Computational modelling of cardiac perfusion to guide Percutaneous Coronary Intervention: a treatment planning tool

Objective: treatment of obstructive coronary artery disease (CAD) requires accurate planning to ensure effective revascularization and full restoration of myocardial perfusion. In this study, we introduce Virtual PCI, a novel computational tool designed to support pre-operative planning of Percutaneous Coronary Intervention (PCI) by predicting the hemodynamic consequences of selected revascularization treatments. Methods: the tool leverages a fully personalized 3D multiscale perfusion model, calibrated using pre-intervention stress CT perfusion (CTP) imaging, to simulate the hemodynamic impact of different revascularization strategies in terms of post-intervention stress myocardial blood flow (MBF) and FFR. The computational framework is also capable of computing the FFR index. We conduct a validation study on patients treated with elective PCI and compare model predictions with dynamic stress CTP at follow-up. Results: the validation study demonstrates high accuracy in predicting post-PCI myocardial perfusion, including potential residual ischemia and cardiac mass at ischemic risk. Through an integrated analysis with FFR, the tool shows potential for its prospective use, identifying in two patients optimal treatment strategies and, in one case, outperforming the executed revascularization in reduction of ischemic burden. Conclusions: Virtual PCI enables the prediction of post-PCI myocardial blood flow (MBF) and FFR, offering a comprehensive assessment of treatment outcomes to identify the best revascularization option from the hemodynamic standpoint. Significance: since it relies solely on non-invasive imaging (cCTA, stress-CTP), Virtual PCI can be integrated early in the diagnostic workflow, providing cardiologists with a powerful, patient-specific tool to optimize PCI planning.