Patients with osteoporosis have an increased risk of sustaining fractures because of the low bone mineral density (BMD) and altered bone micro-architecture which are characteristic features of the disease. Although a good correlation exists between BMD and fracture risks, many other factors influence this relationship. While there is consensus that patients with osteoporosis should be investigated and treated, the issue is much less clear for patients with osteopenia. Because osteopenia is so prevalent, it would be unrealistic to treat all patients with this condition. Therefore, there is a need to identify those patients who are at risk of sustaining a fracture and would benefit most from the available therapy. Providing treatment to the appropriate risk group would not only reduce the number of fractures, but could also reduce the adverse effects associated with treatment, as treating patients earlier could shorten the treatment time. The availability of tools to select patients at risk of fracture should change the impact of the disease.

Fracture risk reduction is the main goal of treating osteoporosis, a condition which, in the absence of fragility fractures, is diagnosed by bone densitometry. Bone density, however, although an important factor predicting fracture risk, is not the only one and several other factors modulate the fracture risk such as the patient's age, body mass index, family history, cigarette smoking, medications and the risk of falling: most fractures are preceded by falls. When developing a treatment strategy it is therefore important to take into consideration other factors apart from bone density. Several algorithms and instruments are available for this purpose. The FRAX (Fracture Risk Assessment) tool developed under the aegis of the World Health Organization and the Garvan Fracture Risk Calculator are commonly used to estimate the patient's fracture risk. Both have advantages and limitations. It must be emphasized, however, that treatment decisions are clinical ones.

Fracture risk reduction is the main goal of treating osteoporosis, a condition which, in the absence of fragility fractures, is diagnosed by bone densitometry. Bone density, however, although an important factor predicting fracture risk, is not the only one and several other factors modulate the fracture risk such as the patient's age, body mass index, family history, cigarette smoking, medications and the risk of falling: most fractures are preceded by falls. When developing a treatment strategy it is therefore important to take into consideration other factors apart from bone density. Several algorithms and instruments are available for this purpose. The FRAX (Fracture Risk Assessment) tool developed under the aegis of the World Health Organization and the Garvan Fracture Risk Calculator are commonly used to estimate the patient's fracture risk. Both have advantages and limitations. It must be emphasized, however, that treatment decisions are clinical ones.

In this thesis a method was developed to reconstruct both the 3D shape and the BMD distribution of bone structures from Dual-energy X-ray Absorptiometry (DXA) images. The method incorporates a statistical model built from a large dataset of Quantitative Computed Tomography (QCT) scans together with a 3D-2D intensity based registration process. The method was evaluated for its ability to reconstruct the proximal femur from a single DXA image. The resulting parameters of the reconstructions were subsequently evaluated for their hip fracture discrimination ability. The reconstruction method was finally extended to the reconstruction of the lumbar vertebrae from anteroposterior and lateral DXA, thereby incorporating a multi-object and multi-view approach. These techniques can potentially improve the fracture risk estimation accuracy over current clinical practice. / En esta tesis se desarrolló un método para reconstruir tanto la forma 3D de estructuras óseas como la distribución de la DMO a partir de una sola imagen de DXA. El método incorpora un modelo estadístico construido a partir de una gran base de datos de QCT junto con una técnica de registro 3D-2D basada en intensidades. Se ha evaluado la capacidad del método para reconstruir la parte proximal del fémur a partir de una imagen DXA. Los parámetros resultantes de las reconstrucciones fueron evaluados posteriormente por su capacidad en discriminar una fractura de cadera. Por fin, se extendió el método a la reconstrucción de las vértebras lumbares a partir de DXA anteroposterior y lateral incorporando así un enfoque multi-objeto y multi-vista. Estos técnicas pueden potencialmente mejorar la precisión en la estimación del riesgo de fractura respecto a la estimación que ofrece la práctica clínica actual.