SLAM-TKA: Simultaneously Localizing X-Ray Device and Mapping Pins in Conventional Total Knee Arthroplasty

This paper presents a novel simultaneous localization and mapping (SLAM) technique, termed SLAM-TKA, for assisting total knee arthroplasty (TKA), a highly effective orthopaedic surgery that replaces arthritic or dysfunctional joint surfaces with knee prostheses. Our proposed SLAM algorithm uses information from a pre-operative tibia CT scan, intra-operative 2D X-ray images, and a trocar pin 3D mesh model to simultaneously localise the X-ray device and map the two trocar pins. Then, the estimated pins are used to evaluate the accuracy of the bone resection plane before the actual bone cutting, which plays a crucial role in precisely implanting the knee prostheses. To ensure high accuracy and robustness of the proposed SLAM algorithm, three energy terms are proposed and used together to align the edge observations of the tibia, fibula and pins on the intra-operative X-ray images and their corresponding pre-operative 3D mesh models in both 2D and 3D space. To enable the proposed iteration-based SLAM algorithm to be implemented in real-time such that the evaluation processing does not interrupt much on the workflow of TKA, the data association of edge correspondences matching and exhausted points-to-mesh distance calculation are pre-computed using the signed distance field method. Simulations are used to evaluate the accuracy and robustness of the proposed algorithm, and the experiments using in-vivo datasets from five patients demonstrate the high accuracy and efficiency in practice. The code and datasets are released at <uri>https://github.com/zsustc/SLAM-TKA</uri>.