Fluoroscopic Image-Based 3-D Environment Reconstruction and Automated Path Planning for a Robotically Steerable...

Area of Science:

• Medical imaging and robotics
• Cardiovascular interventions
• Artificial intelligence in medicine

Background:

• Cardiovascular diseases are a leading global cause of mortality.
• Vascular interventions often rely on manual guidewire placement, posing risks.
• Reducing procedural time and radiation exposure is crucial for patient safety.

Purpose of the Study:

• To develop a fully automated approach for steerable guidewire navigation.
• To enhance precision and reduce invasiveness in cardiovascular procedures.
• To improve patient outcomes and minimize radiation exposure.

Main Methods:

• 3D vascular model reconstruction from fluoroscopic images using shape-from-silhouette.
• Deep learning-based denoising of vascular models.
• Optimal path planning using slice planes and a modified hybrid A-star algorithm.
• Demonstration on a porcine aorta using a COAST guidewire.

Main Results:

• Successful 3D reconstruction and denoising of vascular phantoms.
• Development of an efficient path planning algorithm for guidewire navigation.
• Validation of the automated system in an ex vivo model.

Conclusions:

• The proposed automated system offers a promising solution for steerable guidewire navigation.
• This technology has the potential to significantly improve cardiovascular interventions.
• Further research can lead to enhanced robotic-assisted vascular surgeries.