Quasi-automated reconstruction of the femur from bi-planar X-rays

Abstract

3D reconstruction from low-dose Bi-Planar X-Rays (BPXR) is a rising practice in clinical routine. However, this process is time consuming and highly depends on the user. This study aims to partially automate the process for the femur, thus decreasing reconstruction time and increasing robustness. As a training set, 50 femurs are segmented from CT scans together with 120 BPXR reconstructions. From this dataset, an initial solution for the bony contours is defined through Gaussian Process Regression (GPR), using eight digitized landmarks. This initial solution is projected on both x-rays and automatically adjusted using an adapted Minimal Path Algorithm (MPA). To evaluate this method, CT-scans were acquired from 20 cadaveric femurs. For each sample, the CTbased reconstruction is compared to the one automatically generated from the digitally reconstructed radiographs. Euclidean distances between femur reconstructions and the segmented CT data are on average 1.0 mm with a Root Mean Square Error (RMSE) of 0.8 mm. Femoral torsion errors are assessed: the bias is lower than 0.1° with a 95% confidence interval of 4.8°. The proposed method substantially improves 3D reconstructions from BPXR, as it enables a fast and reliable reconstruction, without the need for manual adjustments, which is essential in clinical routine.