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

Cai, Yangchuan; Meng, Zhuangyuan; Jiang, Yuping; Zhang, Xiaolong; Yang, Xinjian; Wang, Shengzhang

doi:10.1016/j.medntd.2020.100031

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…(2014) ]. However, some preliminary comparisons on idealised geometries highlighted the presence of differences in the stent deployed configuration, in particular in the radial direction ( Fu and Xia, 2017 ; Cai et al., 2020 ). Moreover, deformable walls would enable modelling vessel straightening following the insertion of endovascular guides and stent sheaths ( King et al., 2012 ; Gindre et al., 2017 ).…”

Section: Discussionmentioning

confidence: 99%

“…Since intracranial arteries are more rigid than extracranial ones [Hayashi et al (1980)], most studies assume rigid walls finding good agreement between the results of numerical simulations and experimental tests [Ma et al (2012); Ma et al (2013); Ma et al (2014)]. However, some preliminary comparisons on idealised geometries highlighted the presence of differences in the stent deployed configuration, in particular in the radial direction (Fu and Xia, 2017;Cai et al, 2020). Moreover, deformable walls would enable modelling vessel straightening following the insertion of endovascular guides and stent sheaths (King et al, 2012;Gindre et al, 2017).…”

Section: Figurementioning

confidence: 99%

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

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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

et al. 2023

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“…The artery wall was a rigid body in Meng’s team, which is inconsistent with a real vessel wall. Additionally, some literature [ 70 , 71 , 72 , 73 , 74 ] did research on the delivery and deployment of a stent. Babiker et al [ 70 ] proposed different method to simulate the pathway of delivery.…”

Section: Introduction To the Vascular Stentsmentioning

confidence: 99%

“…They extracted the nodes on the central line of the microcatheter to define the pathway by imposing boundary conditions for different nodes in a Cartesian coordinate system. Wang’s team [ 71 ] and Zhang and Xiang’s team [ 72 ] extracted the central line of the patient’s blood vessel as the pathway of delivery stent. All of these research have made some progress and simulated the crimper, delivery, deployment, and release of vascular stent approximately.…”

Section: Introduction To the Vascular Stentsmentioning

confidence: 99%

Structural Design of Vascular Stents: A Review

Pan

Han

2021

Micromachines

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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.

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Study on the effects of alternating capacitive electric fields with different frequencies on promoting wound healing

Jia

Shi

et al. 2022

Medicine in Novel Technology and Devices

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

Cited by 5 publications

References 16 publications

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

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

Structural Design of Vascular Stents: A Review

Study on the effects of alternating capacitive electric fields with different frequencies on promoting wound healing

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