Correspondence free 3D statistical shape model fitting to sparse X-ray projections
In this paper we address the problem of 3D shape reconstruction from sparse X-ray projections. We present a correspondence free method to fit a statistical shape model to two X-ray projections, and illustrate its performance in 3D shape reconstruction of the femur. The method alternates between 2D segmentation and 3D shaoe reconstruction, where 2D segmentation is guided by dynamic programming along the model projection on the X-ray plane. 3D reconstruction is based on the iterative minimization of the 3D distance between a set of support points and the back-projected silhouette with respect to the pose and model parameters. We show robustness of the reconstruction on simulated X-ray projection data of the femur, varying the field of view; and in a pilot study on cadaveric femora.