Literature DB >> 26620777

A Finite Element Method to Predict Adverse Events in Intracranial Stenting Using Microstents: In Vitro Verification and Patient Specific Case Study.

Francesco Iannaccone1,2, Matthieu De Beule3,4, Sander De Bock3, Imramsjah M J Van der Bom5, Matthew J Gounis6, Ajay K Wakhloo6, Matthieu Boone7, Benedict Verhegghe3,4, Patrick Segers3.   

Abstract

Clinical studies have demonstrated the efficacy of stent supported coiling for intra-cranial aneurysm treatment. Despite encouraging outcomes, some matters are yet to be addressed. In particular closed stent designs are influenced by the delivery technique and may suffer from under-expansion, with the typical effect of "hugging" the inner curvature of the vessel which seems related to adverse events. In this study we propose a novel finite element (FE) environment to study potential failure able to reproduce the microcatheter "pull-back" delivery technique. We first verified our procedure with published in vitro data and then replicated the intervention on one patient treated with a 4.5 × 22 mm Enterprise microstent (Codman Neurovascular; Raynham MA, USA). Results showed good agreement with the in vitro test, catching both size and location of the malapposed area. A simulation of a 28 mm stent in the same geometry highlighted the impact of the delivery technique, which leads to larger area of malapposition. The patient specific simulation matched the global stent configuration and zones prone to malapposition shown on the clinical images with difference in tortuosity between actual and virtual treatment around 2.3%. We conclude that the presented FE strategy provides an accurate description of the stent mechanics and, after further in vivo validation and optimization, will be a tool to aid clinicians to anticipate the acute procedural outcome avoiding poor initial results.

Entities:  

Keywords:  Aneurysm; Apposition; Cerebral; Hugging; Incomplete; Intra-cranial; Microstent; Stenting

Mesh:

Year:  2015        PMID: 26620777     DOI: 10.1007/s10439-015-1505-2

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  1 in total

1.  Optimizing through computational modeling to reduce dogboning of functionally graded coronary stent material.

Authors:  Arezoo Khosravi; Amir Akbari; Hossein Bahreinizad; Milad Salimi Bani; Alireza Karimi
Journal:  J Mater Sci Mater Med       Date:  2017-08-17       Impact factor: 3.896

  1 in total

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