在将形态测量学方法应用于更新的成像形式方面，还需要做更多的研究。

3.2. Increased cortical thickness3.2皮层厚度增加

Cortical thickness measures the radial distance between white and gray matter surfaces (Thesen et al., 2011). Increased cortical thickness is a sensitive finding in malformations of cortical development, specifically FCDs (Thesen et al., 2011). In past studies, increased cortical thickness has been reported in 91% of patients (Bernasconi and Bernasconi, 2011). Early studies focused on cortical thickness computed as the distance between isosurfaces corresponding to the gray-white junction and the gray-CSF junction, utilizing Laplace"s equation to identify these intensity-based contours (Antel et al., 2002, Bernasconi, 2004, Colliot et al., 2005, Colliot et al., 2006b). Currently, the most commonly used cortical thickness tools include Civet-CLASP (MacDonald et al., 2000, Kim et al., 2005), Freesurfer (Dale et al., 1999) and diffeomorphic registration based thickness measures (Tustison et al., 2010, Das et al., 2009).

皮质厚度测量白色和灰质表面之间的径向距离(Thesen et al。 ，2011)。皮质厚度增加是皮质发育异常，特别是 FCD 的敏感发现(Thesen 等，2011)。在过去的研究中，91% 的患者皮质厚度增加(Bernasconi and Bernasconi，2011)。早期的研究集中在皮层厚度计算为对应于灰白色交界处和灰色-脑脊液交界处的等值面之间的距离，利用拉普拉斯方程来识别这些基于强度的轮廓(Antel 等，2002，Bernasconi，2004，Colliot 等，2005，Colliot 等，2006b)。目前，最常用的皮层厚度测量工具包括 Civet-CLASP (MacDonald 等，2000，Kim 等，2005) ，Freesurfer (Dale 等，1999)和基于差异形态配准的厚度测量(Tustison 等，2010，Das 等，2009)。

Cortical thickness can be measured through a voxel wise approach or by utilizing tools that compute it using surface-based morphometry. A recent study that utilized surface-based morphometry showed that with cortical thickness as a feature, “a surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time” (Thesen et al., 2011).

3.3. Gray-white junction blurring

Another important image finding sensitive to malformations of cortical development is blurring of the junction between outer gray matter and inner white matter, which contributes to a pseudo-thickening of the cortex (see Fig. 1). Up to 72–96% of lesions will have this finding on MRI (Bernasconi and Bernasconi, 2011). In addition, the majority of patients (up to 83%) who have FCDs but no imaging findings have subtle GW junction blurring that is initially missed by the neuroradiologist.

Techniques used to model GW blur include computing a gradient map after convolution with a Gaussian kernel (Antel et al., 2002, Colliot et al., 2005, Colliot et al., 2006b) and computing VBM of GM intensity across a nominal distribution from healthy controls (Huppertz et al., 2005). Another technique approximated areas of blur by finding regions with the highest cortical thickness, which would find pseudo-thick regions of the cortex as a result of GW blur. This technique has 70% sensitivity in detecting blurred regions (Qu et al., 2013).

The most well-studied and successful technique is the MAP technique (Huppertz et al., 2005) which creates a “junction” image. Junction images are calculated by computing the histograms of classified gray and white matter voxels. Next, those with intensities that fall in between the two histogram means are isolated. These are used to identify voxels that are not definitively gray or white matter. The resulting binary image is convolved with a smoothing kernel, revealing areas of ambiguous gray voxels. This image is subtracted from similarly created junction images from a nominal distribution of healthy controls, showing areas of abnormal “junction” (Huppertz et al., 2005). One study that used the MAP technique was able to identify a lesion in one patient"s imaging that was initially read as normal. This led to resection of the identified abnormal tissue and the patient has been seizure-free post surgery (Wang et al., 2012). Another study similarly found subtle lesions in the orbitofrontal region of a patient with FCD and gliosis (Wang et al., 2013). This technique helps in redirecting the reviewer to suspicious areas rather than generating new diagnostic information for clinicians (Wang et al., 2012, Wang et al., 2015).

Other more advanced methods exist to quantitatively measure GW blur. For instance, a recent study utilized an “iterated local searches on neighborhood” technique to improve the specificity of these approaches, specifically the gradient method, by measuring the GW border width and creating a potential map based on the probability distributions of GM and WM in each voxel (Xiaoxia et al., 2014). This potential map is then converted into a distance map between the gray and white matter surfaces using an iterative optimization approach that optimally searches in a cubic neighborhood to find the shortest distance. This distance metric represented the GW junction width and highlighted abnormally thickened regions of the cortex (Xiaoxia et al., 2014).

其他更先进的方法存在定量测量 GW 模糊。例如，最近的一项研究利用“邻域迭代局部搜索”技术来改善这些方法的特异性，特别是梯度方法，通过测量 GW 边界宽度并根据每个体素中 GM 和 WM 的概率分布创建潜在的地图(Xiasia 等，2014)。然后使用迭代优化方法将这个潜在的映射转换为灰色和白质表面之间的距离映射，该方法在一个立方邻域中优化搜索以找到最短的距离。这个距离度量代表了 GW 连接宽度，并突出显示了皮层异常增厚的区域(Xiasia et al。 ，2014)。

3.4. Sulcal and gyral abnormalities3.4. 颅骨和脑回异常

Patients with malformations of cortical development will show many different sulcal and gyral abnormalities. For instance, some FCDs are located in the deep sulcus (Besson et al., 2008a). Other malformations, such as lissencephaly and polymicrogyria will have abnormally shaped sulci and gyral structure. Other radiological findings include specific image findings when the cortical surface is reconstructed, such as polymicrogyria, pachygyria and the power-button sign, which occurs in 62% of FCD type II patients due to a particular elongation of the precentral sulcus (Mellerio et al., 2014b).

皮质发育畸形患者会出现许多不同的沟回异常。例如，一些 FCD 位于深沟(Besson 等，2008a)。其他畸形，如无脑畸形和多小脑回，将有异常形状的沟和回结构。其他放射学发现包括当皮质表面重建时的特定图像发现，如多小脑回，厚脑回和电源按钮符号，这在62% 的 FCD II 型患者中由于中央前沟的特定延伸而发生(Mellerio 等，2014b)。

Early methods of sulcal mapping used graph matching algorithms, driven by minimization of a global cost function derived from intensity-based potentials (Rivière et al., 2002). Voxel-based methods have also been used to determine these abnormal gyration patterns that stand out when compared to normal health control gyri (Wagner et al., 2011). Similarly, surface based methods (e.g. Freesurfer) allows for quantification of surface-based features such as gyrification index, curvature and sulcal depth (Dale et al., 1999). Gyrification index quantifies the gyral anatomy in a circular region around any surface vertex. This surface-based feature was found to be helpful in detecting epileptogenic malformations but were not specific to this condition (Thesen et al., 2011). More recent sulcal morphometric methods have been used to identify regions with deep sulci or broad gyri (Hong et al., 2014).

早期的磺化映射方法使用图形配合算法，由强度基础电位导出的全局成本函数的最小化驱动(Rivière et al。 ，2002)。基于体素的方法也被用来确定这些异常的回旋模式，当与正常健康控制脑回相比较时突出(Wagner et al。 ，2011)。类似地，基于表面的方法(例如 Freesurfer)允许表面特征的定量，例如回旋指数、曲率和沟深(Dale et al。 ，1999)。陀螺化指数量化任何表面顶点周围圆形区域的回旋解剖。这种基于表面的特征被发现有助于检测致痫畸形，但不是特异于这种情况(Thesen 等，2011)。最近的沟形态测量方法已经被用于鉴别具有深沟或宽回的区域(Hong et al。 ，2014)。

3.5. Diffuse/multifocal hyperintensities3.5弥漫/多焦点高信号

Any of the above features can be present in different lobes of the brain. There have been studies confirming multi-focal occurrence of MCD (Fauser et al., 2009). Other malformations of cortical development such as periventricular heterotopia or subcortical band heterotopia can have abnormal gray matter signal around the ventricles or near the gray-white junction at multiple locations of the brain (see Fig. 2). All the above methods be applied throughout the brain but suffer in specificity.

上述任何一个特征都可以在不同的脑叶中出现。已经有研究证实 MCD 的多焦点发生(Fauser et al。 ，2009)。其他皮质发育畸形，如脑室周围异位或皮质下带异位，可能在脑室周围或接近灰白色交界处的多个位置出现异常的灰质信号(见图2)。上述所有方法都适用于整个大脑，但具有特异性。

3.6. Summary3.6摘要

Most of the imaging findings that radiologists look for in patients with epilepsy can be computed and detected sufficiently well, though with questionable specificity. The most common methods of computation and detection involved voxel-based morphometry or a statistical mapping of features compared to a nominal distribution from normal controls. Other surface-based morphometric techniques exist to compute other anatomical features, such as gyrification, sulcal depth, and cortical thickness. While many studies have combined some of these features with variable success on FCD type II, future studies should focus on identifying all malformations of cortical development like low-grade glial tumors. In addition, future studies should combine some of these features or compute these features across multiple imaging modalities to gain better specificity. This can better identify epileptogenic lesions.

放射科医生在癫痫患者身上寻找的大多数成像结果都能够被充分地计算和检测出来，尽管其特异性值得怀疑。最常见的计算和检测方法包括基于体素的形态测定法或特征的统计映射，与来自正常对照的标称分布相比较。还有其他基于表面的形态测量技术可以计算其他解剖特征，例如旋转、沟深和皮质厚度。虽然许多研究已经将这些特征中的一些与 FCD II 型的不同成功相结合，但未来的研究应该集中于识别所有皮质发育畸形，如低级别胶质瘤。此外，未来的研究应该结合这些特征中的一些，或者通过多种成像模式计算这些特征，以获得更好的特异性。这样可以更好地鉴别致痫性病变。

Go to:浏览:4. How well can computational analysis identify lesions based on these features?4. 根据这些特征，计算机分析能否很好地识别病变？

Automated analysis provides an effective way to simplify the analysis and diagnosis of malformations of cortical development in epilepsy patients. It can reduce the burden on radiologists in their practice and improve diagnostic accuracy (Wang and Summers, 2012). Many studies have attempted to fully automate the detection of MCDs, specifically cortical dysplasias, by applying supervised and unsupervised learning techniques based on the features discussed in the previous section. Below we review the techniques tried as well as the accuracy of these automated techniques.

自动化分析为简化癫痫患者皮质发育畸形的分析和诊断提供了一种有效的方法。它可以减轻放射科医师在实践中的负担，提高诊断的准确性(Wang 和 Summers，2012)。许多研究试图通过应用基于上一节讨论的特征的监督和非监督式学习技术来完全自动检测 mcd，特别是皮质发育不良。下面我们回顾一下所尝试的技术以及这些自动化技术的准确性。

4.1. Segmentation4.1分割

Many studies attempted to segment cortical boundaries of lesions based on changes in image gradients, especially in cases where they caused significant gray-white blurring or increased cortical thickness. Despotović et al. (2011) integrated Markov random field-based energy functions with a graph cuts algorithm to more accurately segment cortices with focal cortical dysplasias (Despotović et al., 2011). These investigators confirmed their accuracy through comparison to other segmentation techniques, such as SPM and FSL, using Dice scores, a common metric of overlap and similarity used with segmentation algorithms.

许多研究试图根据图像梯度的变化来分割病变的皮层边界，特别是在病变引起明显的灰白色模糊或皮层厚度增加的情况下。Despotovi 等(2011)将基于马尔可夫随机场的能量函数与图切算法相结合，以更准确地分割具有局灶性皮质发育不良的皮层(Despotovi 等，2011)。这些研究人员通过与其他分割技术(如 SPM 和 FSL)的比较，使用 Dice 得分(一种用于分割算法的共同的重叠和相似度量标准)来确认他们的准确性。

Shen et al. (2011) used fuzzy c-means segmentation algorithm to create a fuzzy index matrix that quantified degree of gray-white blurring (Shen et al., 2011). This technique identified the lesion correctly in 5 of 7 patients with FCDs. These positive results indicate further research is needed to investigate the utility of advanced techniques in segmentation.

Shen 等人(2011)使用模糊 c 均值分割算法来创建一个模糊指数矩阵，量化灰白模糊的程度(Shen 等人，2011)。这项技术在7例 FCD 患者中有5例正确识别了病变。这些积极的结果表明，需要进一步的研究，以调查先进的技术在分割的效用。

4.2. Supervised learning4.2监督式学习

In supervised learning, each sample contains two parts: one is a set of input features and the other is output observations or labels (Wang and Summers, 2012). The purpose of supervised learning is to deduce a functional relationship from training data that generalizes well to testing data (Wang and Summers, 2012). The following studies used different forms of supervised learning to correctly classify voxels as lesional or normal. They primarily looked at FCD type II.

在监督式学习中，每个样本包含两部分: 一部分是一组输入特征，另一部分是输出观察值或标签(Wang and Summers，2012)。监督式学习的目的是从训练数据中推断出一种函数关系，这种关系可以很好地推广到测试数据中(Wang and Summers，2012)。以下研究使用不同形式的监督式学习来正确地将体素分为损伤性体素和正常体素。他们主要研究 FCD II 型。

El Azami et al. (2013) utilized multiple textural features to compute symmetric textural patches taken from both hemispheres. These features were trained using a reduced coulomb energy classifier that attempts to fit the best hypersphere that would correctly classify each voxel based on their feature set. Their training data was a small sample (1%) of voxels (from both healthy controls and patients) along with features corresponding to each one. The test data was run using a leave-one-out cross validation scheme. This was followed by outlier removal via thresholding of cluster size and distance to nearby lesional clusters. This resulted in 77% coverage of correctly identified lesional voxels that were concordant with manually drawn ROIs in study patients.

El Azami 等人(2013)利用多种纹理特征来计算来自两个半球的对称纹理斑块。这些特征被训练使用一个减少库仑能量分类器，试图适应最好的超球面，将正确分类每个体素基于他们的特征集。他们的训练数据是一个小样本(1%)的体素(来自健康对照和患者)以及与每一个相对应的特征。测试数据采用留一个交叉确证方案。然后通过阈值分割群集大小和距离附近的病变群集异常点去除。这导致77% 的正确识别病变体素的覆盖率与研究患者手工绘制的 ROI 一致。

Strumia et al. (2013) computed textural based features such as image gradient, skewness of local cortical thickness histograms, and spatial tissue probability maps on patients with FCDs and normal healthy controls. These features were used in a Naive-Bayes classifier to calculate probabilities at each voxel. The accuracy of classification of this method was 51% with a dice score of 0.13. This was compared to the MAP technique (Huppertz et al., 2005), which only had an accuracy of 17%.

Strumia 等(2013)计算了 FCD 患者和正常健康对照者的基于纹理的特征，如图像梯度、局部皮层厚度直方图的偏斜度和空间组织概率图。Naive-Bayes 分类器使用这些特征来计算每个体素的概率。该方法分类准确率为51% ，骰子得分为0.13。这与 MAP 技术(Huppertz et al。 ，2005)进行了比较，后者的准确率仅为17% 。

Antel et al. (2003) computed the following features: cortical thickening, blurring of gray-white junction, gray-level hyperintensity through image gradient, and textural features such as statistics on gray-level co-occurrence matrices (angular second momentum, difference entropy, contrast). As a first step, the intensity-based features were trained in a Bayesian classifier to classify as lesional or normal. Voxels classified as lesional were then reclassified based on Fisher"s discriminant ratio using textural features. This boosting technique was performed using a leave-one-out cross validation approach and resulted in an average sensitivity of 83% detection in the test patient population. There were no lesions classified in the normal healthy control population (100% specificity). There were some additional ones detected in the patient population, though further study is required to determine whether these correlated with clinical disease.

Antel 等人(2003)计算了以下特征: 皮质增厚，灰白连接的模糊，通过图像梯度的灰度高信号，以及纹理特征，例如灰度共现矩阵的统计(角秒动量，差熵，对比度)。作为第一步，在贝叶斯分类器中训练基于强度的特征，将其分类为损伤或正常。分类为病变的体素，然后重新分类基于费舍尔的判别比使用纹理特征。这种增强技术是使用一次性交叉确证的方法进行的，结果在测试患者群体中平均检测灵敏度为83% 。在正常健康对照人群中没有病变分类(100% 特异性)。在患者人群中发现了一些额外的病例，但是还需要进一步的研究来确定这些病例是否与临床疾病相关。

Another study, Besson et al. (2008b), took a similar approach but used surfaced-based features, including cortical thickness, curvature and sulcal depth. In addition, the authors of the study modeled voxel-based features, such as gray-white blur and signal hyperintensity, on these surface contours outlining the gray and white matter. They applied a four-layer feed forward neural network to classify each vertex as lesional or healthy. To avoid overfitting, a cross-validation method was used to optimize the neural networks. The mean and standard deviation of all surface features of clusters of vertices classified as lesional were then reclassified using a fuzzy k-nearest neighbor to remove false positives in healthy controls. The sensitivity of the first classifier, which was 95%, reduced to 68% once the second classifier was implemented to ensure no false positives were detected in healthy controls (100% specificity).

另一项研究，Besson 等人(2008b) ，采取了类似的方法，但使用表面为基础的特征，包括皮层厚度，曲率和沟深。此外，这项研究的作者模拟了基于体素的特征，比如灰白模糊和信号高信号，在这些表面轮廓上勾勒出灰色和白质。他们应用四层前馈神经网络将每个顶点分类为损伤或健康。为了避免过度拟合，使用了交叉验证方法来优化神经网络。然后用模糊 k 最近邻重新分类顶点群的所有表面特征的平均值和标准差，以去除健康对照中的假阳性。一旦实施第二个分类器，第一个分类器的灵敏度(95%)降至68% ，以确保在健康对照组中没有检测到假阳性(100% 特异性)。

Yang et al. (2011) computed statistical features on cortical thickness and gradient vectors and applied them to a Naïve Bayes classifier. This classifier resulted in 62% sensitivity, 81% specificity after parameters were optimized.

4.3. Recent computational models

Recent methods have refocused the problem of detecting lesions as an outlier detection problem. This follows from the idea that a lesion is an outlier in the feature space when compared to the same region across control populations. This outlier detection approach has been successful in other fields (e.g. seizure prediction (Gardner et al., 2006)) and overcomes the need to collect large amounts of training data and fine-tune parameters of the model. The most recent supervised algorithm (Ahmed et al., 2014) classified segmented patches of the cortex, obtained using unsupervised segmentation of the flattened cortex, which clusters regions with homogeneous feature values. As a result, their study corrected their method for 3 issues: All voxels in previous learning models are assumed to be independent of each other, most learning models use a second reclassifier to improve specificity but in the process lose sensitivity of the delineation of the epileptogenic lesion and better models need to be able to detect lesions in patients who are MRI negative instead of on imaging with visible lesions.

Another outlier detection method trained a one-class SVM (OC-SVM) to classify voxels as normal based on 6 features (probability maps of GM, WM, CSF, gray matter intensity, gray-white blurring). Then, a test image was inspected using the classifier and a threshold to the distance metric was applied to identify voxels that were “outliers”, or very different, compared to the normal distribution for these feature values (El Azami et al., 2013). This method did similarly well to previously described techniques, such as SPM and MAP (Huppertz et al., 2005), and was able to detect lesions that were missed during initial reading. The authors concluded the study by saying that with better and larger normal control population, their method would be able to detect smaller lesions (El Azami et al., 2013).

In unsupervised learning, there is only one set of features and no label information for each sample (Wang and Summers, 2012). The main purpose of unsupervised learning is to discover interrelationship between the features to uncover latent variables behind the observations (Wang and Summers, 2012). One study used unsupervised techniques to model multiscale cortical surface patches derived from coarse to fine resolutions of the image. These patches were then fed into a random forest, specifically a hierarchical conditional random field, which considers patches that overlap with each other at all scales. This method resulted in detection of lesions in 90% of MR+ images and an impressive 80% of MR- images (Ahmed et al., 2014).

在非监督式学习中，每个样本只有一组特性，没有标签信息(Wang and Summers，2012)。非监督式学习的主要目的是发现特征之间的相互关系，以揭示观察背后的潜在变量(Wang and Summers，2012)。一项研究使用无监督技术来模拟从图像的粗细分辨率衍生出的多尺度皮层表面斑块。然后，这些斑块被输入一个随机森林，特别是一个等级森林，这个条件随机域考虑到斑块在所有尺度上相互重叠的情况。这种方法导致在90% 的 MR + 图像和令人印象深刻的80% 的 MR 图像中检测到病变(Ahmed 等，2014)。

Very few studies have looked at extending these techniques to other modalities. One study looked at the power of combining diffusion weighted imaging with magnetic resonance spectroscopy. Using a linear discriminant analysis on images obtained from pediatric patients with FCD, DNETs and gangliomas, the study found that combining features from different modalities is more powerful than taken individually. When taken alone, none of the MRI parameters was able to distinguish FCD from DNET and gangliomas. When apparent diffusion coefficient variable was added to the model, one patient was still misclassified. The complete separation of all three groups of patients was possible only when conventional MRI, diffusion, and MRS were combined together (Fellah et al., 2012).

很少有研究考虑将这些技术推广到其他形式。一项研究观察了扩散加权成像和磁共振光谱相结合的力量。使用线性辨别分析从患有 FCD、 DNETs 和神经节瘤的儿童患者获得的图像，研究发现结合不同形式的特征比单独使用更有效。当单独使用时，没有一个 MRI 参数能够区分 FCD 与 DNET 和神经节瘤。当明显的扩散系数变量被加入到模型中时，一个病人仍然被错误分类。只有当常规 MRI、弥散和 MRS 结合在一起时，所有三组患者才有可能完全分离(Fellah et al。 ，2012)。

4.4. Summary4.4摘要

This section surveyed a number of automated techniques that detect and define areas of MCD, mostly focal cortical dysplasia. Though most of the methods were supervised, all used different set of features, including voxel-, surface- and texture-based features. Most studies applied their techniques to standard imaging (T1W, T2W), and focused on FCDs. It is important to note that studies applying these techniques are almost all relatively recent, and some of the most promising are published as machine learning or computer science conference papers, speaking to the novel nature of the work. In addition, most of these studies could be applied to other modalities as well.

这部分调查了一些自动化技术，检测和定义区域的 MCD，主要是局灶性皮质发育不良。尽管大多数方法都是在监督下实现的，但所有方法都使用了不同的特征集，包括基于体素、表面和纹理的特征。大多数研究将其技术应用于标准成像(T1W，T2W) ，并将重点放在 fcd 上。值得注意的是，应用这些技术的研究几乎都是相对较新的，其中一些最有希望的研究以机器学习或计算机科学会议论文的形式发表，说明了这项工作的新颖性。此外，这些研究中的大部分也可以应用于其他模式。

There is a need to consider other data-driven approaches in the future including dimensionality reduction, which decreases the number of features and increases relevant information. This has shown promise in complementing current clinical diagnostic tools. For instance, in a study of asthmatics versus non-asthmatics, textural features and other second order image features had higher predictive power of diagnosis as compared to spirometry values (Tustison et al., 2010). Interestingly, it was also found that spirometric values are relatively orthogonal to these image feature values in terms of informational content (Tustison et al., 2010). The same lesson can be applied to the problem of detecting epileptogenic regions, where the current gold standard for seizure localization is through electrophysiology, brain imaging, clinical information, neuropsychological testing, and the physical examination; all felt to give orthogonal information. Utilizing complementary information can help narrow the solution space for search algorithms, so that the underlying structural lesion can be best described through use of minimally-redundant maximally-relevant image features.

未来需要考虑其他数据驱动的方法，包括降维，它减少了功能的数量，增加了相关信息。这在补充目前的临床诊断工具方面显示了希望。例如，在哮喘患者与非哮喘患者的研究中，与肺功能测定值相比，纹理特征和其他二阶图像特征具有更高的诊断预测能力(Tustison 等，2010)。有趣的是，还发现肺活量值在信息含量方面与这些图像特征值相对正交(Tustison 等，2010)。同样的教训也适用于癫痫发作区域的检测问题，目前癫痫发作定位的黄金标准是通过电生理学、脑显象、临床信息、神经心理学测试和身体检查，所有这些都给出了正交信息。利用互补信息可以帮助缩小搜索算法的解决方案空间，以便通过使用最小冗余最大相关图像特征来最好地描述底层结构损害。

Go to:浏览:5. Discussion5. 讨论

A number of methods have emerged over the last decade to detect malformations of cortical development, such as T2W hyperintensity, T1W hypointensity, increased cortical thickness, increased blur of the gray-white junction, and abnormal cortical folding patterns. Technological innovations in imaging such as diffusion imaging and computing techniques such as voxel-based morphometry have converged to make this tremendous advancement. In addition, automated methods like applying machine learning techniques, most commonly supervised learning schemes, have shown impressive results in detecting the most commons lesion that may be present in a patient with drug resistant epilepsy.

过去十年出现了许多检测皮层发育畸形的方法，如 T2W 高信号、 T1W 低信号、皮层厚度增加、灰白色连接增加模糊以及皮层折叠异常。成像的技术创新，例如扩散成像和计算技术，例如基于体素的形态测定法，已经融合在一起，使这种巨大的进步。此外，自动化方法，如应用机器学习技术，最常见的监督式学习方案，在检测耐药性癫痫患者可能存在的最常见病变方面，已经显示出令人印象深刻的结果。

5.1. Multi-centric data-sharing platform5.1. 多中心数据共享平台

The large variability in which lesion was detected across studies is one of the drawbacks of this field. Another is our lack of knowledge as to how much of the epileptic network must be removed to render patients seizure-free, as it may be that more focal resection of better-localized regions would yield better overall outcome. Additionally, variability in patients across studies and institutions, different protocols for pre-surgical evaluation at different centers, different imaging equipment used at each center, and the underlying pathology of the patients included in each study (Zhang et al., 2014) are the contributing factors to the varying accuracies of the different methods mentioned in this review.

病变在不同研究中的检测变异性很大，这是该领域的缺点之一。另一个原因是我们缺乏关于必须切除多少癫痫网络才能使患者无癫痫发作的知识，因为更多局灶性切除更好的局部区域可能会产生更好的总体结果。此外，不同研究和机构患者的差异，不同中心手术前评估的不同方案，每个中心使用的不同成像设备，以及每项研究中包括的患者的潜在病理学(Zhang et al。 ，2014)是导致本综述中提到的不同方法的不同准确性的因素。

The authors of this review believe a multi-centric data-sharing platform with computational pipeline analysis (example in Fig. 3) is the natural next step in the line of research. This will be critical to standardizing neuroimaging data analyses across institutions, avoiding bias and allowing algorithms to be improved to detect multiple types of lesions, not just FCDs. This pipeline would ideally satisfy the following criteria:

这篇综述的作者认为，一个具有计算流水线分析的多中心数据共享平台(图3中的例子)是研究的下一个自然步骤。这对于标准化各机构的神经影像学数据分析，避免偏倚和允许改进算法以检测多种类型的病变，而不仅仅是 FCD 是至关重要的。这条管道最好能满足以下基准:

100">Fig. 3图3

Sample outline of a pipeline to identify key features in multi-modal imaging from patients with drug-resistant epilepsy.

确定耐药性癫痫患者多模式成像的主要特征的管道样本轮廓。

1. The pipeline should have the capability to analyze large-scale neuroimaging data across multiple modalities and time points utilizing the most accurate algorithms to provide regions of interest for further study.

1.管道应该能够跨多种方式和时间点分析大规模的神经影像学数据，利用最精确的算法为进一步研究提供感兴趣的区域。

2. The pipeline should be capable of identifying important radiological features that are of interest to radiologists, such as cortical atrophy, pseudothickening of the GM, ventricular abnormalities, etc.

2.管道应能识别放射科医生感兴趣的重要放射学特征，如皮质萎缩、 GM 假性增厚、心室异常等。

3. The pipeline should be designed with a modular structure to allow easy plug-and-play of different machine learning algorithms in order to serve as a benchmarking platform, where different algorithms can be compared to “gold standard” training data. In addition, this will be important to clinically validate newer sequences and imaging technologies that are discovered, such as ultrahigh field imaging (Madan and Grant, 2009) and advanced diffusion sequences (Winston et al., 2014).

3.流水线应该设计成模块化的结构，以便于不同的机器学习算法的即插即用，从而作为一个基准测试平台，在这个平台上，不同的算法可以与“黄金标准”的训练数据进行比较。此外，这对于临床验证已发现的较新的序列和成像技术(如超高场成像(Madan and Grant，2009)和高级扩散序列(Winston et al。 ，2014))也很重要。

4. Finally, the pipeline should also be able to take in other electrophysiology and clinical metadata in order to better adapt to the clinician"s need. This would also allow researchers to study how well their algorithms correlate with electrophysiological and clinical findings or to apply novel network analysis methods on ECoG-derived networks as well as fMRI and DTI whole-brain networks.

4.最后，管道还应该能够吸收其他电生理学和临床元数据，以便更好地适应临床医生的需要。这也将允许研究人员研究他们的算法与电生理学和临床所见相关性如何，或者将新的网络分析方法应用于 EcoG 衍生的网络以及 fMRI 和 DTI 全脑网络。

Analyzing the amount of neuroimaging data collected at standard epilepsy centers requires a substantial amount of computational resources. Leveraging elastic cloud resources can be a cost effective solution to advance this field into one that is more collaborative and transpires across multiple clinical institutions. There are multiple resources in the neurology community that utilize these cloud resources, such as (Kini et al., 2015), Human Connectome Project (Van Essen et al., 2013), the European EEG database () (Klatt et al., 2012) and LONI IDA (Dinov et al., 2010).

分析在标准癫痫中心收集的神经影像数据量需要大量的计算资源。利用弹性云资源可以成为一个具有成本效益的解决方案，将这个领域推进到一个更具协作性和跨越多个临床机构的解决方案。神经学界有多种资源利用这些云资源，如 (Kini 等，2015) ，Human Connectome Project (埃森等，2013) ，欧洲脑电图数据库( )(Klatt 等，2012)和 LONI IDA (Dinov 等，2010)。

Still, it is a challenge to implement such a multi-centric approach without proper incentives (e.g. federal funding) or appropriate guidance from clinical and scientific leadership. In addition, standardization of imaging data can be a challenge in the clinical epilepsy domain because of the large variability in clinical imaging sequences in the patient workup. All pipelines would have to ensure data format interoperability, de-identification of protected health information (PHI), and adherence to mandated government regulations. Imaging data would have to be manually curated to remove any incomplete data that is of poor quality (e.g. images with artifact distortions) before being uploaded to data sharing platform used by these pipelines. Institutions that would like to contribute would have to make sure they get appropriate consent from patients and have approval from their institutional ethics board (e.g. IRB).

然而，如果没有适当的激励(如联邦资助)或临床和科学领导的适当指导，实施这样一个多中心的方法仍然是一个挑战。此外，成像数据的标准化可能是临床癫痫领域的一个挑战，因为患者检查过程中临床成像序列的变化很大。所有管道都必须确保数据格式的互操作性，去除受保护健康信息(PHI)的标识，并遵守强制性的政府规定。成像数据在上载到这些管道所使用的数据共享平台之前，必须经过人工处理，以去除任何质量欠佳的不完整数据(例如有伪影失真的图像)。想要捐助的机构必须确保获得患者的适当同意，并得到其机构伦理委员会(例如 IRB)的批准。

Over the past 4years, our team of neuroscience and computer science experts has established a cloud-based resource, , that we believe can be a potential solution to these challenges. This platform provides data sharing and analysis capabilities to the neuroscience community, with additional imaging analysis tools planned for the near future. This collaborative platform allows researchers to have tight control over data access and allows researchers to share algorithms and data. We hope solutions like this platform will address these challenges and promote multi-centric work that can make significant impact on clinical practice.

在过去的4年中，我们的神经科学和计算机科学专家团队已经建立了一个基于云的资源，，我们相信这是解决这些挑战的潜在方案。该平台为神经科学提供数据共享和分析能力，并计划在不久的将来增加成像分析工具。这种协作平台允许研究人员对数据访问进行严格控制，并允许研究人员共享算法和数据。我们希望类似这个平台的解决方案能够应对这些挑战，并促进能够对临床实习产生重大影响的多中心工作。

For now, future studies should at least try to ensure proper feature selection in their methods and include newer imaging modalities that provide insight into pathophysiologic mechanisms. In addition, any research study should attempt to make their computational pipelines publicly available to scientists and clinicians so they can be applied in a clinical setting.

现在，未来的研究至少应该尝试在他们的方法中确保适当的特征选择，并包括更新的成像模式，提供对病理生理机制的洞察。此外，任何研究都应该尝试向科学家和临床医生公开其计算流程，以便他们能够应用于临床环境。

5.2. Feature selection5.2特征选择

Features that were used in most studies were more important than the classifier itself because these were derived from important knowledge-guided radiological markers. It is useful to carefully consider which features should be used in any model because slight changes in feature set can cause considerable variability in the resulting prediction model. Proper feature selection will reduce the computation cost of including irrelevant features as well as prevent overfitting.

在大多数研究中使用的特征比分类器本身更重要，因为这些特征来源于重要的知识引导的放射学标记。仔细考虑在任何模型中应该使用哪些特征是有用的，因为特征集的微小变化可能导致所得到的预测模型的相当大的可变性。适当的特征选择可以减少包含不相关特征的计算量，避免过度拟合。

The brute-force method of feature selection is to exhaustively search through all possible combinations of input features and find the best subset. The computational cost associated with this is too high with significant danger of overfitting. Instead, methods that would allow for a ranking of different computational image features in conjunction with clinical values (e.g. radiological or electrophysiological) would yield useful feature subsets that can greatly improve accuracy.

蛮力特征选择的方法是彻底搜索所有可能的输入特征组合，并找到最佳的子集。与此相关的计算成本太高，存在过度拟合的显著危险。相反，允许结合临床价值(例如放射学或电生理学)对不同计算图像特征进行排序的方法将产生有用的特征子集，可以大大提高准确性。

For instance, cortical thickness and gray-white junction blur are useful features to predict cortical dysplasia. Thus, a straightforward approach for feature selection would be to choose features similar to this which best characterize the observed data and agree with expert clinical classification of the target image. This would quantify to clinicians what minimally-redundant information each feature uniquely provides. This might additionally unveil key radiomarkers of disease extent that are better indicators than standard radiological markers. This class of methods is getting more traction in the field of epilepsy. For example, experts in the computational imaging field have uncovered a surprising subpopulation of temporal lobe epilepsy patients who have bilateral hippocampal hypertrophy using surface-based volumetry (Bernhardt et al., 2015). They found this subgroup was high correlated with surgical outcome (Bernhardt et al., 2015).

例如，皮质厚度和灰白色连接模糊是预测皮质发育不良的有用特征。因此，一个直接的特征选择方法是选择与此类似的特征，以最好地表征观测数据并符合目标图像的专家临床分类。这将为临床医生量化每个特征提供的最小冗余信息。这可能还会揭示疾病程度的关键放射标志物，它们是比标准放射标志物更好的指标。这类方法在癫痫领域得到越来越多的关注。例如，计算机成像领域的专家已经发现了一个令人惊讶的使用表面体积测量的双侧海马肥大的颞叶癫痫患者亚群(Bernhardt et al。 ，2015)。他们发现这种亚群与手术结果高度相关(Bernhardt et al。 ，2015)。

All future studies should also consider adding features obtained from newer modalities such as 7T T1 MRI or DTI. In addition, applying the same features as before can uncover potentially important radiomarkers in newer modalities that better explain the pathophysiology. It would be intriguing to study how multi-modal imaging features affect the prediction models of lesion localization.

所有未来的研究也应该考虑增加从新的方式，如7 T T1磁共振成像或弥散张量成像获得的特征。此外，应用与以前相同的功能可以发现潜在的重要放射标志物在更新的形式，更好地解释病理生理学。研究多模态成像特征如何影响病变定位的预测模型将是有趣的。

It is important to note that other more automated approaches to feature selection have been applied to EEG and other feature selection problems and may have applicability here. These include the use of genetic algorithms to search a feature space, including testing a range of secondary features (i.e. features of features (D"Alessandro et al., 2003)).

值得注意的是，其他更自动化的特征选择方法已经应用于脑电图和其他特征选择问题，可能在这里有适用性。这包括使用遗传算法搜索特征空间，包括测试一系列次要特征(即特征的特征(D’Alessandro 等，2003))。

5.3. Gaps in knowledge of pathophysiologic mechanisms.5.3. 对病理生理机制的认识不足。

The pathophysiologic mechanisms and the full clinical spectrum of syndromes associated with malformations of cortical development are unknown. The fact that some lesions can be invisible in structural and functional imaging means that imaging technology has not advanced far enough to understand for investigators to understand MCD pathophysiology. For instance, S.H. Eriksson et al. found that there is lack of correlation between SPM derived gray matter probability values and quantitative cortical neuropathological measures in normal-appearing gray matter, which suggests that there are underlying intrinsic cortical changes that are not reflected in the computed gray matter maps (Eriksson et al., 2009). In addition, lesions in the seizure zone may not be generating seizures, but may require interaction with normal cells outside the seizure region of structural disorganization in order to initiate a seizure (Schwartzkroin and Wenzel, 2012). This has been shown to happen in the perituberal tissue in patients with tuberous sclerosis complex (Sosunov et al., 2015). Thus, delineating the full extent of the epileptogenic zone may be difficult, especially when imaging is incapable of rendering these interactions. Further, it may be necessary to combine different modalities of imaging, including structural, functional, metabolic and EEG to gain insight into the extent of the “critical mass” of the lesion that should be resected (Schwartzkroin and Wenzel, 2012).

与皮质发育畸形相关的各种综合征的病理生理机制和全部临床范围尚不清楚。事实上，一些病变在结构和功能成像上是看不见的，这意味着成像技术还没有进步到足以让研究人员了解 MCD 病理生理学的地步。例如，S.H. Eriksson 等人发现，在正常出现的灰质中，SPM 衍生的灰质概率值与定量皮层神经病理学测量之间缺乏相关性，这表明存在潜在的内在皮层变化，这在计算机灰质图中没有反映(Eriksson 等人，2009)。此外，癫痫发作区的病变可能不会引起癫痫发作，但可能需要与结构紊乱的癫痫发作区域外的正常细胞相互作用以启动癫痫发作(Schwartzkroin 和 Wenzel，2012)。这种情况在结节性硬化症患者的输卵管周围组织中已经得到证实(Sosunov et al。 ，2015)。因此，描述致癫痫区的全部范围可能是困难的，特别是当成像无法呈现这些相互作用时。此外，可能有必要结合不同形式的成像，包括结构、功能、代谢和脑电图，以深入了解应该切除的病变的“临界质量”(Schwartzkroin and Wenzel，2012)。

Studies that have looked at lesion histopathology from patients whose images were read as normal indicate that a majority of these lesions were mild forms of malformations (45% FCD, 22% gliosis, 13% hamartia and gliosis, and 9% hippocampal sclerosis). Thus, imaging techniques need to improve in order to detect these milder forms of malformation earlier in the clinical treatment phase.

对图像正常的患者病变组织病理学的研究表明，这些病变大部分是轻度畸形(45% 的 FCD，22% 的神经胶质增生，13% 的错构和神经胶质增生，以及9% 的海马硬化)。因此，成像技术需要改进，以便在临床治疗阶段更早地发现这些较轻微的畸形。

5.4. Imaging technique challenges5.4成象技术挑战赛

Existing technological improvements in imaging have greatly improved the sensitivity to malformations. For instance, the phased array coil in high and ultra-high field MRI improves signal-to-noise ratio (SNR) and allows for radiologists to more easily detect lesions (Knake et al., 2005). The current state of 7T MRI allows for improved contrast to noise ratio, especially at the gray-white junction (Duyn et al., 2007). This modality has been shown to correlate well with microscopic pathology in hippocampal pathologies like sclerosis (Coras et al., 2014). But, few studies have tested the benefit of this technique and correlated with outcomes (Speck et al., 2009). As a result, even fewer studies have combined images obtained at 7T with other modalities of imaging. These image techniques should be able to capture features visible on histology such as cortical laminar disorganization and the presence of dysmorphic neurons with/without characteristic “balloon cells (BCs)” (Miyata et al., 2013).

现有的成像技术改进大大提高了对畸形的敏感性。例如，高场和超高场 MRI 中的相控阵线圈提高了信噪比(SNR) ，并使放射科医生更容易发现病变(Knake et al。 ，2005)。目前7T MRI 的状态允许改善对比噪声比，特别是在灰白交界处(Duyn et al。 ，2007)。这种模式已被证明与海马病理如硬化症的显微病理学良好相关(Coras 等，2014)。但是，很少有研究测试这种技术的好处并与结果相关(Speck et al。 ，2009)。因此，更少的研究将在7 t 时获得的图像与其他形式的成像结合起来。这些图像技术应该能够捕捉组织学上可见的特征，例如皮层层状紊乱和具有/不具有特征性“气球样细胞(BC)”的畸形神经元的存在(Miyata 等，2013)。

5.5. Next steps: gold standard metrics for lesion localization and quantification下一步: 病变定位和定量的黄金标准指标

The studies mentioned in this review differ in the way they approach quantifying accuracy of their methods. Currently, there is no gold standard metric to assess if a lesion has been correctly identified. In addition, there is no gold standard metric to quantify its extent. As a result, we cannot be sure if current methods are correctly detecting their target regions. In addition, if a lesion is detected, we cannot measure the accuracy of its estimated extent.

这篇综述中提到的研究在方法的量化准确性方面有所不同。目前，还没有金标准来评估病变是否被正确识别。此外，没有金本位标准来量化其程度。因此，我们不能确定当前的方法是否正确地检测了它们的目标区域。此外，如果发现病变，我们不能测量其估计范围的准确性。

In most studies, sensitivity is defined as the proportion of patients in which there is overlap between predicted lesions and surgical resection volume in patients. These are measured in a patient cohort with favorable outcomes (e.g. Engel I). There is no confirmatory histopathology or electrophysiology for the other “positive” findings in volumes that were not resected. Similarly, specificity is defined as the proportion of healthy controls in which the method failed to find any “false positive” lesion.

在大多数研究中，敏感性被定义为患者预测病变与手术切除体积重叠的患者比例。这些是在有良好结果的患者队列中测量的(例如恩格尔 I)。对于没有切除的其他“阳性”组织病理学，没有确切的电生理学。同样，特异性被定义为健康对照组中未能发现任何“假阳性”病变的比例。

In order to ensure standardization of these definitions, all patients should have exact lesion localization and seizure onset maps cross-validated across different neuroimaging modalities, surgical histopathology, surface/depth electrode recording and other clinical metadata. The full spatial extent of the lesion pathology should be accurately mapped to allow researchers to measure the volume overlap. These standardizations should be set as guidelines by the clinical leadership (e.g. similar to the ILAE histopathological classification system (Blümcke et al., 2011)).

为了确保这些定义的标准化，所有患者都应该有精确的病变定位和癫痫发作起始地图，并通过不同的神经影像学模式、手术组织病理学、表面/深部电极记录和其他临床元数据进行交叉验证。病变病理的全部空间范围应该被精确地绘制出来，使研究人员能够测量体积重叠。这些标准化应该由临床领导层制定为指导方针(例如类似于 ILAE 组织病理学分类系统(Blümcke et al。 ，2011))。

Any findings that do not overlap with these ground truth lesion and SOZ maps should be marked as false positives to get a better estimate of specificity. This allows specificity to be measured at the voxel-level versus at the weaker subject-level (where normal healthy controls are used as a test of specificity).

任何与这些地面病变和 SOZ 图不重叠的发现都应标记为假阳性，以获得更好的特异性估计。这使得特异性可以在体素水平与较弱的受试者水平(正常健康对照被用作特异性测试)进行测量。

5.6. Next steps: make computational pipelines available to clinicians5.6下一步: 让临床医生可以使用计算管道

Most current research methods use computational neuroimaging pipelines to preprocess data, compute features and input into a supervised or semi-supervised classifier. These pipelines are useful for researchers and clinicians alike. Researchers can use these pipelines to improve on others work and apply these methods to other patient cohorts. Clinicians can use these pipelines as part of the clinical decision workflow, thereby uncovering lesions that might have been missed by radiologists. Thus, it is important than any research study that builds such a pipeline makes the code easily available and usable for clinicians, who are often not fully versed in the technical details of the image computation. This will encourage clinicians to put these pipelines in their clinical practice and study its impact on patient outcome.

目前大多数研究方法使用计算神经影像管道来预处理数据、计算特征并输入到有监督或半监督的分类器中。这些管道对研究人员和临床医生都很有用。研究人员可以利用这些管道来改进其他人的工作，并将这些方法应用于其他患者群。临床医生可以使用这些管道作为临床决策工作流程的一部分，从而发现可能被放射科医生错过的病变。因此，建立这样一个流水线的研究比任何研究都重要，它使得代码对于临床医生来说更容易获得和使用，因为临床医生通常不完全精通图像计算的技术细节。这将鼓励临床医生将这些管道纳入他们的临床实习，并研究其对患者结果的影响。

5.7. Summary5.7摘要

In summary, this integrated data-sharing platform will serve as an adaptable and powerful platform for clinicians and computer scientists. We envision a future in which the use of such modular, clinically validated pipelines will become commonplace in epilepsy centers alongside the advances in imaging and electrophysiology. We strongly believe that this big data approach to semi-automating the detection of subtle lesions will be an important ingredient of next-generation computer vision breakthroughs in epilepsy neuroimaging.

总之，这个集成的数据共享平台将为临床医生和计算机科学家提供一个适应性强、功能强大的平台。我们设想在未来，随着成像和电生理学的进步，这种模块化的、经过临床验证的管道将在癫痫中心普及。我们坚信，这种大数据方法的半自动化检测微小病变将是下一代计算机视觉突破癫痫神经影像的重要组成部分。

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