PhD Oral Exam - Pedram Fekri, Mechanical Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Cardiac catheterization is a procedure in minimally invasive cardiovascular interventions, where safe and effective performance relies on accurate force awareness, catheter visualization, and navigation within complex vascular geometries. This doctoral research investigates learning-based, sensor-free frameworks using standard clinical imaging modalities, with the objective of advancing toward autonomous catheter-based systems. The work is conducted as a multidisciplinary study at the intersection of artificial intelligence, deep learning–based computer vision, medical robotics, mechatronics, and autonomous systems, based on the premise that catheter deflections observed from multiple imaging viewpoints encode sufficient information to infer three-dimensional contact forces and support goal-directed navigation. A lightweight fusion-based convolutional neural network is first proposed to estimate three-dimensional contact forces directly from stereo catheter images without reliance on physical sensors or explicit mechanical modeling. This is extended to a multitask encoder–decoder architecture that simultaneously performs catheter segmentation and force estimation from biplane fluoroscopic images within a single end-to-end framework, supported by a synthetic X-ray image generator designed to resemble clinical fluoroscopy. The framework is further extended through a Vision Transformer–based architecture with cross-attention for joint stereo segmentation and force estimation. Finally, autonomous catheter navigation is explored through a goal-conditioned, multimodal vision-to-action model trained using imitation learning for perception-driven catheter steering in a physical robotic setup. Overall, this dissertation demonstrates that force estimation, catheter perception, and navigation can be learned within sensor-free frameworks, supporting future autonomous robotic catheterization systems.