A Rapid and Computationally Inexpensive Method to Virtually Implant Current and Next-Generation Stents into Subject-Specific Computational Fluid Dynamics Models
Computational modeling is often used to quantify hemodynamic alterations induced by stenting, but frequently uses simplified device or vascular representations. Based on a series of Boolean operations, we developed an efficient and robust method for assessing the influence of current and next-generation stents on local hemodynamics and vascular biomechanics quantified by computational fluid dynamics. Stent designs were parameterized to allow easy control over design features including the number, width and circumferential or longitudinal spacing of struts, as well as the implantation diameter and overall length. The approach allowed stents to be automatically regenerated for rapid analysis of the contribution of design features to resulting hemodynamic alterations. The applicability of the method was demonstrated with patient-specific models of a stented coronary artery bifurcation and basilar trunk aneurysm constructed from medical imaging data. In the coronary bifurcation, we analyzed the hemodynamic difference between closed-cell and open-cell stent geometries. We investigated the impact of decreased strut size in stents with a constant porosity for increasing flow stasis within the stented basilar aneurysm model. These examples demonstrate the current method can be used to investigate differences in stent performance in complex vascular beds for a variety of stenting procedures and clinical scenarios.
KeywordsComputational fluid dynamics Coronary artery disease Cerebral aneurysm Shear stress Numerical modeling
This work is supported by a Translational Opportunity Grant of the Pilot and Collaborative Clinical and Translational Research Grants program from the Clinical and Translational Science Institute of Southeastern Wisconsin and computational support for this work was made possible by NSF grants CTS-0521602 and OCI-0923037. Dr. Bon-Kwon Koo is the recipient of a research grant from the CardioVascular Research Foundation (CVRF), Korea. The authors recognize Nathan Wilson Ph.D. of Open Source Medical Software Corporation for technical assistance.
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