论文标题
从多视图血管造影序列的心脏周期同步的端到端深度学习模型
End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences
论文作者
论文摘要
冠状动脉的动态重建（3D+T）可以为临床医生提供重要的灌注细节。不同视图的时间匹配，可能不会同时获得，这是对冠状节段进行准确立体匹配的先决条件。在本文中，我们展示了如何使用RAW X射线血管造影视频在心脏周期期间从血管造影序列进行训练，以同步不同的视图。首先，我们使用具有血管造影序列的神经网络模型来提取描述心脏周期发展的特征。然后，我们将每个帧的特征向量之间的距离与从第二视图中的那些视图一起生成显示条纹模式的距离图。使用探路，我们在两个视频的每个帧之间提取最佳的时间相干关联。最后，我们将评估集的同步帧与心电图信号进行比较，以显示为96.04％精度的对齐。
Dynamic reconstructions (3D+T) of coronary arteries could give important perfusion details to clinicians. Temporal matching of the different views, which may not be acquired simultaneously, is a prerequisite for an accurate stereo-matching of the coronary segments. In this paper, we show how a neural network can be trained from angiographic sequences to synchronize different views during the cardiac cycle using raw x-ray angiography videos exclusively. First, we train a neural network model with angiographic sequences to extract features describing the progression of the cardiac cycle. Then, we compute the distance between the feature vectors of every frame from the first view with those from the second view to generate distance maps that display stripe patterns. Using pathfinding, we extract the best temporally coherent associations between each frame of both videos. Finally, we compare the synchronized frames of an evaluation set with the ECG signals to show an alignment with 96.04% accuracy.
