The qualitative and rapid assessment of atherosclerotic lesions is still a challenging task. The primary therapy for this pathology involves implanting coronary stents, which help restore blood flow in atherosclerosis-prone arteries. In-stent restenosis is a stenting procedure complication detected in about 10-40% of patients. A numerical study using two-way fluid–solid interaction (FSI) assessed the effectiveness of stenting and was able to reduce the number of complications. Nevertheless, the boundary conditions (BCs) used in the simulation play a crucial role in the implementation of an adequate computational analysis. Three CoCr stent designs were modeled with the suggested approach. The artery–plaque system was modeled as a multi-layer structure with anisotropic hyperelastic mechanical properties. Two kinds of boundary conditions for the solid domain were examined—fixed support (FS) and remote displacement (RD)—to assess their impact on hemodynamic parameters to predict restenosis. Additionally, the influence of artery elongation (short-artery model vs. long-artery model) on the numerical results with the FS boundary conditions was analyzed. A comparison of the FS and RD boundary conditions demonstrated that the variation in the hemodynamic parameter values did not exceed 2%. An analysis of the short-artery and long-artery models revealed that the difference in hemodynamic parameters was less than 5.1%, and in most cases, it did not exceed 2.5%. The RD boundary conditions were found to reduce the computation time by up to 1.7–2.0 times compared to the FS boundary conditions. This study revealed that the stent design significantly affected hemodynamic parameters as restenosis predictors. Moreover, the stress–strain state of the artery–plaque–stent system also depends on the proper choice of boundary conditions.

The authors thank the Ministry of Science and Higher Education of the Russian Federation for their financial assistance within the framework of the state assignment for 18 performing fundamental scientific research (FSNM-2023-0003 project).