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基于表面的 MRI 形态测量学检测致痫性皮质畸形。

Detection of epileptogenic cortical malformations with surface-based MRI morphometry.

机构信息

Comprehensive Epilepsy Center, Department of Neurology, New York University, New York, New York, United States of America.

出版信息

PLoS One. 2011 Feb 4;6(2):e16430. doi: 10.1371/journal.pone.0016430.

Abstract

Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the 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. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from surgery.

摘要

磁共振成像技术已经彻底改变了癫痫患者结构性异常的检测方式。然而,在常规的目视检查中,许多局灶性异常仍然无法被发现。在这里,我们使用一种自动化的、基于表面的方法来量化与致痫性皮质畸形相关的形态学特征,以检测异常的皮质厚度和模糊的灰白质边界。我们使用 3T MRI 形态测量学和基于表面的球形平均技术,精确地将个体大脑之间的解剖结构对齐,将已知病变的单个患者与大型正常对照组进行比较,以检测异常皮质厚度、灰白质对比度、局部脑回、脑沟深度、雅可比距离和曲率的聚类。为了评估阈值和平滑对检测灵敏度和特异性的影响,我们系统地改变了这些参数,采用不同的阈值和平滑水平。为了评估该技术检测致痫性病变的有效性,我们将检测到的结构异常与专家追踪、颅内 EEG、病理和手术结果在同质的患者样本中进行了比较。通过优化参数并结合厚度和 GWC,基于表面的检测方法以 92%的敏感性(很少出现假阳性)识别出 92%的皮质病变(特异性),成功地将患者与对照区分开,准确率为 94%。检测到的结构异常与癫痫发作区、异常组织学和所有手术患者的阳性结果有关。然而,该方法在大多数情况下无法充分描述病变范围。如果使用优化的参数,自动化的基于表面的 MRI 形态测量可能是一种有价值的附加临床工具,可以提高对细微或先前隐匿性畸形的检测能力,从而可以更准确地识别出可能受益于手术的难治性局灶性癫痫患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaf0/3033882/46d6c362bb11/pone.0016430.g001.jpg

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