A new approach for the in-vivo characterization of the biomechanical behavior of the breast and the cornea

The characterization of the mechanical behavior of soft living tissues is a big challenge in Biomechanics. The difficulty arises from both the access to the tissues and the manipulation in order to know their physical properties. Currently, the biomechanical characterization of the organs is mainly performed by testing ex-vivo samples or by means of indentation tests. In the first case, the obtained behavior does not represent the real behavior of the organ. In the second case, it is only a representation of the mechanical response of the indented areas. The purpose of the research reported in this thesis is the development of a methodology to in-vivo characterize the biomechanical behavior of two different organs: the breast and the cornea. The proposed methodology avoids invasive measurements to obtain the mechanical response of the organs and is able to completely characterize of the biomechanical behavior of them. The research reported in this thesis describes a methodology to in-vivo characterize the biomechanical behavior of the breast and the cornea. The estimation of the elastic constants of the constitutive equations that define the mechanical behavior of these organs is performed using an iterative search algorithm which optimizes these parameters. The search is based on the iterative variation of the elastic constants of the model in order to increase the similarity between a simulated deformation of the organ and the real one. The similarity is measured by means of a volumetric similarity function which combines overlap-based coefficients and distance-based coefficients. Due to the number of parameters to be characterized as well as the non-convergences that the solution may present in some regions, genetic heuristics were chosen to drive the search algorithm. In the case of the breast, the elastic constants of an anisotropic hyperelastic neo-Hookean model proposed to simulate the compression of the breast during an MRI-guided biopsy were estimated. Results from this analysis showed that the proposed algorithm accurately found the elastic constants of the proposed model, providing an average relative error below 10%. The methodology was validated using breast software phantoms. Nevertheless, this methodology can be easily transferred into its use with real breasts. In the case of the cornea, the elastic constants of a hyperelastic second-order Ogden model were estimated for 24 corneas corresponding to 12 patients. The finite element method was applied in order to simulate the deformation of the human corneas due to non-contact tonometry. The iterative search was applied in order to estimate the elastic constants of the model which approximates the most the simulated deformation to the real one. Results showed that these constants can be estimated with an error of about 5%. After the results obtained for both organs, it can be concluded that the iterative search methodology presented in this thesis allows the \textit{in-vivo} estimation the patient-specific elastic constants of the constitutive biomechanical models that govern the biomechanical behavior of these two organs.Lago Ángel, MÁ. (2014). A new approach for the in-vivo characterization of the biomechanical behavior of the breast and the cornea [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/44116TESI

Determination of Material Parameters of PDMS Material Models by MATLAB

Generally, material constants and their corresponding stability regions of hyperelastic constitutive models can be obtained by well-known commercial software. Nonetheless, reproduction of engineering stress-strain curves from these software do not accurately represent of the uniaxial testing data of a Polydimethylsiloxane material (PDMS). This research aimed to develop PP algorithm, which are MATLAB codes, used to determinate hyperelastic material constants and their stability regions from uniaxial testing data of PDMS material. Hyperelastic constitutive models composed of Neo-Hookean; 3, 5, and 9 parameters Mooney-Rivlin; 2nd and 3rd order Yeoh; and 1st, 2nd and 3rd order Ogden. Moreover, the accuracies of our results were evaluated by the residual sum of squares (RSS) between testing data and hyperelastic models and compared with ones of ANSYS. In Neo-Hookean and Ogden models, the PP algorithm effectively determined material constants from the uniaxial testing data in which their RSS were lower than ones from ANSYS while the strain limit ranges were comparable. However, in Mooney-Rivlin and Yeoh models, the PP algorithm obtained lower RSS but had narrower strain limit ranges than ones from ANSYS. Finally, the Ogden 3rd order model is the accurate constitutive model for PDMS since it obtained not only low RSS but also no strain range limit

Finite Element Software for Rubber Products Design

L

CALIBRATION OF MATERIAL MODELS FOR THE HUMAN CERVICAL SPINE LIGAMENT BEHAVIOUR USING A GENETIC ALGORITHM

Research of biomaterials in loading conditions has become a significant field in the material science nowadays. In order to provide better understanding of the loading effects on material structures, complex material models are usually chosen, depending on their applicability to the material under consideration. In order to provide as accurate as possible the material behavior modeling of the human cervical spine ligaments, the procedure for calibration of two material models has been evaluated. The calibration of material models was based on the genetic algorithm procedure in order to make possible optimization of material parameters identification for the chosen models. The influence of genetic algorithm operators upon the results in evaluated procedure has been tested and discussed here and the simulated behavior of the material has been compared to the experimentally recorded stress stretch relationship of the material under consideration. Since various influential factors contribute to the genetic algorithm performance in calibration of complex material models and identification of material parameters, additional possible improvements have been suggested for further research

Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues

[EN] This paper presents a novel methodology to in-vivo estimate the elastic constants of a constitutive model proposed to characterize the mechanical behavior of the breast tissues. An iterative search algorithm based on genetic heuristics was constructed to in-vivo estimate these parameters using only medical images, thus avoiding invasive measurements of the mechanical response of the breast tissues. For the first time, a combination of overlap and distance coefficients were used for the evaluation of the similar- ity between a deformed MRI of the breast and a simulation of that deformation. The methodology was validated using breast software phantoms for virtual clinical trials, compressed to mimic MRI-guided biopsies. The biomechanical model chosen to characterize the breast tissues was an anisotropic neo-Hookean hyperelastic model. Results from this analysis showed that the algorithm is able to find the elastic constants of the constitutive equations of the proposed model with a mean relative error of about 10%. Furthermore, the overlap between the reference deformation and the simulated deformation was of around 95% showing the good performance of the proposed methodology. This methodology can be easily extended to characterize the real biomechanical behavior of the breast tissues, which means a great novelty in the field of the simulation of the breast behavior for applications such as surgical planing, surgical guidance or cancer diagnosis. This reveals the impact and relevance of the presented work.This project has been funded by MECD (reference AP2009-2414) and US National Institutes of Health (R01 Grant #CA154444), and the US National Science Foundation (III Grant #0916690). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, and NSF. The authors of this manuscript have no conflict of interest with the presented workLago, MA.; Rupérez Moreno, MJ.; Martínez Martínez, F.; Martinez-Sanchis, S.; Bakic, P.; Monserrat, C. (2015). Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues. Expert Systems with Applications. 42(21):7942-7950. https://doi.org/10.1016/j.eswa.2015.05.058S79427950422

Numerical modelling of the growth and remodelling phenomena in biological tissues

Living biological tissues are complex structures that have the capacity of evolving in response to external loads and environmental stimuli. The adequate modelling of soft biological tissue behaviour is a key issue in successfully reproducing biomechanical problems through computational analysis. This study presents a general constitutive formulation capable of representing the behaviour of these tissues through finite element simulation. It is based on phenomenological models that, used in combination with the generalized mixing theory, can numerically reproduce a wide range of material behaviours. First, the passive behaviour of tissues is characterized by means of hyperelastic and finite-strain damage models. A new generalized damage model is proposed, providing a flexible and versatile formulation that can reproduce a wide range of tissue behaviour. It can be particularized to any hyperelastic model and requires identifying only two material parameters. Then, the use of these constitutive models with generalized mixing theory in a finite-strain framework is described and tools to account for the anisotropic behaviour of tissues are put forth. The active behaviour of tissues is characterized through constitutive models capable of reproducing the growth and remodelling phenomena. These are built on the hyperelastic and damage formulations described above and, thus, represent the active extension of the passive tissue behaviour. A growth model considering biological availability is used and extended to include directional growth. In addition, a novel constitutive model for homeostatic-driven turnover remodelling is presented and discussed. This model captures the stiffness recovery that occurs in healing tissues, understood as a recovery or reversal of damage in the tissue, which is driven by both mechanical and biochemical stimuli. Finally, the issue of correctly identifying the material parameters for computational modelling is addressed. An inverse method using optimization techniques is developed to facilitate the identification of these parameters.Els teixits biològics vius són estructures complexes que tenen la capacitat d'evolucionar en resposta a càrregues externes i estímuls ambientals. El modelat adequat del comportament del teixit biològic tou és un tema clau per poder reproduir amb èxit problemes biomecànics mitjançant anàlisi computacional. Aquest estudi presenta una formulació constitutiva general capaç de representar el comportament d'aquests teixits mitjançant la simulació amb elements finits. Es basa en models fenomenològics que, usats en combinació amb la teoria de mescles generalitzada, permeten reproduir numèricament un ampli ventall de comportaments materials. Primer, el comportament passiu dels teixits es caracteritza amb models hiperelàstics i de dany en grans deformacions. Es proposa un model generalitzat de dany que proporciona una formulació versàtil i flexible per poder reproduir una extensa gamma de conductes de teixits. Pot ser particularitzat amb qualsevol model hiperelàstic i requereix identificar tan sols dos paràmetres materials. Llavors, es descriu l'ús d'aquests models constitutius en conjunt amb la teoria generalitzada de mescles, desenvolupada en el marc de grans deformacions, i es presenten eines que permeten incorporar les propietats anisòtropes dels teixits. El comportament actiu dels teixits es caracteritza mitjançant models constitutius capaços de reproduir els fenòmens de creixement i remodelació. Aquests es construeixen sobre les formulacions d'hiperelasticitat i dany descrites anteriorment i, per tant, suposen l'extensió activa del comportament passiu del teixit. Es fa servir un model de creixement que té en compte la disponibilitat biològica de l'organisme, que després s'amplia per incloure dany direccional en el model. També es presenta i analitza un nou model constitutiu per al remodelat per renovació tendint a l’homeòstasi (homeostatic-driven turnover remodelling). Aquest model captura la recuperació de rigidesa que s'observa en teixits que es guareixen. Aquí, el remodelat s'entén com la recuperació o inversió del dany en el teixit i és motivat tant per estímuls mecànics com bioquímics. Finalment, s'aborda el tema de la identificació correcta dels paràmetres materials per al modelat computacional. Es desenvolupa un mètode invers que fa ús de tècniques d'optimització per facilitar la identificació d'aquests paràmetre

Nanoindentation testing of soft polymers : computation, experiments and parameters identification

Since nanoindentation technique is able to measure the mechanical properties of extremely thin layers and small volumes with high resolution, it also became one of the important testing techniques for thin polymer layers and coatings. This dissertation is focusing on the characterization of polymers using nanoindentation, which is dealt with numerical computation, experiments and parameters identification. An analysis procedure is developed with the FEM based inverse method to evaluate the hyperelasticity and time-dependent properties. This procedure is firstly verified with a parameters re-identification concept. An important issue in this dissertation is to take the error contributions in real nanoindentation experiments into account. Therefore, the effects of surface roughness, adhesion force, friction and the real shape of the tip are involved in the numerical model to minimize the systematic error between the experimental responses and the numerical predictions. The effects are quantified as functions or models with corresponding parameters to be identified. Finally, data from uniaxial or biaxial tensile tests and macroindentation tests are taken into account. The comparison of these different loading situations provides a validation of the proposed material model and a deep insight into nanoindentation of polymers.Da Nanoindentation die Messung der mechanischen Eigenschaften von dünnen Schichten und kleinen Volumen mit hoher Auflösung ermöglicht, hat sich diese Messmethode zu einer der wichtigsten Testmethoden für dünne Polymerschichten und -beschichtungen entwickelt. Diese Dissertation konzentriert sich auf die Charakterisierung von Polymeren mittels Nanoindentation, die in Form von numerischen Berechnungen, Experimenten und Parameteridentifikationen behandelt wird. Es wurde ein Auswertungsverfahren mit einer FEM basierten inversen Methode zur Berechnung der Hyperelastizität und der zeitabhängigen Eigenschaften entwickelt. Dieses Verfahren wird zunächst mit einem Konzept der Parameter Re-Identifikation verifiziert. Fehlerquellen wie Oberflächenrauheit, Adhäsionskräfte, Reibung und die tatsächlichen Form der Indenterspitze werden in das numerische Modell eingebunden, um die Abweichungen der numerischen Vorhersagen von den experimentellen Ergebnissen zu minimieren. Diese Einflüsse werden als Funktionen oder Modelle mit dazugehörigen, zu identifizierenden Parametern, quantifiziert. Abschließend werden Messwerte aus uni- oder biaxialen Zugversuchen und Makroindentationsversuchen betrachtet. Der Vergleich dieser verschiedenen Belastungszustände liefert eine Bestätigung des vorgeschlagenen Materialmodells und verschafft einen tieferen Einblick in die bei der Nanoindentation von Polymeren ablaufenden Mechanismen

Efficiency of hyperelastic models for rubber-like materials

International audienceThis paper focuses on the modeling of rubber-like material behaviour under several modes of deformation using hyperelastic constitutive equations. A procedure based on genetic algorithms coupled to classical optimisation methods is proposed to identify the parameters of the models upon experimental data given in the literature. This leads to the classification of nineteen models with respect to criteria related to their capability to predict material behaviour

A new methodology for the in vivo estimation of the elastic constants that characterize the patient-specific biomechanical behavior of the human cornea

This work presents a methodology for the in vivo characterization of the complete biomechanical behavior of the human cornea of each patient. Specifically, the elastic constants of a hyperelastic, second-order Ogden model were estimated for 24 corneas corresponding to 12 patients. The finite element method was applied to simulate the deformation of human corneas due to non-contact tonometry, and an iterative search controlled by a genetic heuristic was used to estimate the elastic parameters that most closely approximates the simulated deformation to the real one. The results from a synthetic experiment showed that these parameters can be estimated with an error of about 5%. The results of 24 in vivo corneas showed an overlap of about 90% between simulation and real deformed cornea and a modified Hausdorff distance of 25 mu m, which indicates the great accuracy of the proposed methodology. (C) 2014 Elsevier Ltd. All rights reserved.This project has been partially funded by MECD (reference AP2009-2414) and MINECO (INNPACTO, IPT-2012-0495-300000).Lago, MA.; Rupérez Moreno, MJ.; Martínez Martínez, F.; Monserrat Aranda, C.; Larra, E.; Gueell, JL.; Peris-Martinez, C. (2015). A new methodology for the in vivo estimation of the elastic constants that characterize the patient-specific biomechanical behavior of the human cornea. Journal of Biomechanics. 48(1):38-43. https://doi.org/10.1016/j.jbiomech.2014.11.009S384348

Design of a pneumatic soft robotic actuator using model-based optimization

In this thesis, the design and optimization process of a novel soft intelligent modular pad (IntelliPad) for the purpose of pressure injury prevention is presented. The structure of the IntelliPad consists of multiple individual multi-chamber soft pneumatic-driven actuators that use pressurized air and vacuum. Each actuator is able to provide both vertical and horizontal motions that can be controlled independently. An analytical modeling approach using multiple cantilever beams and virtual springs connected in a closed formed structure was developed to analyze the mechanical performance of the actuator. The analytical approach was validated by a finite element analysis. For optimizing the actuator\u27s mechanical performance, firefly algorithm and deep reinforcement learning-based design optimization frameworks were developed with the purpose of maximizing the horizontal motion of the top surface of the actuators, while minimizing its corresponding effect on the vertical motion. Four optimized designs were fabricated. The actuators were tested and validated experimentally to demonstrate their required mechanical performance in order to regulate normal and shear stresses at the skin-pad interface for pressure injury prevention applications