Numerical simulation of stent deployment within patient-specific artery and its validation against clinical data

Abstract

© 2019 Elsevier B.V. Background and objective: One of the most widely adopted endovascular treatment procedures is the stent implantation. The effectiveness of the treatment depends on the appropriate stent expansion. However, it is difficult to accurately predict the outcome of such an endovascular intervention. Numerical simulations represent a useful tool to study the complex behavior of the stent during deployment. This study presents a numerical model capable of simulating this process. Methods: The numerical model consists of three parts: modeling of stent expansion, modeling the interaction of the stent with the arterial wall and the deformation of the arterial wall. The model is able to predict the shapes of both stent and arterial wall during the entire deployment process. Simulations are performed using patient-specific clinical data that ensures more realistic results. Results: The numerical simulations of stent deployment are performed using the extracted geometry of the coronary arteries of two patients. The obtained results are validated against clinical data from the follow up examination and both quantitative and qualitative analysis of the results is presented. The areas of several slices of the arterial wall are calculated for all the three states (before, after and follow up) and the standard error of the area when comparing simulation and follow up examination is 5.27% for patient #1 and 4.5% for patient #2. Conclusions: The final goal of numerical simulations in stent deployment should be to provide a clinical tool that is capable of reliably predicting the treatment outcome. This study showed through the good agreement of results of the numerical simulations and clinical data that the presented numerical model represents a step towards this final goal. These simulations can also provide valuable information about distribution of forces and stress in the arterial wall that can improve pre-operative planning and treatment optimization.