Conditional Physics-Informed Graph Neural Network for Fractional Flow Reserve Assessment

Baihong Xie, Xiujian Liu, Heye Zhang, Chenchu Xu, Tieyong Zeng, Yixuan Yuan, Guang Yang, Zhifan Gao

Abstract: The assessment of fractional flow reserve (FFR) is significant for diagnosing coronary artery disease and determining the patients and lesions in need of revascularization. Deep learning has become a promising approach for the assessment of FFR, due to its high computation efficiency in contrast to computational fluid dynamics. However, it suffers from the lack of appropriate priors. The current study only considers adding priors into the loss function, which is insufficient to learn features having strong relationships with the boundary conditions. In this paper, we propose a conditional physics-informed graph neural network (CPGNN) for FFR assessment under the morphology and boundary condition information. Specially, CPGNN adds morphology and boundary conditions into inputs to learn the conditioned features and penalizes the residual of physical equations and the boundary condition in the loss function. Additionally, CPGNN consists of a multi-scale graph fusion module (MSGF) and a physics-informed loss. MSGF is to generate the features constrained by the coronary topology and better represent the different-range dependence. The physics-informed loss uses the finite difference method to calculate the residuals of physical equations. Our CPGNN is evaluated over 183 real-world coronary observed from 143 X-ray and 40 CT angiography. The FFR values of CPGNN correlate well with FFR measurements r = 0.89 in X-ray and r = 0.88 in CT.