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首页> 外文会议>IEEE International Symposium on Biomedical Imaging: From Nano to Macro >Ventricular maps in 804 subjects correlate with cognitive decline, CSF pathology, and imminent Alzheimer's disease
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Ventricular maps in 804 subjects correlate with cognitive decline, CSF pathology, and imminent Alzheimer's disease

机译：804名受试者的心室图与认知能力下降，CSF病理和即将到来的阿尔茨海默氏病相关

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There is an urgent need for neuroimaging biomarkers of Alzheimer's disease (AD) that correlate with cognitive decline, and with accepted measures of pathology detectable in cerebrospinal fluid (CSF). Ideal biomarkers should also be able to predict future decline, and should be computable automatically from hundreds to thousands of images without user intervention. Here we used our multi-atlas fluid image alignment method (MAFIA [1]), to automatically segment parametric 3D surface models of the lateral ventricles in brain MRI scans from 184 AD, 391 MCI, and 229 healthy elderly controls. Radial expansion of the ventricles, computed pointwise, was correlated with measures of (1) clinical decline, (2) pathology from CSF, and (3) future deterioration. Surface-based correlation maps were assessed using a cumulative distribution function method to rank influential covariates according to their effect sizes. The resulting approach is highly automated, and boosts the power of fluid image registration by integrating multiple independent registrations to reduce segmentation errors.
机译：迫切需要阿尔茨海默氏病（AD）的神经影像生物标记物，该标记物与认知能力下降以及在脑脊液（CSF）中可检测到的病理学指标有关。理想的生物标记物还应该能够预测未来的下降趋势，并且应该可以自动计算成百上千的图像，而无需用户干预。在这里，我们使用了多图册流体图像对齐方法（MAFIA [1]），在184 AD，391 MCI和229名健康老年人的大脑MRI扫描中自动分割了侧脑室的参数3D表面模型。逐点计算心室的径向扩张与以下方面的测量结果相关：（1）临床下降，（2）CSF病理以及（3）未来恶化。使用累积分布函数方法评估基于表面的相关图，以根据其影响大小对有影响的协变量进行排名。最终的方法是高度自动化的，并且通过集成多个独立的配准来减少分割错误，从而增强了流体图像配准的能力。

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