Mathematical models are well recognized and widely adopted tools to study stenting procedures. Nowadays, the increased computational power allows satisfying clinical needs more easily. The simulations of complex patient-specific cases including the implantation of multiple stents in coronary bifurcations or curved vessels have become a reality. Thanks to image-based methods, the peculiar anatomical features prior and after a stent insertion are detectable. The stress state exerted within the arterial wall of a coronary artery can be estimated by means of structural simulations. This review chapter aims to describe the most recent advances in this area with particular focus on stent deployment simulations in coronary bifurcations.
Modeling the stent deployment in coronary arteries and coronary bifurcations / Chiastra, Claudio; Dubini, Gabriele; Migliavacca, Francesco - In: Biomechanics of Coronary Atherosclerotic Plaque: From Model to Patient[s.l] : Elsevier Inc, 2021. - ISBN 978-0-12-817195-0. - pp. 563-582 [10.1016/B978-0-12-817195-0.00026-3]
Modeling the stent deployment in coronary arteries and coronary bifurcations
Chiastra, Claudio;
2021
Abstract
Mathematical models are well recognized and widely adopted tools to study stenting procedures. Nowadays, the increased computational power allows satisfying clinical needs more easily. The simulations of complex patient-specific cases including the implantation of multiple stents in coronary bifurcations or curved vessels have become a reality. Thanks to image-based methods, the peculiar anatomical features prior and after a stent insertion are detectable. The stress state exerted within the arterial wall of a coronary artery can be estimated by means of structural simulations. This review chapter aims to describe the most recent advances in this area with particular focus on stent deployment simulations in coronary bifurcations.File | Dimensione | Formato | |
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2020 Chiastra - Modeling the stent deployment in coronary arteries and coronary bifurcations.pdf
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Chapter-Stent-FEA-pre-print.pdf
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https://hdl.handle.net/11583/2904522