Background: The averaged cortical thickness of meta-ROI is currently being used for the diagnosis and prognosis of Alzheimer’s disease (AD) using structural MRI brain images. The purpose of this work is to present a hybrid meta-ROI for the detection of AD. Methods: The AD detectability of selected cortical and volumetric regions of the brain was examined using signal detection theory. The top performing cortical and volumetric ROIs were taken as input nodes to the artificial neural network (ANN) for AD classification. Results: An AD diagnostic accuracy of 91.9% was achieved by using a hybrid meta-ROI consisting of thicknesses of entorhinal and middle temporal cortices, and the volumes of the hippocampus and inferior lateral ventricles. Pairing inferior lateral ventricle dilation with hippocampal volume reduction improves AD detectability by 5.1%. Conclusions: Hybrid meta-ROI, including the dilation of inferior lateral ventricles, outperformed both cortical thickness- and volumetric-based meta-ROIs in the detection of Alzheimer’s disease.
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机译：背景：meta-ROI 的平均皮质厚度目前用于使用结构 MRI 脑图像诊断和预测阿尔茨海默病 （AD）。这项工作的目的是提出一种用于检测 AD 的混合元 ROI。方法： 使用信号检测理论检查大脑选定皮层和体积区域的 AD 可检测性。表现最好的皮层和体积 ROI 被作为人工神经网络 （ANN） 的输入节点进行 AD 分类。结果： 通过使用由内嗅和中颞叶皮层的厚度以及海马体和下侧脑室的体积组成的混合 meta-ROI，实现了 91.9% 的 AD 诊断准确率。将下侧脑室扩张与海马体积减少相结合，可将 AD 检测率提高 5.1%。结论： 混合 meta-ROI，包括下侧脑室的扩张，在检测阿尔茨海默病方面优于基于皮质厚度和体积的 meta-ROIs。
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