Bayesian Inference of a Human Bone and Biomaterials Using Ultrasonic Transmitted Signals
Résumé
Ultrasonic techniques could be good candidates to aid the assessment of osteoporosis detection, due to their non-intrusiveness and low cost. While earlier studies made use of the measured ultrasonic phase velocity and attenuation inside the bone, very few have considered an inverse identication of both the intrinsic pore microstructure and the mechanical properties of the bone, based on Biot's model. The main purpose of this work is to present an in vitro methodology for bone identication, adopting a statistical Bayesian inference technique using ultrasonic transmitted signals, which allows the retrieval of the identied parameters and their uncertainty. In addition to the bone density, Young's modulus and Poisson's ratio, the bone pore microstructure parameters (porosity, tortuosity and viscous length) are identied. These additional microstructural terms could improve our knowledge on the correlations between bone microstructure and bone diseases, since they provide more information on the trabecular structure. In general, the exact properties of the saturating uid are unknown (bone marrow and blood in the case of bone study) so in this work, the uid properties (water) are identied during the inference as a proof of concept.
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