Predictive Numerical Simulations of Double Branch Stent-Graft Deployment in an Aortic Arch Aneurysm

TL;DR

This paper presents a computational simulation tool that accurately predicts the deployment of double branch stent-grafts in complex aortic arch aneurysm cases, aiding surgical planning and reducing complications.

Contribution

The study introduces a patient-specific simulation method for predicting stent-graft deployment in complex aortic arch repairs, demonstrating high accuracy and potential clinical utility.

Findings

Simulation predictions closely match post-operative scans

Torsion effects are accurately reproduced by the model

Deviations in diameter and position are within acceptable ranges

Abstract

Total endovascular repair of the aortic arch represents a promising option for patients ineligible to open surgery. Custom-made design of stent-grafts (SG), such as the Terumo Aortic RelayBranch device (DB), requires complex preoperative measures. Accurate SG deployment is required to avoid intraoperative or postoperative complications, which is extremely challenging in the aortic arch. In that context, our aim is to develop a computational tool able to predict SG deployment in such highly complex situations. A patient-specific case is performed with complete deployment of the DB and its bridging stents in an aneurysmal aortic arch. Deviations of our simulation predictions from actual stent positions are estimated based on post-operative scan and a sensitivity analysis is performed to assess the effects of material parameters. Results show a very good agreement between simulations and post-operative scan, with especially a torsion effect, which is successfully reproduced by our simulation. Relative diameter, transverse and longitudinal deviations are of 3.2$\pm$4.0%, 2.6$\pm$2.9mm and 5.2$\pm$3.5mm respectively. Our numerical simulations show their ability to successfully predict the DB deployment in complex anatomy. The results emphasize the potential of computational simulations to assist practitioners in planning and performing complex and secure interventions.