TY - JOUR
T1 - Patient-specific in silico endovascular repair of abdominal aortic aneurysms
T2 - application and validation
AU - Hemmler, André
AU - Lutz, Brigitta
AU - Kalender, Günay
AU - Reeps, Christian
AU - Gee, Michael W.
N1 - Publisher Copyright:
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2019/8/15
Y1 - 2019/8/15
N2 - Non-negligible postinterventional complication rates after endovascular aneurysm repair (EVAR) leave room for further improvements. Since the potential success of EVAR depends on various patient-specific factors, such as the complexity of the vessel geometry and the physiological state of the vessel, in silico models can be a valuable tool in the preinterventional planning phase. A suitable in silico EVAR methodology applied to patient-specific cases can be used to predict stent-graft (SG)-related complications, such as SG migration, endoleaks or tissue remodeling-induced aortic neck dilatation and to improve the selection and sizing process of SGs. In this contribution, we apply an in silico EVAR methodology that predicts the final state of the deployed SG after intervention to three clinical cases. A novel qualitative and quantitative validation methodology, that is based on a comparison between in silico results and postinterventional CT data, is presented. The validation methodology compares average stent diameters pseudo-continuously along the total length of the deployed SG. The validation of the in silico results shows very good agreement proving the potential of using in silico approaches in the preinterventional planning of EVAR. We consider models of bifurcated, marketed SGs as well as sophisticated models of patient-specific vessels that include intraluminal thrombus, calcifications and an anisotropic model for the vessel wall. We exemplarily show the additional benefit and applicability of in silico EVAR approaches to clinical cases by evaluating mechanical quantities with the potential to assess the quality of SG fixation and sealing such as contact tractions between SG and vessel as well as SG-induced tissue overstresses.
AB - Non-negligible postinterventional complication rates after endovascular aneurysm repair (EVAR) leave room for further improvements. Since the potential success of EVAR depends on various patient-specific factors, such as the complexity of the vessel geometry and the physiological state of the vessel, in silico models can be a valuable tool in the preinterventional planning phase. A suitable in silico EVAR methodology applied to patient-specific cases can be used to predict stent-graft (SG)-related complications, such as SG migration, endoleaks or tissue remodeling-induced aortic neck dilatation and to improve the selection and sizing process of SGs. In this contribution, we apply an in silico EVAR methodology that predicts the final state of the deployed SG after intervention to three clinical cases. A novel qualitative and quantitative validation methodology, that is based on a comparison between in silico results and postinterventional CT data, is presented. The validation methodology compares average stent diameters pseudo-continuously along the total length of the deployed SG. The validation of the in silico results shows very good agreement proving the potential of using in silico approaches in the preinterventional planning of EVAR. We consider models of bifurcated, marketed SGs as well as sophisticated models of patient-specific vessels that include intraluminal thrombus, calcifications and an anisotropic model for the vessel wall. We exemplarily show the additional benefit and applicability of in silico EVAR approaches to clinical cases by evaluating mechanical quantities with the potential to assess the quality of SG fixation and sealing such as contact tractions between SG and vessel as well as SG-induced tissue overstresses.
KW - Abdominal aortic aneurysm
KW - Endovascular repair
KW - Finite element method
KW - Patient-specific modeling
KW - Stent-graft
UR - http://www.scopus.com/inward/record.url?scp=85062704449&partnerID=8YFLogxK
U2 - 10.1007/s10237-019-01125-5
DO - 10.1007/s10237-019-01125-5
M3 - Article
C2 - 30834463
AN - SCOPUS:85062704449
SN - 1617-7959
VL - 18
SP - 983
EP - 1004
JO - Biomechanics and Modeling in Mechanobiology
JF - Biomechanics and Modeling in Mechanobiology
IS - 4
ER -