3D Mechanical Modeling of Facial Soft Tissue for Surgery Simulation

State of the art medical image acquisition, image analysis procedures and numerical calculation techniques are used to realize a computer model of the face capable of realistically represent the force-deformation characteristics of soft tissue. The model includes a representation of the superficial layers of the face (skin, superficial musculoaponeurotic system, fat), and most facial muscles. The whole procedure is illustrated for determining geometrical information, assigning mechanical properties to each soft tissue represented in the model, and validating model predictions based on a comparison with experimental observations. The capabilities, limitations and possible future use of this approach are discussed.

Computer-based design and simulation methods enable engineers to optimize functionality, reliability, manufacturing process, and costs of new devices and machines. To this end, computer models are developed that represent the three-dimensional (3D) geometry of the system, the interaction of its parts, the changes in shape caused by the application of loads, as expected in the different conditions of service. Recent improvements in the procedures for 3D geometric data acquisition and representation, advances in the understanding and the mathematical description of the force-displacement relationship in the case of large deformations and nonlinear time-dependent behavior of materials, and the continuous enhancement in the performance of modern computer systems have allowed application of this approach to complex design problems, such as, for example, the optimization of the crash resistance behavior of a car. Application of computer-based design and simulation procedures to medical problems is one of the main objectives of current research in biomechanics. Examples of recent developments are simulations of trauma, calculations for predicting the outcome of surgery, real-time simulations for intraoperative navigation, and surgical training using virtual reality. This article presents the 3D numerical model of the face developed in our laboratory at ETH Zurich for simulation of soft facial tissue response to physiologic loads, and for investigation of plastic and reconstructive surgery procedures and devices.

Modeling the changes in shape of the face caused by the application of external force vectors or internal (muscle) contractions is a challenging mechanical problem. It requires an accurate representation of the anatomic elements, their interactions, the kinematic boundary conditions (ligament fixations), and the nonlinear and time-dependent force-deformation characteristics of all involved tissues and organs. Although numerical models were intended initially to realize realistic animations of facial expressions (eg, Terzopoulos and colleagues ), more detailed representation of face anatomy and the force-deformation characteristics were developed for simulations related to the physiology and pathology of face deformation, such as for modeling human mastication, facial outlook or expressions of emotion in craniofacial and maxillofacial surgery planning, and for the prediction of the outcome of reconstructive surgery after burns injuries.

The so-called finite element (FE) method is a calculation tool that provides accurate simulations of the mechanical behavior of deformable bodies. The large computational time associated with FE calculations has motivated the development of alternative numerical approaches allowing for fast (in certain cases, even real-time) surgery simulation, such as mass-spring or mass-tensor models. FE models are often used as the benchmark for evaluation of the predictive capabilities of these simplified numerical algorithms (see, eg, Mollemans and colleagues ).

FE models of the face proposed in the literature were mostly based on the external contour of the face and the shape of the bones. In the study by Keeve and colleagues, uniform mechanical properties for soft facial tissue were applied. Chabanas distinguished 2 layers and modeled 4 muscles using elements running through the layers. Zhachow and colleagues and Gladilin and colleagues proposed a more accurate model from the anatomic point of view: magnetic resonance images were segmented to extract the shape of 18 face muscles, which were embedded into homogeneous soft tissue. The muscles were activated through the definition of a contractile force acting in the direction of predefined muscle fibers. Active muscle behavior was considered also in the work by Nazari and colleagues for simulation of orofacial movements.

In most of these models, linear elastic material behavior is assumed for soft facial tissue. Nonlinear mechanical model equations are required in the case of large deformations of facial tissue (see, eg, Har-Shai and colleagues ). Mazza and colleagues used hyperelastic-viscoplastic constitutive equations with internal variables (including a so-called aging function) to simulate gravimetric descent such as that resulting from a progressive loss of stiffness of the aging facial tissue. The corresponding FE model consists of 4 layers of uniform thickness obtained from laser scan data of the external face surface.

Although still far from the realization of computer models capable of quantitative and reliable patient-specific prediction of surgical outcome, today’s numerical simulations, with an increasing level of realism, are intended to provide objective criteria for comparison of alternative surgical procedures, improve visualization and prediction of soft tissue deformation for surgery planning and intraoperative navigation, and to complement trials on cadavers and clinical studies for the development of new tools for plastic and reconstructive surgery. This article reports on the development of a 3D FE model of a face designed to give a faithful representation of (1) the anatomy, (2) the mechanical interactions between different tissues, and (3) the nonlinear force-deformation characteristics of all tissues. The model presented in this paper is one of the most accurate numerical models of the face available so far. The geometry of each anatomic part is based on reconstructions from magnetic resonance images; shape, constraints, and interactions of tissues and organs have been verified to be consistent with state-of-the-art knowledge on face anatomy; nonlinear constitutive equations are used for modeling the mechanical behavior of each tissue.

The procedure for model generation is presented, including the reconstruction based on segmented contours of the anatomic parts, the realization of the corresponding FE mesh for numerical simulations, the anatomic aspects considered for defining kinematic boundary conditions, and interactions between the anatomic parts. The mechanical model equations used for describing the mechanical behavior of facial tissues are introduced later in this article. Materials parameters were initially selected based on data from the literature. Specific information on the mechanical behavior of the face being investigated was obtained from a series of experiments. The response of facial tissues to gravity loads and to the application of a measured pressure inside the oral cavity has been quantified using magnetic resonance images and holographic techniques and compared with the results of the corresponding FE calculations. Information on the local mechanical response of the superficial soft tissue (skin, superficial musculoaponeurotic system [SMAS], and superficial fat) in different regions of the face, was obtained using suction experiments (Cutometer and aspiration device). Results of predictions of gravimetric soft tissue descent will be presented as an example of application of the face model. Aging predictions were evaluated comparing the contour of the simulated aged face with the results of contour extraction from frontal photos of volunteers at different ages. This article also includes a discussion of the capabilities and limitations of the present model and an outlook to possible applications and further development of this approach.

Model development

Representation of Bones, Muscles, Tissue Layers, Ligaments

High-resolution magnetic resonance imaging (MRI) data were acquired for determination of the shape of bones, soft layers, and muscles. This procedure is commonly used to generate 3D anatomic models. Using a Philips Achieva 1.5T scanner, 150 slices at a distance of 2 mm with an in-plane resolution of 0.5 mm were obtained for the head and neck of a 27-year old man lying in the supine position. The high quality of images obtained resulted from an iterative optimization considering acquisition time and ability of the tested person to maintain a relaxed position, without muscular contractions. Fig. 1 shows an example of a transverse MRI image.