An Human-Computer Interactive Augmented Reality System for Coronary Artery Diagnosis Planning and Training

Area of Science:

• Medical Imaging
• Augmented Reality
• Surgical Planning

Background:

• Current coronary artery diagnosis and surgical planning lack intuitive visualization tools.
• Medical intern training can be improved with more interactive and realistic simulation methods.

Purpose of the Study:

• To design and implement an augmented reality system for coronary artery diagnosis planning and training (ARS-CADPT).
• To enhance the intuitiveness and naturalness of coronary artery diagnosis and preoperative planning.
• To improve the training effectiveness for medical interns.

Main Methods:

• Developed a 3D reconstruction algorithm using computed tomographic (CT) images to model coronary artery vessels (CAV).
• Implemented static and dynamic gesture recognition algorithms for real-time, user-friendly human-computer interaction (HCI).
• Created a Sort-First parallel rendering and splicing display subsystem to support multiple users.

Main Results:

• Achieved high accuracy in the 3D reconstruction of coronary artery vessel models.
• Demonstrated natural and fluent human-computer interaction.
• Confirmed good visual effects and system performance.

Conclusions:

• The ARS-CADPT system effectively meets the requirements for coronary artery diagnosis and preoperative planning.
• The system offers a valuable tool for enhancing medical education and training.
• Augmented reality provides a promising approach for improving medical procedures and learning.