Estimation of the hyperelastic parameters of fresh human oropharyngeal soft tissues using indentation testing

In vivo mechanical response of thigh soft tissues under compression: A two-layer model allows an improved representation of the local tissue kinematics

Biomechanical parameters have the potential to be used as physical markers for prevention and diagnosis. Finite Element Analysis (FEA) is a widely used tool to evaluate these parameters in vivo. However, the development of clinically relevant FEA requires personalisation of the geometry, boundary conditions, and constitutive parameters. This contribution focuses on the characterisation of mechanical properties in vivo which remains a significant challenge for the community. The aim of this retrospective study is to evaluate the sensitivity of the computed elastic parameters (shear modulus of fat and muscle tissues) derived by inverse analysis as a function of the geometrical modelling assumption (homogenised monolayer vs bilayer) and the formulation of the cost function. The methodology presented here proposes to extract the experimental force-displacement response for each tissue layer (muscle and fat) and construct the associated Finite Element Model for each volunteer, based on data previously collected in our group (N = 7 volunteers) as reported in (Fougeron et al., 2020). The sensitivity analysis indicates that the choice of the cost function has minimal impact on the topology of the response surface in the parametric space. Each surface displays a valley of parameters that minimises the cost function. The constitutive properties of the thigh (reported as median ± interquartile range) were determined to be (μ=198±322Pa,α=37) for the monolayer and (μmuscle=1675±1127Pa,αmuscle=22±14,μfat=537±1131Pa,αfat=32±7) for the bilayer. A comparison of the homogenised monolayer and bilayer models showed that adding a layer reduces the error on the local force displacement curves, increasing the accuracy of the local kinematics of soft tissues during indentation. This allows for an increased understanding of load transmission in soft tissue. The comparison of the two models in terms of strains indicates that the modelling choice significantly influences the localization of maximal compressive strains. These results support the idea that the biomechanical community should conduct further work to develop reliable methodologies for estimating in vivo strain in soft tissue.

Elastic parameter identification of three-dimensional soft tissue based on deep neural network

In the field of virtual surgery and deformation simulation, the identification of elastic parameters of human soft tissues is a critical technology that directly affects the accuracy of deformation simulation. Current research on soft tissue deformation simulation predominantly assumes that the elasticity of tissues is fixed and already known, leading to the difficulty in populating with the elasticity measured or identified from specific tissues of real patients. Existing elasticity modeling efforts struggle to be implemented on irregularly structured soft tissues, failing to adapt to clinical surgical practices. Therefore, this paper proposes a new method for identifying human soft tissue elastic parameters based on the finite element method and the deep neural network, UNet. This method requires only the full-field displacement data of soft tissues under external loads to predict their elastic distribution. The performance and validity of the algorithm are assessed using test data and clinical data from rhinoplasty surgeries. Experiments demonstrate that the method proposed in this paper can achieve an accuracy of over 99% in predicting elastic parameters. Clinical data validation shows that the predicted elastic distribution can reduce the error in finite element deformation simulations by more than 80% at the maximum compared to the error with traditional uniform elastic parameters, effectively enhancing the computational accuracy in virtual surgery simulations and soft tissue deformation modeling.

The surface mechanics of cooked rice as influenced by gastric fluids measured using a micro texture analyzer

In this study, a micro texture analyzer (MTA) was employed to explore the texture characteristics of the surface of an individual steamed rice (SR) and fried rice (FR) grain exhibited in four simulated digestion environments in vitro. The elastic modulus, hardness and elastic index of the single cooked rice particle were measured using the MTA. The hardness of SR particles decreased by 66, 81, 89.1, and 95% after simulated digestion in distilled water, HCl, simulated gastric fluid (SGF), and simulated salivary and gastric fluid (SSF + SGF), respectively. This is in line with the most significant volume expansion and structure ruptures when digested in SSF + SGF. Similar mechanical and structural behaviors were shown for FR, but the hardness and elastic modulus decreased less than those of SR under the same digestion conditions. The different surface mechanics are consistent with the reduced expansion and more compact structure with smaller voids in FR during in vitro digestion. This could be attributed to the encapsulation by frying oil on the surface that would retard the diffusion of digestive fluids into the rice kernels. A weak negative correlation was found between the elastic modulus and the moisture content of the cooked rice. The present study has quantitatively assessed the surface mechanics of cooked rice as influenced by gastric fluids using the MTA. This is practically meaningful for gaining an in-depth understanding of the influence of textural modifications on disintegration of solid foods and release of nutrients during digestion.

Mechanical properties of whole-body soft human tissues: a review

The mechanical properties of soft tissues play a key role in studying human injuries and their mitigation strategies. While such properties are indispensable for computational modelling of biological systems, they serve as important references in loading and failure experiments, and also for the development of tissue simulants. To date, experimental studies have measured the mechanical properties of peripheral tissues (e.g. skin)in-vivoand limited internal tissuesex-vivoin cadavers (e.g. brain and the heart). The lack of knowledge on a majority of human tissues inhibit their study for applications ranging from surgical planning, ballistic testing, implantable medical device development, and the assessment of traumatic injuries. The purpose of this work is to overcome such challenges through an extensive review of the literature reporting the mechanical properties of whole-body soft tissues from head to toe. Specifically, the available linear mechanical properties of all human tissues were compiled. Non-linear biomechanical models were also introduced, and the soft human tissues characterized using such models were summarized. The literature gaps identified from this work will help future biomechanical studies on soft human tissue characterization and the development of accurate medical models for the study and mitigation of injuries.