Catheter Monitoring in Intelligent Endovascular Navigation Systems: Interactive Simulations and Mixed Reality for Enhanced Navigational Awareness

2026-04-24

AI Summary

This study addresses the challenge of real-time, precise monitoring of catheter–vessel interactions during endovascular interventions by proposing an innovative approach that integrates sensor-informed finite element simulation with mixed-reality visualization on the HoloLens 2. A patient-specific venous pathway model is reconstructed from CT imaging and coupled with a hybrid sensing system combining fiber Bragg grating and electromagnetic sensors for accurate catheter tracking. Contact mechanics are simulated using the Lagrange multiplier method to dynamically reconstruct catheter shape and vessel deformation. The system achieves near-real-time computational performance, with the HoloLens 2 maintaining a stable frame rate of 35–40 fps. Validation shows median absolute errors in vessel wall displacement below 1 mm and relative errors under 2.33 mm, significantly enhancing intraoperative interaction awareness and decision-making support.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Motion & Tracking

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Purpose: Developing and testing a framework that integrates real-time catheter shape reconstruction, interactive simulations, and mixed reality visualization to enable accurate monitoring of catheter-vessel interactions during endovascular navigation. Methods: A finite element model (FEM) of the venous pathway from the right femoral vein to the inferior vena cava was generated from computed tomography data and implemented into an interactive simulation. Catheter motion was imposed as boundary condition, and catheter-vessel contact was modeled with a Lagrange multiplier formulation to compute vessel deformation. The framework was tested in-vitro using a sensorized catheter with Fiber Bragg Grating and electromagnetic sensors as it was advanced through a silicone replica of the vascular anatomy. Real-time sensor read-outs fed the simulation, and the updated catheter and vessel geometries were streamed to Hololens 2. The performance and accuracy of FEM-computed vessel wall displacement were validated against experimental ground-truth obtained via stereo frames triangulation. Results: The simulated time exceeded the real temporal extent by 12% during initial navigation and by 45% when the catheter reached the most tortuous portion. Hololens 2 rendering remained stable at 35-40 frames per second. The median relative displacement error between FEM-computed and ground-truth vessel wall displacements remained below 1 mm and 2.33 mm for these two phases, respectively. Conclusion: The study demonstrates the feasibility of integrating interactive biomechanical simulation with real-time sensor data to enable continuous monitoring of catheter-vessel interactions, with mixed reality visualization serving as a user interface to support operator decision-making.

Problem

• catheter monitoring
• endovascular navigation
• catheter-vessel interaction
• navigational awareness
• mixed reality

Innovation

• mixed reality
• real-time simulation
• catheter-vessel interaction
• finite element modeling
• sensorized catheter