Simulations numériques prédictives et personnalisées des interventions endovasculaires pour les anévrismes aortiques abdominaux
Predictive and patient-specific numerical simulations of endovascular surgery for aortic abdominal aneurysms
Michel ROCHETTE
Director of Research, ANSYS
Abstract : Endovascular aneurysm repair is a mini-invasive technique used to treat abdominal aortic aneurysms. It relies on the exclusion of the aneurysmal site by the deployment of one or several stent-grafts introduced via the femoral and iliac arteries. The stiffness of the tools used for stent navigation in arteries leads to a straightening of the vascular structure. The arterial deformations may be related to difficulties to deliver the stents or to a discrepancy in the arterial lengths calculated from the preoperative CT-scan. Mechanical finite-element simulation might be useful to predict these deformations and their influence on the patient anatomy, providing to surgeons a new tool in order to plan the intervention with objective data
.A biomechanical model of the vascular structure is built from the preoperative CT-scan data. The biomechanical model takes into account nonlinear properties of the arterial wall, loading due to arterial pressure and external support provided by external tissues and bones structures. This model is then use to run an explicit finite element simulation of endovascular tools insertion with Ansys Ls-Dyna software.
The shape of the navigation tools predicted by the patient specific simulation are compared and validated with patient intraoperative images. Finally this endovascular simulation is integrated in the commercial surgery sizing software Endosize (Therenva).
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Biography : Michel ROCHETTE
DR Michel ROCHETTE has a PhD in Applied Mathematics from the University of Nice. He was the founder of CADOE (as adaptive computation using high order derivatives) in 1994. Since 2001, after the acquisition of CADOE by ANSYS, he has been managing the research and development in France. His main research topics are reduced order modeling and patient specific simulation. |