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<p>Endovascular aneurysm repair (EVAR) is associated with favorable short-term outcomes; however, its long-term durability can be enhanced through effective decision-making tools. Currently, most clinical decision-making relies on pre-operative CT imaging that does not fully account for vascular deformation or endovascular device behavior. To address this limitation, we present a high-fidelity virtual EVAR model designed to predict procedural outcomes and optimize treatment planning. We used only patient imaging data to reconstruct tissue structures, preserving a non-invasive workflow. Finite element simulations captured the crimping of stent grafts (SGs), navigation of endovascular devices, and SG implantation. Deformation and stored energy were continuously tracked at each procedural step. We validated the model’s results against post-operative CT data using an image fusion technique. Compared to the patient’s post-operative data, the model showed strong alignment, with a mean modified Hausdorff distance of 1.71 ± 1.40 mm between the simulated and actual lumen centerlines. Additionally, the 1.65 ± 1.13 mm lumen radius error further supports the model’s validity. This high-fidelity, automated, and cost-effective framework can serve as a complementary tool for current EVAR pre-planning practices, potentially improving device selection, streamlining navigation roadmaps, reducing complications, and ultimately enhancing patient outcomes.</p>

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High-fidelity virtual endovascular aneurysm repair model as a decision-making tool

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Reza Abdollahi,
  • Hossein Mohammadi,
  • Simon Lessard,
  • Stephane Elkouri,
  • Philippe Charbonneau,
  • Rosaire Mongrain,
  • Gilles Soulez

摘要

Endovascular aneurysm repair (EVAR) is associated with favorable short-term outcomes; however, its long-term durability can be enhanced through effective decision-making tools. Currently, most clinical decision-making relies on pre-operative CT imaging that does not fully account for vascular deformation or endovascular device behavior. To address this limitation, we present a high-fidelity virtual EVAR model designed to predict procedural outcomes and optimize treatment planning. We used only patient imaging data to reconstruct tissue structures, preserving a non-invasive workflow. Finite element simulations captured the crimping of stent grafts (SGs), navigation of endovascular devices, and SG implantation. Deformation and stored energy were continuously tracked at each procedural step. We validated the model’s results against post-operative CT data using an image fusion technique. Compared to the patient’s post-operative data, the model showed strong alignment, with a mean modified Hausdorff distance of 1.71 ± 1.40 mm between the simulated and actual lumen centerlines. Additionally, the 1.65 ± 1.13 mm lumen radius error further supports the model’s validity. This high-fidelity, automated, and cost-effective framework can serve as a complementary tool for current EVAR pre-planning practices, potentially improving device selection, streamlining navigation roadmaps, reducing complications, and ultimately enhancing patient outcomes.