MRI-Guided Endovascular Interventions with CathBot Robotic Platform

Endovascular procedures often rely on X-ray fluoroscopy for real-time imaging guidance, which poses risks of ionizing radiation. MRI offers a safer alternative with superior soft tissue contrast and no ionizing radiation. However, its use in procedural guidance is limited by technical challenges. This thesis addresses these challenges in MRI-guided endovascular interventions using the CathBot robotic platform and interactive MR imaging. The key issues are low spatial/temporal resolution, guidewire visibility, and procedure safety. The project aims to enhance MRI-guided procedures by optimizing an interactive MRI sequence, detecting an MR-safe passive endovascular guidewire using a convolutional neural network (CNN), and implementing haptic feedback on the CathBot platform. It compares balanced steady-state free precession (bSSFP) and gradient echo (GRE) sequences to optimize signal-to-noise and contrast-to-noise ratios while maintaining interactivity. The CNN detects susceptibility artifacts from paramagnetic markers on the guidewire in near real-time. Additionally, an automated MRI slice alignment system tracks the guidewire's movement. Integration with the CathBot platform provides real-time collision detection and haptic feedback, introducing a concept for haptic feedback in MRI-guided interventions, ensuring improved accuracy and reduced risk during procedures.