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Nicolas Bochud

Researcher at Paris 12 Val de Marne University

Publications -  47
Citations -  526

Nicolas Bochud is an academic researcher from Paris 12 Val de Marne University. The author has contributed to research in topics: Cortical bone & Ultrasonic sensor. The author has an hindex of 12, co-authored 43 publications receiving 382 citations. Previous affiliations of Nicolas Bochud include French Institute of Health and Medical Research & University of Granada.

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Predicting bone strength with ultrasonic guided waves

TL;DR: Results show that the free plate model allows retrieving reliable waveguide properties, despite the presence of soft tissue, and suggest that the more sophisticated bilayer model, although it is more precise to predict experimental data in the forward problem, could turn out to be hardly manageable for solving the inverse problem.
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In Vivo Characterization of Cortical Bone Using Guided Waves Measured by Axial Transmission

TL;DR: This pilot study demonstrates the potential of bidirectional AT for the in vivo assessment of cortical thickness, a bone strength-related factor, by measuring multimode guided waves in vivo and infer from it cortical thickness.
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Bone cortical thickness and porosity assessment using ultrasound guided waves: An ex vivo validation study.

TL;DR: This study presents the first validation study for assessing cortical thickness and porosity using the axial transmission technique, and finds that the automatic signal processing minimizes operator-dependent errors for parameters determination.
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Ultrasound‐Based Estimates of Cortical Bone Thickness and Porosity Are Associated With Nontraumatic Fractures in Postmenopausal Women: A Pilot Study

TL;DR: Cortical BDAT measurements may be considered useful for assessing fracture risk in postmenopausal women, and there was a significant association between increased Ct.Po and vertebral and wrist fractures when these fractures were not associated with any measured aBMD variables.
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Towards real-time assessment of anisotropic plate properties using elastic guided waves

TL;DR: A model-based inverse problem solution is proposed to optimally recover the properties of interest using genetic algorithms, which allow identifying from a single out-of-symmetry axis measurement the full set of anisotropic elastic coefficients and either the plate thickness or the propagation direction.