Augmented Reality With Dynamic Anatomy Modelling for Knee Arthroscopy

Abstract

Research on augmented reality (AR) for knee arthroscopy has not adequately focused on knee flexion during surgery. To overcome major AR errors caused by knee movement, this study presents an association model between the finite-element models of the knee surface and bones to enable dynamic anatomy modelling. The association model allows the displacement of the knee surface elements and the reaction force of the bone elements to interact with each other. During knee flexion, the real-time shape of the knee is captured with a colour and depth camera, and the association model deforms accordingly from the extension to the flexion state. The proposed model was evaluated using computed tomography data from the knees of six participants. The results showed that the association model successfully compensates for the movement of the femur and tibia within an error margin of only 3.85 mm around the drilling area. The proposed model could therefore enable effective AR-based surgical navigation during kneesurgeries.