A finite element model of the face including an orthotropic skin model under in vivo tension

Finite element analysis of biomechanical interactions of a subcutaneous suspension suture and human face soft-tissue: a cadaver study

In order to study the local interactions between facial soft-tissues and a Silhouette Soft® suspension suture, a CE marked medical device designed for the repositioning of soft tissues in the face and the neck, Finite element simulations were run, in which a model of the suture was embedded in a three-layer Finite Element structure that accounts for the local mechanical organization of human facial soft tissues. A 2D axisymmetric model of the local interactions was designed in ANSYS, in which the geometry of the tissue, the boundary conditions and the applied loadings were considered to locally mimic those of human face soft tissue constrained by the suture in facial tissue repositioning. The Silhouette Soft suture is composed of a knotted thread and sliding cones that are anchored in the tissue. Hence, simulating these interactions requires special attention for an accurate modelling of contact mechanics. As tissue is modelled as a hyper-elastic material, the displacement of the facial soft tissue changes in a nonlinear way with the intensity of stress induced by the suture and the number of the cones. Our simulations show that for a 4-cone suture a displacement of 4.35 mm for a 2.0 N external loading and of 7.6 mm for 4.0 N. Increasing the number of cones led to the decrease in the equivalent local strain (around 20%) and stress (around 60%) applied to the tissue. The simulated displacements are in general agreement with experimental observations.

Fast 3D Face Reconstruction from a Single Image Using Different Deep Learning Approaches for Facial Palsy Patients

The 3D reconstruction of an accurate face model is essential for delivering reliable feedback for clinical decision support. Medical imaging and specific depth sensors are accurate but not suitable for an easy-to-use and portable tool. The recent development of deep learning (DL) models opens new challenges for 3D shape reconstruction from a single image. However, the 3D face shape reconstruction of facial palsy patients is still a challenge, and this has not been investigated. The contribution of the present study is to apply these state-of-the-art methods to reconstruct the 3D face shape models of facial palsy patients in natural and mimic postures from one single image. Three different methods (3D Basel Morphable model and two 3D Deep Pre-trained models) were applied to the dataset of two healthy subjects and two facial palsy patients. The reconstructed outcomes were compared to the 3D shapes reconstructed using Kinect-driven and MRI-based information. As a result, the best mean error of the reconstructed face according to the Kinect-driven reconstructed shape is 1.5±1.1 mm. The best error range is 1.9±1.4 mm when compared to the MRI-based shapes. Before using the procedure to reconstruct the 3D faces of patients with facial palsy or other facial disorders, several ideas for increasing the accuracy of the reconstruction can be discussed based on the results. This present study opens new avenues for the fast reconstruction of the 3D face shapes of facial palsy patients from a single image. As perspectives, the best DL method will be implemented into our computer-aided decision support system for facial disorders.

Reinforcement learning coupled with finite element modeling for facial motion learning

BACKGROUND AND OBJECTIVE

Facial palsy patients or patients with facial transplantation have abnormal facial motion due to altered facial muscle functions and nerve damage. Computer-aided system and physics-based models have been developed to provide objective and quantitative information. However, the predictive capacity of these solutions is still limited to explore the facial motion patterns with emerging properties. The present study aims to couple the reinforcement learning and the finite element modeling for facial motion learning and prediction.

METHODS

A novel modeling workflow for learning facial motion was developed. A physically-based model of the face within the Artisynth modeling platform was used. Information exchange protocol was proposed to link reinforcement learning and rigid multi-bodies dynamics outcomes. Two reinforcement learning algorithms (deep deterministic policy gradient (DDPG) and Twin-delayed DDPG (TD3)) were used and implemented to drive the simulations of symmetry-oriented and smile movements. Numerical outcomes were compared to experimental observations (Bosphorus database) for evaluation and validation purposes.

RESULTS

As result, after more than 100 episodes of exploring the environment, the agent starts to learn from previous trials and can find the optimal policy after more than 300 episodes of training. Regarding the symmetry-oriented motion, the muscle excitations predicted by the trained agent help to increase the value of reward from R = -2.06 to R = -0.23, which counts for ∼89% improvement of the symmetry value of the face. For smile-oriented motion, two points at the edge of the mouth move up 0.35 cm, which is within the range of movements estimated from the Bosphorus database (0.4 ± 0.32 cm).

CONCLUSIONS

The present study explored the muscle excitation patterns by coupling reinforcement learning with a detailed finite element model of the face. We developed, for the first time, a novel coupling scheme to integrate the finite element simulation into the reinforcement learning process for facial motion learning. As perspectives, this present workflow will be applied for facial palsy and facial transplantation patients to guide and optimize the functional rehabilitation program.

A quantitative analysis of biomechanical lung model consistency using 5DCT datasets

PURPOSE

Lung biomechanical models are important for understanding and characterizing lung anatomy and physiology. A key parameter of biomechanical modeling is the underlying tissue elasticity distribution. While human lung elasticity estimations do not have ground truths, model consistency checks can and should be employed to gauge the stability of the estimation techniques. This work proposes such a consistency check using a set of 10 subjects.

METHODS

We hypothesize that lung dynamics will be stable over a 2-3 min time period and that this stability can be employed to check biomechanical estimation stability. For this purpose, two sets of 12 fast helical free breathing computed tomography scans (FHFBCT) were acquired back-to-back for each of the subjects. A published breathing motion model [five-dimensional CT (5DCT)] was generated from each set. Both of the models were used to generate two biomechanical modeling input sets: (a) The lung geometry at the end-exhalation, and (b) the voxel displacement map that mapped the end-exhalation lung geometry with the end-inhalation lung geometry. Finite element biomechanical lung models were instantiated using the end-exhalation lung geometries. The models included voxel-specific lung tissue elasticity values and were optimized using a gradient search approach until the biomechanical model-generated displacement maps matched those of the 5DCT voxel displacement maps. Finally, the two elasticity distributions associated with each of the patient 5DCTs were quantitatively compared. Because the end-exhalation geometries differed slightly between the two scan datasets, the elasticity distributions were deformably mapped to one of the exhalation datasets.

RESULTS

For the 10 patients, on average, 90% of parenchymal voxels had <2 kPa Young's modulus difference between the two estimations, with a mean voxel difference of only 0.6 kPa. Similarly, 97% of the parenchymal voxels had <2 mm displacement difference between the two models with a mean difference of 0.48 mm. Furthermore, overlapping elasticity histograms for voxels between -600 and -900 HU (parenchymal tissues) showed that the histograms were consistent between the two estimations.

CONCLUSION

In this paper, we demonstrated that biomechanical lung models can be consistently estimated when using motion-model based imaging datasets, even though the models were created from scans acquired at different breaths.

Influences of sodium and glycosaminoglycans on skin oedema and the potential for ulceration: a finite-element approach

Venous ulcers are chronic transcutaneous wounds common in the lower legs. They are resistant to healing and have a 78% chance of recurrence within 2 years. It is commonly accepted that venous ulcers are caused by the insufficiency of the calf muscle pump, leading to blood pooling in the lower legs, resulting in inflammation, skin oedema, tissue necrosis and eventually skin ulceration. However, the detailed physiological events by which inflammation contributes to wound formation are poorly understood. We therefore sought to develop a model that simulated the inflammation, using it to determine the internal stresses and pressure on the skin that contribute to venous ulcer formation. A three-layer finite-element skin model (epidermis, dermis and hypodermis) was developed to explore the roles in wound formation of two inflammation identifiers: glycosaminoglycans (GAG) and sodium. A series of parametric studies showed that increased GAG and sodium content led to oedema and increased tissue stresses of 1.5 MPa, which was within the reported range of skin tissue ultimate tensile stress (0.1-40 MPa). These results suggested that both the oedema and increased fluid pressure could reach a threshold for tissue damage and eventual ulcer formation. The models presented here provide insights to the pathological events associated with venous insufficiency, including inflammation, oedema and skin ulceration.

Spatial-Temporal Changes of Mechanical Microenvironment in Skin Wounds During Negative Pressure Wound Therapy

Cell migration, proliferation, and differentiation are regulated by mechanical cues during skin wound healing. Negative pressure wound therapy (NPWT) reduces the healing period by optimizing the mechanical microenvironment of the wound bed. Under NPWT, it remains elusive how the mechanical microenvironment (e.g., stiffness, strain gradients) changes both in time and space during wound healing. To illustrate this, the healing time of full-thickness skin wounds under NPWT, with pressure settings ranging from -50 to -150 mm Hg, were evaluated and compared with gauze dressing treatments (control group), and three-dimensional finite element models of full-thickness skin wounds on days 1 and 5 after treatment were developed on the basis of MR 3D imaging data. Shear wave elastography (SWE) was applied to detect the stiffness of wound soft tissue on days 1 and 5, and nonlinear finite element analysis (FEA) was used to represent the spatial-temporal environment of the 3D strain field of the wound under NPWT vs the control group. Compared with the control group, NPWT with -50, -80, and -125 mm Hg promoted wound healing. SWE showed that the elastic modulus of wounded skin increased during healing. Meanwhile, the elastic modulus in wounded skin under NPWT was significantly smaller than in the control group. Strain and its gradient decreased under NPWT during wound healing, while no significant change was observed in the control group. This study, which is based on MR 3D imaging, shear wave elastography, and nonlinear FEA, provides an in-depth understanding of changes of the skin mechanical microenvironment under NPWT in the time-space dimension and the associated wound healing.

Detection of inhomogeneity by the observation of the surface of the material simulating biological tissues

This paper presents a research which involves the observation of the movement of points presented on a material surface under the influence of mechanical extortion. Tests were performed using two 15 mm silicone layers, one of which contained 1 mm thick elements of nitrile-butadiene rubber. Analysed materials were structurally heterogeneous tissue phantoms. Test results that were obtained indicated that the developed method allows detecting inhomogeneity and its approximate location, what may be used in pathological state prevention.

A model of human skin under large amplitude oscillatory shear

Skin mechanics is of importance in various fields of research when accurate predictions of the mechanical response of skin is essential. This study aims to develop a new constitutive model for human skin that is capable of describing the heterogeneous, nonlinear viscoelastic mechanical response of human skin under shear deformation. This complex mechanical response was determined by performing large amplitude oscillatory shear (LAOS) experiments on ex vivo human skin samples. It was combined with digital image correlation (DIC) on the cross-sectional area to assess heterogeneity. The skin is modeled as a one-dimensional layered structure, with every sublayer behaving as a nonlinear viscoelastic material. Heterogeneity is implemented by varying the stiffness with skin depth. Using an iterative parameter estimation method all model parameters were optimized simultaneously. The model accurately captures strain stiffening, shear thinning, softening effect and nonlinear viscous dissipation, as experimentally observed in the mechanical response to LAOS. The heterogeneous properties described by the model were in good agreement with the experimental DIC results. The presented mathematical description forms the basis for a future constitutive model definition that, by implementation in a finite element method, has the capability of describing the full 3D mechanical behavior of human skin.

Implementation and validation of constitutive relations for human dermis mechanical response

Finite element models in conjunction with adequate constitutive relations are pivotal in several physiological and medical applications related to both native and engineered tissues, allowing to predict the tissue response under various loading states. In order to get reliable results, however, the validation of the constitutive models is crucial. Therefore, the main purpose of this work is to provide an experimental-computational approach to the biomechanical investigation of soft tissues such as the dermis. This is accomplished by implementing and validating three widely adopted hyperelastic constitutive models (the Ogden, the Holzapfel, and the Gasser-Ogden-Holzapfel laws) supposed to be adequate to reproduce human reticular dermis mechanical behavior. Biaxial experimental data have represented the basis for the determination of the respective material parameters identified thanks to the definition of a cost function accounting for the discrepancy between experimental and predicted data. Afterwards, the experimental tests have been reproduced through finite element simulations. Hence, the constitutive laws have been validated comparing experimental and numerical outcomes in terms of displacements of four reference points and stress-strain relations. Hence, an experimental-numerical framework is proposed for the investigation of collagenous tissues, which could become more accurate with larger and independent experimental datasets. Graphical abstract ᅟ.

IMAGE-BASED SKELETAL MUSCLE COORDINATION: CASE STUDY ON A SUBJECT SPECIFIC FACIAL MIMIC SIMULATION

Facial muscle coordination is a fundamental mechanism for facial mimics and expressions. The understanding of this complex mechanism leads to better diagnosis and treatment of facial disorders like facial palsy or disfigurement. The objective of this work was to use magnetic resonance imaging (MRI) technique to characterize the activation behavior of facial muscles and then simulate their coordination mechanism using a subject specific finite element model. MRI data of lower head of a healthy subject were acquired in neutral and in the pronunciation of the sound [o] positions. Then, a finite element model was derived directly from acquired MRI images in neutral position. Transversely-isotropic, hyperelastic, quasi-incompressible behavior law was implemented for modeling facial muscles. The simulation to produce the pronunciation of the sound [o] was performed by the cumulative coordination between three pairs of facial mimic muscles (Zygomaticus Major (ZM), Levator Labii Superioris (LLS), Levator Anguli Oris (LAO)). Mean displacement amplitude showed a good agreement with a relative deviation of 15% between numerical outcome and MRI-based measurement when all three muscles are involved. This study elucidates, for the first time, the facial muscle coordination using in vivo data leading to improve the model understanding and simulation outcomes.

sEMG-assisted inverse modelling of 3D lip movement: a feasibility study towards person-specific modelling

We propose a surface-electromyographic (sEMG) assisted inverse-modelling (IM) approach for a biomechanical model of the face to obtain realistic person-specific muscle activations (MA) by tracking movements as well as innervation trajectories. We obtained sEMG data of facial muscles and 3D positions of lip markers in six volunteers and, using a generic finite element (FE) face model in ArtiSynth, performed inverse static optimisation with and without sEMG tracking on both simulation data and experimental data. IM with simulated data and experimental data without sEMG data showed good correlations of tracked positions (0.93 and 0.67) and poor correlations of MA (0.27 and 0.20). When utilising the sEMG-assisted IM approach, MA correlations increased drastically (0.83 and 0.59) without sacrificing performance in position correlations (0.92 and 0.70). RMS errors show similar trends with an error of 0.15 in MA and of 1.10 mm in position. Therefore, we conclude that we were able to demonstrate the feasibility of an sEMG-assisted inverse modelling algorithm for the perioral region. This approach may help to solve the ambiguity problem in inverse modelling and may be useful, for instance, in future applications for preoperatively predicting treatment-related function loss.

Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model

PURPOSE

Functional inoperability in advanced oral cancer is difficult to assess preoperatively. To assess functions of lips and tongue, biomechanical models are required. Apart from adjusting generic models to individual anatomy, muscle activation patterns (MAPs) driving patient-specific functional movements are necessary to predict remaining functional outcome. We aim to evaluate how volunteer-specific MAPs derived from surface electromyographic (sEMG) signals control a biomechanical face model.

METHODS

Muscle activity of seven facial muscles in six volunteers was measured bilaterally with sEMG. A triple camera set-up recorded 3D lip movement. The generic face model in ArtiSynth was adapted to our needs. We controlled the model using the volunteer-specific MAPs. Three activation strategies were tested: activating all muscles [Formula: see text], selecting the three muscles showing highest muscle activity bilaterally [Formula: see text]-this was calculated by taking the mean of left and right muscles and then selecting the three with highest variance-and activating the muscles considered most relevant per instruction [Formula: see text], bilaterally. The model's lip movement was compared to the actual lip movement performed by the volunteers, using 3D correlation coefficients [Formula: see text].

RESULTS

The correlation coefficient between simulations and measurements with [Formula: see text] resulted in a median [Formula: see text] of 0.77. [Formula: see text] had a median [Formula: see text] of 0.78, whereas with [Formula: see text] the median [Formula: see text] decreased to 0.45.

CONCLUSION

We demonstrated that MAPs derived from noninvasive sEMG measurements can control movement of the lips in a generic finite element face model with a median [Formula: see text] of 0.78. Ultimately, this is important to show the patient-specific residual movement using the patient's own MAPs. When the required treatment tools and personalisation techniques for geometry and anatomy become available, this may enable surgeons to test the functional results of wedge excisions for lip cancer in a virtual environment and to weigh surgery versus organ-sparing radiotherapy or photodynamic therapy.

Mathematical and computational modelling of skin biophysics: a review

The objective of this paper is to provide a review on some aspects of the mathematical and computational modelling of skin biophysics, with special focus on constitutive theories based on nonlinear continuum mechanics from elasticity, through anelasticity, including growth, to thermoelasticity. Microstructural and phenomenological approaches combining imaging techniques are also discussed. Finally, recent research applications on skin wrinkles will be presented to highlight the potential of physics-based modelling of skin in tackling global challenges such as ageing of the population and the associated skin degradation, diseases and traumas.

A physically motivated constitutive model for 3D numerical simulation of skeletal muscles

A detailed numerical implementation within the FEM is presented for a physically motivated three-dimensional constitutive model describing the passive and active mechanical behaviors of the skeletal muscle. The derivations for the Cauchy stress tensor and the consistent material tangent are provided. For nearly incompressible skeletal muscle tissue, the strain energy function may be represented either by a coupling or a decoupling of the distortional and volumetric material response. In the present paper, both functionally different formulations are introduced allowing for a direct comparison between the coupled and decoupled isochoric-volumetric approach. The numerical validation of both implementations revealed significant limitations for the decoupled approach. For an extensive characterization of the model response to different muscle contraction modes, a benchmark model is introduced. Finally, the proposed implementation is shown to provide a reliable tool for the analysis of complex and highly nonlinear problems through the example of the human mastication system by studying bite force and three-dimensional muscle shape changes during mastication.