标题： 基于物理信息的自编码器的动脉瘤生长时间序列重建 显示英文标题

标题： Aneurysm Growth Time Series Reconstruction Using Physics-informed Autoencoder

摘要： 动脉瘤（图1）是人体动脉的局部膨胀，其破裂是美国发病率和死亡率的主要原因。因此，预测动脉瘤破裂对动脉瘤的管理和治疗选择具有重要意义。动脉瘤破裂的预测依赖于动脉瘤生长历史的时间序列分析。然而，由于动脉瘤生长的时间尺度较长，动脉瘤生长的时间序列并不总是可获取的。我们在此提出了一种方法，可以直接从患者参数重建动脉瘤生长时间序列。预测基于[患者参数，患者动脉瘤生长时间历史]的数据对。为了获得从患者参数到患者动脉瘤生长时间历史的映射，我们首先应用自编码器来获得每个患者的时序数据的紧凑表示。然后通过一个五层神经网络学习从患者参数到相应时序数据的紧凑表示的映射。移动平均和卷积输出层被实现以显式考虑时序数据的时间依赖性。除此之外，我们还提出利用关于动脉瘤生长机制的先验知识来改善时序数据的重建结果。基于物理的先验知识作为与自编码器相关的优化问题的约束条件。该模型可以处理代数和微分约束。我们的结果表明，如果训练数据没有误差，包含关于数据的物理模型信息不会显著改善时序数据的重建结果。然而，在训练数据存在噪声和偏差误差的情况下，包含物理模型约束可以显著改善预测的时序数据。 显示英文摘要

摘要： Arterial aneurysm (Fig.1) is a bulb-shape local expansion of human arteries, the rupture of which is a leading cause of morbidity and mortality in US. Therefore, the prediction of arterial aneurysm rupture is of great significance for aneurysm management and treatment selection. The prediction of aneurysm rupture depends on the analysis of the time series of aneurysm growth history. However, due to the long time scale of aneurysm growth, the time series of aneurysm growth is not always accessible. We here proposed a method to reconstruct the aneurysm growth time series directly from patient parameters. The prediction is based on data pairs of [patient parameters, patient aneurysm growth time history]. To obtain the mapping from patient parameters to patient aneurysm growth time history, we first apply autoencoder to obtain a compact representation of the time series for each patient. Then a mapping is learned from patient parameters to the corresponding compact representation of time series via a five-layer neural network. Moving average and convolutional output layer are implemented to explicitly taking account the time dependency of the time series. Apart from that, we also propose to use prior knowledge about the mechanism of aneurysm growth to improve the time series reconstruction results. The prior physics-based knowledge is incorporated as constraints for the optimization problem associated with autoencoder. The model can handle both algebraic and differential constraints. Our results show that including physical model information about the data will not significantly improve the time series reconstruction results if the training data is error-free. However, in the case of training data with noise and bias error, incorporating physical model constraints can significantly improve the predicted time series.