Shape, Pose and Density Statistical Model for 3D Reconstruction of Articulated Structures from X-Ray Images.

Abstract

This article proposes a joint statistical model, to describe the volumetric shape + pose + density information, and a reconstruction algorithm to simultaneously recover the volumetric information of several anatomical structures from biplanar radiographs. A PCA-based representation is proposed as compact model representation and a hybrid AAM search and genetic optimization is used to perform the reconstruction. A study was conducted to recover a 3D volume grid containing a human knee mesh from 2 orthogonal simulated radiographs. The model was computed on a data set of 200 subjects and the reconstruction test was performed on 18 subjects, leading to a surface distance RMSE of 0.7 ± 0.31 mm for the distal femur, 0.9 ± 0.3 mm for the proximal tibia and 0.8 ± 0.3 mm for the fibula. These results demonstrate the feasibility and the pertinence of the proposed approach, the next step being its application in a clinical context.