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Comunicació presentada al congrés: Medical Imaging 2010: Image Processing (SPIE) celebrat del 13 al 18 de febrer de 2010 a San Diego, California. Area Bone Mineral Density (aBMD) measured by Dual-energy X-ray Absorptiometry (DXA) is an established criterion in the evaluation of hip fracture risk. The evaluation from these planar images, however, is limited to 2D while it has been shown that proper 3D assessment of both the shape and the Bone Mineral Density (BMD) distribution improves the fracture risk estimation. In this work we present a method to reconstruct both the 3D bone shape and 3D BMD distribution of the proximal femur from a single DXA image. A statistical model of shape and a separate statistical model of the BMD distribution were automatically constructed from a set of Quantitative Computed Tomography (QCT) scans. The reconstruction method incorporates a fully automatic intensity based 3D-2D registration process, maximizing the similarity between the DXA and a digitally reconstructed radiograph of the combined model. For the construction of the models, an in vitro dataset of QCT scans of 60 anatomical specimens was used. To evaluate the reconstruction accuracy, experiments were performed on simulated DXA images from the QCT scans of 30 anatomical specimens. Comparisons between the reconstructions and the same subject QCT scans showed a mean shape accuracy of 1.2mm, and a mean density error of 81mg/cm3. The results show that this method is capable of accurately reconstructing both the 3D shape and 3D BMD distribution of the proximal femur from DXA images used in clinical routine, potentially improving the diagnosis of osteoporosis and fracture risk assessments at a low radiation dose and low cost. This research has been financially supported by the 3D-FemOs project (3-Dimensional Femur Reconstruction for Osteoporotic Fracture Risk Assessment) with a grant awarded by ACC1Ó Valtec (Tipus 1). In addition this work has been supported by a grant from the Instituto de Salud Carlos III (FI09/00757) and by a grant from the \Deutsche Forschungsgemeinschaft" (LO 730 / 3-1). The authors acknowledge Benedikt Schuler (Institute for Biomedical Image Analysis, UMIT, Hall in Tirol, Austria) and Dr. E. M. Lochmüuller (Universitäts-Frauenklinik der LMU, Mnchen, Germany) for the data collection.