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首页> 外文期刊>Psychiatry Research. Neuroimaging >Combination of dynamic C-11-PIB PET and structural MRI improves diagnosis of Alzheimer's disease
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Combination of dynamic C-11-PIB PET and structural MRI improves diagnosis of Alzheimer's disease

机译：动态C-11-PIB PET和结构MRI的组合可改善阿尔茨海默氏病的诊断

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摘要

Structural magnetic resonance imaging (sMRI) is an established technique for measuring brain atrophy, and dynamic positron emission tomography with C-11-Pittsburgh compound B (C-11-PIB PET) has the potential to provide both perfusion and amyloid deposition information, It remains unclear, however, how to better combine perfusion, amyloid deposition and morphological information extracted from dynamic C-11-PIB PET and sMRI with the goal of improving the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). We adopted a linear sparse support vector machine to build classifiers for distinguishing AD and MCI subjects from cognitively normal (CN) subjects based on different combinations of regional measures extracted from imaging data, including perfusion and amyloid deposition information extracted from early and late frames of C-11-PIB separately, and gray matter volumetric information extracted from sMRI data. The experimental results demonstrated that the classifier built upon the combination of imaging measures extracted from early and late frames of C-11-PIB as well as sMRI achieved the highest classification accuracy in both classification studies of AD (100%) and MCI (85%), indicating that multimodality information could aid in the diagnosis of AD and MCI. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
机译：结构磁共振成像（sMRI）是一种测量脑萎缩的成熟技术，使用C-11-Pittsburgh化合物B（C-11-PIB PET）进行的动态正电子发射断层扫描有可能提供灌注和淀粉样沉积信息，但是，如何更好地结合从动态C-11-PIB PET和sMRI提取的灌注，淀粉样蛋白沉积和形态学信息仍不清楚，目的是改善对阿尔茨海默氏病（AD）和轻度认知障碍（MCI）的诊断。我们采用线性稀疏支持向量机来构建分类器，以基于从成像数据中提取的区域度量的不同组合来区分AD和MCI受试者与认知正常（CN）受试者，包括从C早期和晚期帧中提取的灌注和淀粉样蛋白沉积信息分别使用-11-PIB，并从sMRI数据中提取灰质体积信息。实验结果表明，在从AD（100％）和MCI（85％）的分类研究中，基于从C-11-PIB的早期和晚期帧以及sMRI提取的成像手段的组合而建立的分类器实现了最高的分类精度），表明多模态信息可以帮助诊断AD和MCI。 （C）2015 Elsevier Ireland Ltd.保留所有权利。

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