Computational thrombosis modeling based on multiphase porous media theory for prognostic evaluation of aortic dissection after stenting

Abstract

Accurately and rapidly predicting the occurrence and progression of false lumen thrombosis in patients undergoing thoracic endovascular aortic repair (TEVAR) is crucial for optimizing patient recovery. Traditional models for predicting false lumen thrombosis often lack the ability to capture phase interface changes, and their complex parameters and algorithms result in a long computation time. This study introduces a multiphase porous media approach that can accurately and rapidly predict thrombus formation in aortic dissection patients at different postoperative stages. The approach employed the Darcy–Brinkman–Stokes equation to model the interaction between the thrombotic and fluid phases and incorporated a novel porosity equation to explicitly capture phase interface dynamics. Additionally, the hemodynamic parameters associated with thrombus formation were updated to enhance the physical accuracy of the algorithm while reducing its computational complexity. Using patient-specific models derived from computed tomography angiography datasets, our algorithm demonstrated excellent predictive performance in real patients. The predicted thrombus morphology in the third and sixth months postoperatively closely matched the actual imaging data, with discrepancies in thrombus volume remaining within a ±10% range at each postoperative stage. Moreover, the algorithm significantly improved computational convergence, reducing the computation time to 30 minutes and enhancing the computational efficiency by 80% compared to traditional methods. By integrating the porous media framework, this approach offers a valuable tool for rapid clinical diagnosis and the prediction of post-TEVAR recovery.