Finite element modeling and simulation of the implantation of braided stent to treat cerebral aneurysm

Manufacturing, Processing, and Characterization of Self-Expanding Metallic Stents: A Comprehensive Review

This paper aims to review the State of the Art in metal self-expanding stents made from nitinol (NiTi), showing shape memory and superelastic behaviors, to identify the challenges and the opportunities for improving patient outcomes. A significant contribution of this paper is its extensive coverage of multidisciplinary aspects, including design, simulation, materials development, manufacturing, bio/hemocompatibility, biomechanics, biomimicry, patency, and testing methodologies. Additionally, the paper offers in-depth insights into the latest practices and emerging trends, with a special emphasis on the transformative potential of additive manufacturing techniques in the development of metal stents. By consolidating existing knowledge and highlighting areas for future innovation, this review provides a valuable roadmap for advancing nitinol stents.

Finite element analysis of braided dense-mesh stents for carotid artery stenosis

When braided dense-mesh stents are used to treat carotid stenosis, the structural mechanics of vascular stents, the contact mechanics with blood vessels, and the fluid mechanics in the blood environment need to be studied in depth to reduce the damage of stents to blood vessels and the incidence of in-stent restenosis. Three types of braided stents with 8, 16, and 24 strands and laser-cut stents with the corresponding size parameters were designed, and the bending behavior of each of these types of stent, deployment, and fluid dynamic analysis of the 24-strand braided stent were simulated. The results show that the bending stress of the 8-, 16-, and 24-strand braided stents is 46.33%, 50.24%, and 31.86% of that of their laser-cut counterparts. In addition, higher strand density of the braided stents was associated with greater bending stress; after the 24-strand braided stent was expanded within the stented carotid artery, the carotid stenosis rate was reduced from 81.52% to 46.33%. After stent implantation, the maximum stress on the vessel wall in a zero-pressure diastolic environment decreased from 0.34 to 0.20 MPa, the maximum pressure on the intravascular wall surface decreased from 4.89 to 3.98 kPa, the area of high-pressure region decreased, the wall shear force of the stenotic segment throat decreased, and blood flow increased in the stenosis segments. The braided stent had less bending stress and better flexibility than the laser-cut stent under the same stent size parameters; after the 24-strand braided stent was implanted into the stented vessel, it could effectively dilate the vessel, and the blood flow status was improved.

Machine learning and reduced order modelling for the simulation of braided stent deployment

Endoluminal reconstruction using flow diverters represents a novel paradigm for the minimally invasive treatment of intracranial aneurysms. The configuration assumed by these very dense braided stents once deployed within the parent vessel is not easily predictable and medical volumetric images alone may be insufficient to plan the treatment satisfactorily. Therefore, here we propose a fast and accurate machine learning and reduced order modelling framework, based on finite element simulations, to assist practitioners in the planning and interventional stages. It consists of a first classification step to determine a priori whether a simulation will be successful (good conformity between stent and vessel) or not from a clinical perspective, followed by a regression step that provides an approximated solution of the deployed stent configuration. The latter is achieved using a non-intrusive reduced order modelling scheme that combines the proper orthogonal decomposition algorithm and Gaussian process regression. The workflow was validated on an idealized intracranial artery with a saccular aneurysm and the effect of six geometrical and surgical parameters on the outcome of stent deployment was studied. We trained six machine learning models on a dataset of varying size and obtained classifiers with up to 95% accuracy in predicting the deployment outcome. The support vector machine model outperformed the others when considering a small dataset of 50 training cases, with an accuracy of 93% and a specificity of 97%. On the other hand, real-time predictions of the stent deployed configuration were achieved with an average validation error between predicted and high-fidelity results never greater than the spatial resolution of 3D rotational angiography, the imaging technique with the best spatial resolution (0.15 mm). Such accurate predictions can be reached even with a small database of 47 simulations: by increasing the training simulations to 147, the average prediction error is reduced to 0.07 mm. These results are promising as they demonstrate the ability of these techniques to achieve simulations within a few milliseconds while retaining the mechanical realism and predictability of the stent deployed configuration.

Structural Design of Vascular Stents: A Review

Percutaneous Coronary Intervention (PCI) is currently the most conventional and effective method for clinically treating cardiovascular diseases such as atherosclerosis. Stent implantation, as one of the ways of PCI in the treatment of coronary artery diseases, has become a hot spot in scientific research with more and more patients suffering from cardiovascular diseases. However, vascular stent implanted into vessels of patients often causes complications such as In-Stent Restenosis (ISR). The vascular stent is one of the sophisticated medical devices, a reasonable structure of stent can effectively reduce the complications. In this paper, we introduce the evolution, performance evaluation standards, delivery and deployment, and manufacturing methods of vascular stents. Based on a large number of literature pieces, this paper focuses on designing structures of vascular stents in terms of “bridge (or link)” type, representative volume unit (RVE)/representative unit cell (RUC), and patient-specific stent. Finally, this paper gives an outlook on the future development of designing vascular stents.