Individual Research Project 08

Simulating endovascular aortic repair towards assessing long-term treatment implications

Rahul Sathish Vellaparambil – ESR 08

Currently endovascular aortic repair (EVAR) is used to treat the vast majority of aortic aneurysms (AA). This is a minimal invasive treatment, where a stent graft is delivered endovascular in order to cover the aneurysmatic part of the aorta. However, recent studies showed that the durability of EVAR requires improvement, and sack enlargement, different types of leakages and stent migration are typically observed long-term complications of this treatment. Especially for complex shaped AAs a particular challenge faced by clinicians is to select the most appropriate stent-graft for an individual patient. This project aims at modelling the stent delivery process as well as its interaction with the aneurysmatic wall using non-linear biomechanical simulations. Such models are currently under development at ARMINES and they will be extended by considering growth and remodelling of the aneurysmatic wall to predict the long-term outcome of the patient-specific EVAR treatment. The model will be validated against clinical follow-up data of AAs using modalities like Ultrasound and Computed Tomography-Angiography (CT-A). In order to fully exploit the information contained in the simulations, the morphology of the aneurysmatic wall will be visualized and assessed using a reconstructed real size anatomical replica. This can be achieved by reproducing the AA’s geometrical features with additive manufacturing (AM) technologies, mimicking tissue properties with different material types. This will allow clinicians to evaluate the long-term fitting of stent-graft within the simulated geometry on patient-specific phantoms. Simulating EVAR treatment will not only improve the management of aneurysm patients, but could also be used for a surgery simulator to train clinicians (Digital Twin) and with the aid of 3D printed phantoms for planning surgical procedures. Most importantly, a sound understanding of EVAR treatment will decrease healthcare related costs by avoiding costly re-interventions.

Expected Results

• Clinically relevant numerical model to predict AA remodelling after EVAR treatment, as part of the Digital Twin..

• Operator sensitivity analysis of model parameters and validation against short-term and long-term clinical outcomes..

• Identification of predictive baseline clinical parameters to assess long-term durability of EVAR treatment.

• 3D printed bio-models of AA reconstructed at different follow-up times..