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3D Reconstruction of Patient Specific Bone Utilizing Biplanar X-Ray Images and Sex Specific Atlases

Introduction: The clinical outcome of a total knee arthroplasty (TKA) is mainly determined by the accuracy of the surgical procedure itself. The use of patient–specific computer knee models for preoperative planning along with intraoperative surgical guidance enables the surgeon to plan the optimal strategy and to precisely complete this planned strategy intraoperativly. In current systems, computed tomography (CT) is often used with manual segmentation to obtain 3D surface models from the volumetric data. Although this method is accurate, the high cost, large radiation dosage, and time-consuming nature of this process prompts the search for alternative methodologies. In this paper we present Biplanar reconstruction as method for reconstruction for patient specific bone. In our method a robust sex-specific statistical atlas of bone is incorporated into the biplanar reconstruction algorithm which enables high accuracy of the resulting 3D bone models. The bone atlas, combined with a powerful 3D-2D registration algorithm [1] constrained with automated morphometric and surgical measurements provide the framework for a solution to this difficult problem. The resulting setup is, low cost, and competitive to CT-based systems. Materials and Methods: Our method follows the process outlined in Figure 1. First X-ray images are enhanced and 2D measurements are performed in order to estimate the size and shape of the bone. These measurements increase the speed and accuracy of the registration process. A calibration step is then performed in order to extract the intrinsic and extrinsic camera parameters for scene calibration and relative pose estimation. The average bone from the atlas is placed in a 3D scene with two or more X-ray images in an initial pose for 3D rigid alignment. The average bone and initial pose are used to create a normally distributed initial population of bone models (represented in the atlas) centered around the average bone at the initial pose. The shape parameters, translation, and rotation combine for a total of 11 degrees of freedom (DOF) problem. The genetic algorithm is then used, along with our novel 3D-2D scoring metric, to optimize both the shape and alignment of the population to the X-ray images.[Figure 1] Results: We verified our algorithm against real X-ray images. This was performed by running the reconstruction algorithm on real X-ray images for the femur using the calibration target. The output was then compared to the manually segmented femur from CT data. The point-to-point root mean square “RMS” surface errors between the known femur model and the reconstructed femur model from X-ray images was found to average 1.9 mm, whereas an average RMS error of 1.82 mm was achieved on the tibia. Figure 2 shows the point-to-point surface mapping between the true model and the reconstructed model for the full femur using real Xray images with an average point-to-surface RMS error of 1.5 mm. Figure 3 shows the surface mapping between the true model and the reconstructed model for tibia using real X-rays with an average RMS error of 1.45 mm.[Figure 2]