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首页> 外文会议>Conference on Computer-Aided Diagnosis >Application of Probabilistically-Weighted Graphs to Image-Based Diagnosis of Alzheimer's Disease using Diffusion MRI
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Application of Probabilistically-Weighted Graphs to Image-Based Diagnosis of Alzheimer's Disease using Diffusion MRI

机译：概率加权图在弥散核磁共振成像基于图像的阿尔茨海默氏病诊断中的应用

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The world's aging population has given rise to an increasing awareness towards neurodegenerative disorders, including Alzheimers Disease (AD). Treatment options for AD are currently limited, but it is believed that future success depends on our ability to detect the onset of the disease in its early stages. The most frequently used tools for this include neuropsychological assessments, along with genetic, proteomic, and image-based diagnosis. Recently, the applicability of Diffusion Magnetic Resonance Imaging (dMRI) analysis for early diagnosis of AD has also been reported. The sensitivity of dMRI to the microstructural organization of cerebral tissue makes it particularly well-suited to detecting changes which are known to occur in the early stages of AD. Existing dMRI approaches can be divided into two broad categories: region-based and tract-based. In this work, we propose a new approach, which extends region-based approaches to the simultaneous characterization of multiple brain regions. Given a predefined set of features derived from dMRI data, we compute the probabilistic distances between different brain regions and treat the resulting connectivity pattern as an undirected, fully-connected graph. The characteristics of this graph are then used as markers to discriminate between AD subjects and normal controls (NC). Although in this preliminary work we omit subjects in the prodromal stage of AD, mild cognitive impairment (MCI), our method demonstrates perfect separability between AD and NC subject groups with substantial margin, and thus holds promise for fine-grained stratification of NC, MCI and AD populations.
机译：世界人口老龄化引起人们对包括阿兹海默氏病（AD）在内的神经退行性疾病的认识日益提高。目前，AD的治疗选择有限，但人们认为，未来的成功取决于我们在早期发现疾病的能力。最常用的工具包括神经心理学评估以及遗传，蛋白质组和基于图像的诊断。最近，已经报道了扩散磁共振成像（dMRI）分析在AD的早期诊断中的适用性。 dMRI对脑组织的微结构的敏感性使其特别适合检测已知在AD早期发生的变化。现有的dMRI方法可分为两大类：基于区域的方法和基于道的方法。在这项工作中，我们提出了一种新方法，该方法将基于区域的方法扩展到多个大脑区域的同时表征。给定从dMRI数据获得的一组预定义特征，我们将计算不同大脑区域之间的概率距离，并将得到的连通性模式视为无向，完全连通的图形。然后将此图的特征用作标记，以区分AD受试者和正常对照（NC）。尽管在这项前期工作中，我们省略了处于AD前驱阶段的受试者，即轻度认知障碍（MCI），但我们的方法证明了AD和NC受试者组之间的完美可分离性，具有可观的优势，因此有望为NC，MCI的细粒度分层和广告人群。

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