Projet ANR-06-BLAN-0357 – BoneChar : Model and experimental validation for the biomechanical ultrasonic characterization in presence of uncertainties: application to bone
Programme : Programme blanc (BLANC) 2006
Année de financement : 2006
Coordinateur du projet : UNIVERSITE PARIS-EST CRETEIL VAL DE MARNE (UNIVERSITE PARIS-EST CRETEIL VAL DE MARNE)
Partenaires : Université Pierre et Marie Curie; Université Paris-Est Créteil Val-de-Marne; Université Paris-Est Marne-la-Vallée
Montant : 80000.0 euros
Date de début : 2006
Durée : 36 mois
Résumé : 1 - Scientific background and objectives -  Quantitative ultrasonic (QUS) techniques can predict fracture risk at least as well as X-ray methods. Because ultrasounds convey information on the mechanical and geometrical properties of bone, they can potentially improve diagnosis of bone pathologies. Several models of US-bone interactions have been developed but they can not predict in vivo bone response to ultrasound. The proper selection of values for physical parameters to be used in the models remains the main difficulty because many characteristics are uncertain in most biomechanical problems. Thus, new modelling strategies based on probabilistic methods taking into account uncertainties can provide a relevant response to this difficulty. -  Our project aims at unlocking model developments in biomechanics by a proper account of biological-intrinsic uncertainties. We will address specifically a model of the QUS axial transmission (AT) technique. Up to now, despite a large body of theoretical and experimental work, the experimental device developed by one of the partner can not be used to obtain independently mechanical or geometrical properties of bone. New models developed during the project will allow solving the forward and inverse problems. -  -  2 - Description of project, methodology -  The aim of the first phase of this project is to develop numerical simulation tools for the resolution of the forward problem (determination of the response of the structure). They will be based on the biomechanical modelling of a configuration of bone capable of accounting for ultrasonic wave propagation phenomena in these heterogeneous media. Therefore, the developed models will describe the coupling between ultrasound and the biological structure by taking into account bone properties (anisotropy, attenuation) as well as its main geometrical parameters. Two simulation tools, one finite element approach and one hybrid method (analytical mixed with finite element) will be elaborated. The validation of both approaches will be performed by comparing the results obtained with both methods and then by comparing with experimental results in vitro. -  These simulation tools will then be used for the second phase of the project, constituted by the development of probabilistic models. A statistical treatment of the data base constituted from the existing experimental results will firstly be performed. Secondly, the probabilistic models of uncertainties due to the geometrical (cortical bone width) and mechanical properties of bone will be constructed using the maximum entropy principle. It permits constructing explicitly accurate models for the random variables and for the stochastic fields, only using the available information. An effort will be made to focus on bone tissue models: isotropic, transverse isotropic, then anisotropic, in the case of a homogeneous and then heterogeneous medium. For all models, a stochastic solver will be developed. The stochastic approach will allow the prediction of the random response that will be compared to experimental results of the in vivo experimental data base. -  The simulation tools will also be used to solve the inverse problem constituted by the determination of the mechanical and geometrical properties of cortical bone. Inverse problems in the domain of mechanical characterization are often mathematically ill-posed. The probabilistic approach allows determining the sensitivity of the AT technique to each mechanical and geometrical parameter independently. This will lead to the choice of a set of parameters to be determined for the solution of the inverse problem. Cost functions will then be constructed using signal-processing techniques and then used to compare experimental and simulated results obtained with each set of parameters. Adapted minimization techniques will lead to a compromise between the computation time and the accuracy of the method. Eventually, robust resolution methods will be develo