Center for Molecular Imaging Technology and Translation (CMITT) Administrative Supplement #4

摘要 阿尔茨海默病(AD)的特征是细胞内的存在和分布 神经原纤维缠绕Tau和细胞外淀粉样蛋白-β。陶氏病的病理与 阿尔茨海默病的认知功能下降具有地区性特点。预测牛磺酸的变化将是非常有价值的 如果给出基线的tau PET测量，负担将在未来分散。这种方法可以 可用于预测各地区的tau负担随年龄的变化情况，从而进行分层 基于他们的进展轨迹，潜在的AD治疗候选者。它也可以用来 为了确定治疗后tau PET分布与预测分布的不同程度 如果没有应用任何治疗方法。最近可获得的纵向tau PET数据为我们提供了一个独特的 技术发展的机遇：使用最先进的深度对tau传播进行建模 学习方法。然而，当前可用的纵向tau PET数据集相对较小， 因此，不足以训练一个深度神经网络。为了解决这个问题，我们提出了一种新的 将简单的数学模型结合到深层神经训练中的方法 网络。我们首先开发了一个简单的网络扩散模型，该模型适用于部分可用的 纵向tau PET数据。然后，我们生成大量的纵向tau PET数据集 利用该模型对U-Net式自动编码器深度神经网络进行了预训练。最后，我们进一步 通过冻结所有参数训练神经网络，但与 神经网络的瓶颈层。这种方法使得对tau传播进行建模成为可能 直接从测量的纵向tau PET数据中获得数据，同时避免因不足而导致的过拟合 训练数据。

Abstract Alzheimer's Disease (AD) is characterized by the presence and distribution of intracellular neurofibrillary tangles tau and extracellular amyloid-β. Tau pathology is deeply associated with cognitive decline in AD in a region-specific manner. It will be highly valuable to predict how tau burden would spread in the future given a baseline tau PET measurement. Such approach can be used to predict how region-wise tau burden changes versus age and, therefore, stratify potential candidates for AD therapy based on their “trajectory” of progression. It can also be used to determine how different a tau PET distribution is after a therapy from the predicted distribution if no therapy is applied. The recent availability of longitudinal tau PET data provides us a unique opportunity for technology development: to model tau-propagation using state-of-the-art deep learning methods. However, current available longitudinal tau PET datasets are relatively small, therefore, insufficient to train a deep neural network. To solve this problem, we propose a novel approach that incorporates a simple mathematical model in the training of the deep neural network. We first develop a simple network diffusion model that fits part of the available longitudinal tau PET data. We then generate a very large number of longitudinal tau PET datasets using the fitted model to pretrain a U-net-like autoencoder deep neural network. Finally, we further train the neural network by freezing all the parameters except those directly associated with the bottleneck layer of the neural network. This approach makes it possible to model tau propagation directly from measured longitudinal tau PET data while avoid overfitting caused by insufficient training data.