Interaction between Flow Diverter and Parent Artery of Intracranial Aneurysm: A Computational Study

Fu, Wenyu; Xia, Qixiao

doi:10.1155/2017/3751202

Cited by 12 publications

(7 citation statements)

References 19 publications

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“…Therefore, the contact pressure can be slightly overestimated. In comparison, the contact pressures predicted by our model (Table 3) are in the same range as previous FEA-based model predictions 40 in a similarly sized vessel. Furthermore, the information of contact pressure together with the length change can be important for planning the sizing of the FD.…”

Section: Discussionsupporting

confidence: 81%

A novel virtual flow diverter implantation method with realistic deployment mechanics and validated force response

Závodszky

Csippa

Paál

et al. 2020

Numer Methods Biomed Eng

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Virtual flow diverter deployment techniques underwent significant development during the last couple of years. Each existing technique displays advantageous features, as well as significant limitations. One common drawback is the lack of quantitative validation of the mechanics of the device. In the following work, we present a new spring‐mass‐based method with validated mechanical responses that combines many of the useful capabilities of previous techniques. The structure of the virtual braids naturally incorporates the axial length changes as a function of the local expansion diameter. The force response of the model was calibrated using the measured response of real FDs. The mechanics of the model allows to replicate the expansion process during deployment, including additional effects such as the push‐pull technique that is required for the deployment of braided FDs to achieve full opening and proper wall apposition. Furthermore, it is a computationally highly efficient solution that requires little pre‐processing and has a run‐time of a few seconds on a general laptop and thus allows for exploratory analyses. The model was applied in a patient‐specific geometry, where corresponding accurate control measurements in a 3D‐printed model were also available. The analysis shows the effects of FD oversizing and push‐pull application on the radial expansion, surface density, and on the wall contact pressure.

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Section: Discussionsupporting

confidence: 81%

A novel virtual flow diverter implantation method with realistic deployment mechanics and validated force response

Závodszky

Csippa

Paál

et al. 2020

Numer Methods Biomed Eng

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“…The value of the scaling parameter was selected considering the need to comply with the requirement of not exceeding the 5% contribution of kinetic energy to the potential energy of the system. Such an approach allows for controlling the contribution of the inertial component [18,19].…”

Section: Methodsmentioning

confidence: 99%

Loss of Stability in a Small-Caliber Vascular Graft

Klyshnikov¹,

Овчаренко²,

Резвова³

et al. 2019

Sovrem Tehnol Med

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The aim of the study is to develop and test a numerical method for assessing a loss of stability in a polymer vascular graft in response to a longitudinal dynamic high-speed load. Materials and Methods. A small-caliber vascular graft made by electrospinning from the composite mixture of polyhydroxybutyrate/ valerate and polycaprolactone polymers was studied. Numerical methods (finite element method) and prototype test (uniaxial tension) were used to assess the loading speeds and the resulting loss of stability, which are critical for small-caliber vascular grafts. Results. The mechanical testing of a vascular graft prototype allowed us to determine its response to quasi-static load. We also showed that the high-speed longitudinal dynamic loads significantly reduced the load-carrying capacity of the structure. This and related effects are suggested to be potential causes of the vessel lumen collapse resulted from irreversible loss of stability. Conclusion. The impact of a dynamic load significantly weakens the vascular graft and affects its elastic and deformation characteristics, leading to the premature critical deformation and eventual occlusion. Therefore, the quality of such products can be improved by a preliminary prognostic work and reinforcement of their construction.

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“…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%

“…The mechanical behaviour of braided stents is typically simulated using a finite element (FE) model where beam elements are used to discretise the wires ( Auricchio et al., 2011 ; Shiozaki et al., 2020 ; Zaccaria et al., 2020 ; McKenna and Vaughan, 2021 ); however, only a few studies modelled numerically flow diverters ( Ma et al., 2012 ; Fu and Xia, 2017 ). Due to the large amount of degrees of freedom (DOFs) and the necessity to solve the contact between the device and the vessel wall, the computational time required by these traditional techniques is very high.…”

Section: Introductionmentioning

confidence: 99%

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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Interaction between Flow Diverter and Parent Artery of Intracranial Aneurysm: A Computational Study

Cited by 12 publications

References 19 publications

A novel virtual flow diverter implantation method with realistic deployment mechanics and validated force response

A novel virtual flow diverter implantation method with realistic deployment mechanics and validated force response

Loss of Stability in a Small-Caliber Vascular Graft

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

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