Enhancing Guidewire Navigation in Aortic Arch Using Recurrent Neural Networks

Overview

This study develops a recurrent neural network-based controller to autonomously navigate endovascular guidewires through varying aortic arch geometries without explicit vessel geometry feedback. The approach demonstrates improved generalization to unseen vessel anatomies, addressing a key limitation of prior learning-based navigation methods.

Background

Endovascular interventions rely on guidewires and catheters to reach vascular lesions for treatment of conditions such as heart attack and stroke. Navigation is guided by fluoroscopy imaging, often augmented with contrast agents to visualize vessels, but these have limitations including radiation exposure and health risks. Automation of guidewire navigation can reduce surgeon radiation exposure and cognitive load, and improve access to care in remote areas. However, current learning-based autonomous navigation methods struggle to generalize across patient-specific vessel geometries, limiting clinical applicability.

Data Highlights

The controller navigates a guidewire from the descending aorta to supraaortal arteries in synthetic 3D aortic arch models with randomized vessel geometries. Feedback is limited to 2D tracking coordinates simulating fluoroscopy projections. The environment parametrizes arch type, geometry, width, and height scaling to test generalization. No numerical success rates or performance metrics are provided in the excerpt.

Key Findings

• Recurrent neural networks enable autonomous navigation of guidewires in varying aortic arch geometries without explicit vessel geometry input.
• The controller receives only 2D tracking coordinates analogous to fluoroscopy imaging, simulating realistic clinical feedback.
• Training on randomized synthetic aortic arches allows evaluation of generalization to unseen vessel anatomies.
• Navigation targets are supraaortal arteries, critical for cerebral artery interventions.
• The approach excludes use of contrast agents and catheters, focusing on guidewire-only navigation.
• Instrument characteristics remain constant, isolating the effect of vessel geometry variability on navigation performance.

Clinical Implications

This autonomous navigation approach could reduce radiation exposure to surgeons by enabling remote operation outside the radiation zone. Improved generalization to patient-specific vessel anatomies enhances the potential for broader clinical deployment, including in remote or resource-limited settings. By automating low-level instrument manipulation, clinicians can focus on higher-level intervention decisions, potentially improving procedural efficiency and safety.

Conclusion

Utilizing recurrent neural networks for guidewire navigation in the aortic arch shows promise in overcoming generalization challenges inherent to patient-specific vascular anatomy. This advancement supports the development of autonomous endovascular interventions with potential to improve clinical workflow and accessibility.

References

1. Krauss et al. 2024 -- Utilizing Recurrent Neural Networks to Enhance Generalization of Vessel Geometry in Autonomous Navigation of Endovascular Guidewires within the Aortic Arch