北京生物医学工程

脑部 PET 图像在阿尔茨海默病早期诊断中的应用

Application of brain PET images in the early diagnosis of Alzheimer's disease

作者：林万云杜民

单位：福州大学物理与信息工程学院（福州　350108 ）福州大学福建省医疗器械和医药技术重点实验室（福州　350108 ）

关键词：阿尔茨海默病； ADNI数据库； PET图像； MR图像； 3D CNN；早期诊断

分类号：R318

出版年·卷·期（页码）：2021·40·2（174-180）

摘要：

目的本研究使用脑部正电子发射型计算机断层显像（positron emission tomography, PET），并且设计了一个3D 卷积神经网络（convolutional neural networks, CNN），以实现对阿尔茨海默病（Alzheimer disease, AD）的早期诊断。方法研究数据取自美国国立卫生研究院老年研究所的ADNI (Alzheimer’s Disease Neuroimaging Initiative)数据库，PET图像和磁共振（magnetic resonance，MR）图像均有收集并对数据进行相关预处理。为避免过早的下采样给模型性能带来不利的影响，设计了一个3D CNN模型，比较两种不同模态的数据在AD早期诊断中各自的优缺点。结果使用本研究组设计的3D CNN模型在基于PET图像的AD早期诊断实验中，预测准确率、敏感度、特异度以及曲线下面积（area under curve，AUC）分别达到71.19%、79.29%、61.35%、71.09%，各项指标均大于使用相同模型但是使用MRI图像时的实验结果。此外，对本研究组的模型与计算机视觉中的经典模型VGG和ResNet使用相同数据进行对比实验，在许多评价指标上都要优于这两个对比模型。结论使用脑部PET图像并结合3D CNN可以更好的利用3D图像的空间位置信息，更有效的提取特征，能对AD早期的病变情况有更准确高效的识别，有助于及时发现疾病并采取措施减缓病情，降低发病概率或推迟发病时间。

Objective In this study, we used positron emission tomography (PET) of the brain and designed a 3D convolutional neural networks (CNN) to achieve early diagnosis of Alzheimer's disease (AD). Methods Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Both PET images and magnetic resonance (MR) images were collected and preprocessed. In order to avoid premature downsampling from affecting the model performance. We designed a 3D CNN model to compare the advantages and disadvantages of two different modal data in the early diagnosis of AD. Results Using our designed 3D CNN model in the early diagnosis experiment of AD based on PET images, the prediction accuracy, sensitivity, specificity and the area under curve (AUC) reached 71.19%, 79.29%, 61.35% and 71.09%, respectively. All indicators are greater than the experimental results when using the same model but using MRI images. Compared with VGG and ResNet, our model is better than these two models in many evaluation indicators. Conclusions Combining brain PET images and 3D CNN can make better use of the spatial position information of 3D images, extract features more effectively, and can more accurately and efficiently identify the pathological changes in the early stage of AD, which is helpful for timely detection of diseases. It can slow down the disease, reduce the probability of disease and delay the time of disease.

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