Deep graph convolutional neural network for one-dimensional hepatic vascular haemodynamic prediction

Abstract

Hepatic vascular hemodynamics is an important reference indicator in the diagnosis and treatment of hepatic diseases. However, Method based on Computational Fluid Dynamics(CFD) are difficult to promote in clinical applications due to their computational complexity. To this end, this study proposed a deep graph neural network model to simulate the one-dimensional hemodynamic results of hepatic vessels. By connecting residuals between edges and nodes, this framework effectively enhances network prediction accuracy and efficiently avoids over-smoothing phenomena. The graph structure constructed from the centerline and boundary conditions of the hepatic vasculature can serve as the network input, yielding velocity and pressure information corresponding to the centerline. Experimental results indicate that our proposed method achieves higher accuracy on a hepatic vasculature dataset with significant individual variations and can be extended to applications involving other blood vessels. Following training, errors in both the velocity and pressure fields are maintained below 1.5%. The trained network model can be easily deployed on low-performance devices and, compared to CFD-based methods, can output velocity and pressure along the hepatic vessel centerline at a speed three orders of magnitude faster. Author summaryWhen using deep learning methods for hemodynamic analysis, simple point cloud data cannot express the real geometric structure of the blood vessels, and it is necessary for the network to have additional geometric information extraction capability. In this paper, we use graph structure to express the structure of hepatic blood vessels, and deep graph neural network to predict the corresponding hemodynamic parameters. The graph structure can effectively express the geometric information of hepatic blood vessels and the topology of branch blood vessels, which can effectively improve the prediction accuracy with strong geometric generalisation ability. The results show that the method achieves the highest prediction accuracy in the one-dimensional hepatic vessel blood flow simulation dataset, and the experimental results on the human aorta also show that our method can be effectively applied to the blood flow simulation of other vascular organs.