This course comprises three parts:
Surgical Metrology
Image Processing and Image Registration
Detailed Design via Implementation
At the end of this course, students are expected to acquire the necessary knowledge to implement, in Python, an augmented reality-based (AR) surgical navigation system as a proof-of-principle.
Course Philosophy and Learning Approach¶
This course adopts a systems engineering perspective on surgical navigation, emphasizing theoretical foundations and practical implementation.
Theoretical Foundation¶
Linear Algebra for data representation and coordinate transformation
Computer Graphics for 2D and 3D visualization
Augmented Reality for 3D visualization and human-computer interaction
About This Course: Focus on Surgical Execution¶
While computer-integrated interventional medicine encompasses the entire planning-execution-assessment cycle, this course provides deep expertise in the execution phase—specifically, surgical navigation systems that serve as “GPS for surgery.”
What We Cover in Depth:¶
Real-time tracking technologies: Optical, electromagnetic, and inertial systems
Registration algorithms: Aligning preoperative plans with intraoperative reality
Navigation interfaces: Displaying guidance information to surgical teams
Accuracy assessment: Methods for measuring and maintaining system performance
Integration challenges: Connecting navigation systems with OR workflows
What We Touch upon:¶
Planning systems: How surgical plans are created (inputs to navigation systems)
Quality assessment: How navigation accuracy affects outcomes (outputs from navigation systems)
Broader CAI context: Where navigation fits in the complete intervention cycle
What We Don’t Cover:¶
Medical imaging physics: Detailed CT/MRI acquisition and reconstruction
Robotic control theory: Low-level motor control and kinematics
Clinical medicine: Diagnosis and treatment selection
Healthcare economics: Detailed cost-effectiveness analysis
This focused approach allows us to develop deep expertise in surgical navigation while maintaining awareness of the broader CAI ecosystem.
Learning Objectives¶
By the end of this course, students will be able to:
Analyze the role of computer-assisted technologies in modern interventional medicine
Design and evaluate surgical navigation systems for specific clinical applications
Understand the technical challenges and solutions in image-guided surgery
Assess the clinical impact and limitations of current CAI technologies
Propose innovative solutions for emerging challenges in computer-assisted intervention