Variational Field Constraint Learning for Degree of Coronary Artery Ischemia Assessment

The crucial role in diagnosing ischemic coronary artery disease is played by fractional flow reserve evaluation. Machine learning based fractional flow reserve evaluation has become the most important method due to it effectiveness and high computation efficiency. However, it still suffers from lacking of the proper description for the coronary artery fluid. This study presents a variational field constraint learning method for assessing fractional flow reserve from digital subtraction angiography images. Our method offers a promising approach by integrating governing equations and boundary conditions as unified constraints. Moreover, we also provide a multi-vessels neural network for the prediction of FFR for coronary artery. By leveraging a holistic consideration of the fluid dynamics, our method achieves more accurate fractional flow reserve prediction compared to existing methods. Our VFCLM is evaluated over 8000 virtual subjects produced by 1D hemodynamic models and 180 in-vivo cases. VFCLM achieves the MAE of 1.17 mmHg and MAPE of 1.20% for quantification.