A verification study on the accuracy of virtual thoracic endovascular aneurysm repair procedure
Year: 2025
Type: article
Abstract: Computational models in the cardiovascular field, among which in Thoracic Endovascular Aneurysm Repair (TEVAR)—which involves the deployment of a stent-graft to exclude pathological region of the aorta from circulation—are gaining interest among clinicians as tools to support pre-operative decision-making. To assess the credibility of in-silico models, the American Society of Mechanical Engineers published the V&V40 framework, which includes verification, validation, and uncertainty quantification. This study presents a verification analysis of TEVAR simulations to evaluate the impact of selected parameters on simulation outcomes, focusing on specific quantities of interests. Simulations were conducted on three patient-specific anatomies replicating the crimping, tracking, and release phases of the clinical procedure. Evaluated numerical parameters included mass damping, friction coefficient, tracking velocity, and release time. Sliding distance, rotation and computational time were analyzed in the tracking phase; while Opening Area, center-to-center distance, and plane rotation were evaluated in the release phase, comparing simulation results with post-operative CT segmentations. A mass damping coefficient of 0.1 ms −1 for the stent-graft and 1 ms −1 for the catheter, a friction coefficient of 1 between stent-graft and catheter, and a velocity of 0.5 m/s yielded the best results during tracking. In the release phase, the selected parameters were: a damping of 0.1 ms −1 , a friction coefficient of 0.1, and a release time of 5 ms. In conclusion, this study provides a comprehensive analysis to support the selection of best settings for TEVAR simulations. A verification analysis is essential to understand the influence of numerical parameters on simulation outcomes
Source: Journal of Biomechanics
Authors: Giulia De Campo, Sara Barati, Anna Ramella, Robin Heijmen, Santi Trimarchi, Francesco Migliavacca, Giulia Luraghi
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,650
Awards: G490430286, G6045954243
OpenAlex ID: W4412499323
