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Pré-Publication, Document De Travail Année : 2023

Bi-layer stiffness identification of soft tissues by suction


Background The in vivo and non-invasive mechanical characterisation of biological soft tissue is a challenge even under moderate quasi-static loading. Suction-based devices represent a promising technique However, the underlying tissues are often assumed to be homogeneous and the heavy and time-consuming postprocessing times hinders any clinical application. Objectives The aim is to improve suction-based mechanical characterization of soft tissues considered as bilayered structures. The whole method shall be practical and unexpensive. Inverse identification of the Young’s moduli of the bilayers should be performed in almost real-time for any patient. Methods An original suction system is proposed based on volume measurements. Cyclic partial vacuum is applied under small deformation using suction cups of aperture diameters ranging from 4 to 30~mm. An inverse methodology is implemented to estimate both of the bilayer elastic stiffness, and optionally the upper layer thickness, based on the interpolation of an off-line finite element database. The setup is validated on silicone bilayer phantoms, then tested in vivo on the abdomen skin of one healthy volunteer. Results On bilayer silicone phantoms with superficial upper layer thickness of 3mm, both Young's moduli identified by suction or uniaxial tension presented a relative difference lower than 10%. Preliminary tests on in vivo abdomen tissue provided the skin and underlying adipose tissue Young's Moduli at 54kPa and 4.8kPa respectively. Once the experimental data were acquired, inverse identification was performed in less than one minute. Conclusions This approach is promising to evaluate elastic moduli in vivo at small strain of bilayered tissues.
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Dates et versions

hal-03718672 , version 1 (09-07-2022)
hal-03718672 , version 2 (06-01-2023)


  • HAL Id : hal-03718672 , version 2


N Connesson, N Briot, P Y Rohan, P A Barraud, S A Elahi, et al.. Bi-layer stiffness identification of soft tissues by suction. 2023. ⟨hal-03718672v2⟩
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