Dynamic Arthroscopic Navigation System for Anterior Cruciate Ligament Reconstruction Based on Multi-level Memory Architecture
This provides improved surgical precision for ACL reconstruction surgeons by enabling stable navigation without additional hardware, though it builds incrementally on prior work.
This paper tackles the problem of tracking anatomical landmarks during anterior cruciate ligament reconstruction surgery by extending a markerless navigation method from static image matching to dynamic video sequence tracking using a multi-level memory architecture. The system achieves real-time performance at 25.3 FPS with 39.5 ms latency and reduces tracking error by approximately 45% compared to the previous static system.
This paper presents a dynamic arthroscopic navigation system based on multi-level memory architecture for anterior cruciate ligament (ACL) reconstruction surgery. The system extends our previously proposed markerless navigation method from static image matching to dynamic video sequence tracking. By integrating the Atkinson-Shiffrin memory model's three-level architecture (sensory memory, working memory, and long-term memory), our system maintains continuous tracking of the femoral condyle throughout the surgical procedure, providing stable navigation support even in complex situations involving viewpoint changes, instrument occlusion, and tissue deformation. Unlike existing methods, our system operates in real-time on standard arthroscopic equipment without requiring additional tracking hardware, achieving 25.3 FPS with a latency of only 39.5 ms, representing a 3.5-fold improvement over our previous static system. For extended sequences (1000 frames), the dynamic system maintained an error of 5.3 plus-minus 1.5 pixels, compared to the static system's 12.6 plus-minus 3.7 pixels - an improvement of approximately 45 percent. For medium-length sequences (500 frames) and short sequences (100 frames), the system achieved approximately 35 percent and 19 percent accuracy improvements, respectively. Experimental results demonstrate the system overcomes limitations of traditional static matching methods, providing new technical support for improving surgical precision in ACL reconstruction.