Self-supervised 3D Reconstruction of Tibia and Fibula from Biplanar X-rays

Abstract

With the growing number of patients experiencing knee-related conditions, total knee arthroplasty (TKA) has become a common procedure, where a 3D visualisation of the patient’s tibia and fibula is essential for preoperative planning. Traditional imaging techniques, such as computed tomography (CT), often expose patients to high levels of radiation or impose significant financial costs. As an alternative, this paper proposes a novel approach that reconstructs a 3D model of the tibia and fibula using only two X-ray images (taken from the coronal and sagittal planes) and a general template, significantly reducing radiation exposure and financial burden. Our algorithm of 3D reconstruction for patient-specific anatomies combines point-based deformation with deep learning techniques. Initially, the general model undergoes a preliminary deformation to match the patient tibia and fibula dimensions. This pre-deformed model then serves as a template, followed by a fine deformation process via a self-supervised graph convolutional network (GCN), whose parameters are trained iteratively by comparing the template projection and the X-ray measurements. Following tests in simulations, cadaver experiments, and in-vivo experiments, our proposed algorithm demonstrates state-of-the-art accuracy and exceptional robustness across different evaluation metrics. Our code is available at https://github.com/DrKaiPan/tfDeform_GCN.git