CFM 2019

Mechanical parameters identification of keloid and surrounding healthy skin using Digital Image Correlation measurements in vivo
Aflah Elouneg  1@  , Danas Sutula  1, 2@  , Marco Sensale  1, 3@  , Franz Chouly  4@  , Jérôme Chambert  1@  , Arnaud Lejeune  1@  , Davide Baroli  3@  , Paul Hauseux  3@  , Stephane Bordas  3@  , Emmanuelle Jacquet  1@  
1 : Univ. Bourgogne Franche Comté, FEMTO-ST Institute, UFC/CNRS/ENSMM/UTBM, Department of Applied Mechanics, Besançon, France
Université de Franche-Comté, Institute Femto-st : FEMTO-ST, Université de Franche-Comté
2 : Technical University of Liberec, Faculty of Tissue Engineering, Liberec, Czech Republic
3 : University of Luxembourg, Institute of Computational Engineering, Luxembourg
4 : Univ. Bourgogne Franche Comté, Institut de Mathématiques de Bourgogne, Dijon, France.  (IMB)
Université de Bourgogne, Centre National de la Recherche Scientifique : UMR5584

The keloid growth (benign tumors of human skin) is not exclusively due to biological or genetic factors. The presence of anatomical sites favorable to the appearance of these tumors while others are lacking them attests to the importance of the mechanical environment of the tissue. The main objective of the project is to understand these alterations from a mechanical point of view in order to be able to prevent their expansion.

The case study consists in a bi-material structure composed of a keloid skin surrounded by healthy skin located on upper left arm of a young female. From the experimental measurements in vivo, we perform a mechanical analysis to characterize the mechanical stress field over the entire area and on the interface ‘healthy skin/keloid skin'. Since the mechanical behavior of the tumorous skin is unknown, many physical models have to be developed and assessed through numerical modeling.

In our study, we assume that the both materials, healthy skin and keloid skin, are isotropic and hyperelastic soft tissues whose hyperelastic strain energies can be established on the basis of phenomenological models. The parameters of these models are estimated using an inverse solution approach developed and implemented in Python using FEniCS computational framework for finite element analysis.

The whole procedure from in vivo experimental test to stress field computation would be described. As the corresponding displacement field is measured by Digital Image Correlation, experimental data need to be filtered to reduce measurement noise. Then, an inverse method, known as Finite Element Model Updating (FEMU), is performed on these data. In addition, the objective cost function takes account difference between numerical and experimental reaction force as a constraint. Once a set of mechanical parameters for the both healthy skin and the keloid skin are identified, the stress fields around the keloid are calculated. Matching preferential directions are determined in order to define as precisely as possible the specifications of a device for preventing the growth of keloids.


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