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Updated: Jun 16, 2026

A New Murine Model of Endovascular Aortic Aneurysm Repair
Published on: July 7, 2013
Mechanical Performance of Thoracic Aortic Stent-Grafts: An In Vitro and In Silico Study
Anna Ramella1,2, Sara Barati1,2, Giulia De Campo1
1Department of Chemistry, Materials and Chemical Engineering "G. Natta", Politecnico di Milano, Leonardo da Vinci, 20133, Milano, Italy.
Abstract:
Thoracic endovascular aortic repair (TEVAR) is the standard of care for thoracic aortic pathologies, and its clinical success is related to the choice of stent-grafts (SGs). In this study, we conducted a comprehensive assessment of four commercial SGs (Valiant Captivia (VC), Terumo RelayPro Bare Stent (TBS), Cook Zenith Alpha (CZA), and Gore CTAG (CTAG)) to evaluate their mechanical performance in idealised and patient-specific conditions. High-fidelity finite element models were developed and validated against experimental tests and in vitro TEVAR procedures in 3D-printed rigid phantoms. The validation showed strong agreement between simulations and experiments (average error < 5%).Then, the SGs were virtually deployed in two aortic models to investigate device-wall interaction through geometrical and mechanical parameters. A greater metal density led to increased graft apposition (up to 94%) and increased radial forces (up to 354 N vs 116N). Conversely, sparser metal structures produced lower but more localised stress regions: maximum values of 0.25 MPa versus 0.49 MPa with denser metal. Higher stresses may contribute to improved device fixation and, when associated with greater apposition, may reduce the risk of endoleak. Nevertheless, high stresses could potentially induce long-term vascular remodelling.These results underscore the influence of SG's design on TEVAR outcomes and support the integration of validated computational simulations into pre-operative planning. The SG performance varied across patient anatomies: this study highlights the importance of personalized device selection and establishes a foundation for using in silico methods to optimize TEVAR strategies and mitigate procedural risks.
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