用于时间分辨血管造影对比剂浓度重建的物理信息学习方法
Physics-Informed Learning for Time-Resolved Angiographic Contrast Agent Concentration Reconstruction
AI总结：
针对4D-DSA血流重建的不适定问题，提出融合流体动力学信息的神经网络模型，在血流模拟数据集上训练后可高效重建对比剂浓度，精度高且对血管重叠、缩短鲁棒，验证了机器学习与血流模拟结合的应用潜力。
摘要：
三维数字减影血管造影（3D-DSA）是一种成熟的基于X射线的血管解剖可视化技术。近年来，四维DSA（4D-DSA）重建算法被开发出来，可通过体积时间序列实现对比剂容积流动动力学的可视化。该重建问题是不适定的，主要原因是投影方向上的血管重叠以及血管几何缩短，这会导致记录的投影图像出现信息损失。不过，我们可以利用底层流体动力学的相关知识来约束解空间。在本研究中，我们将该信息隐含地融入基于神经网络的模型中，该模型在基于图像的血流模拟数据集上进行训练。模型可预测血管系统各中心线点随时间变化的空间平均对比剂浓度，从而降低整体计算需求。经过训练的网络能够重建相对对比剂浓度，其平均绝对误差为0.02±0.02，平均绝对百分比误差为5.31%±9.25%。此外，该网络对不同程度的血管重叠和血管缩短具有鲁棒性。我们的方法证明了机器学习与血流模拟相结合在时间分辨血管造影血流重建中的潜力。
英文摘要：
Three-dimensional Digital Subtraction Angiography (3D-DSA) is a well-established X-ray-based technique for visualizing vascular anatomy. Recently, four-dimensional DSA (4D-DSA) reconstruction algorithms have been developed to enable the visualization of volumetric contrast flow dynamics through time-series of volumes. . This reconstruction problem is ill-posed mainly due to vessel overlap in the projection direction and geometric vessel foreshortening, which leads to information loss in the recorded projection images. However, knowledge about the underlying fluid dynamics can be leveraged to constrain the solution space. In our work, we implicitly include this information in a neural network-based model that is trained on a dataset of image-based blood flow simulations. The model predicts the spatially averaged contrast agent concentration for each centerline point of the vasculature over time, lowering the overall computational demand. The trained network enables the reconstruction of relative contrast agent concentrations with a mean absolute error of 0.02 $\pm$ 0.02 and a mean absolute percentage error of 5.31 % $\pm$ 9.25 %. Moreover, the network is robust to varying degrees of vessel overlap and vessel foreshortening. Our approach demonstrates the potential of the integration of machine learning and blood flow simulations in time-resolved angiographic flow reconstruction.
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