Select figure to enlarge.

The mandible, the tongue, and the tongue muscles are modeled in Rhino.

Numerical CAE methods can help optimize complex medical devices such as implants intended to reduce the effects of sleep apnea. The tongue is the most complex muscle system in the human body. As such, its behavior comprises a multitude of dynamic and sophistically interconnected physiological phenomenon. Simulations can help device developers see how the tongue generates loads and how an implant surgically placed into the tongue will affect a patient’s speech and swallowing.

In a recent project, our team used various software packages to generate a numerical model of the human tongue. Our goal was to simulate the dynamics of sleep apnea and optimize the implant design. We preprocessed the model with the NURBS surface modeler Rhinoceros 3D (Rhino) and CAE program MSC.Patran, solved with MSC.Marc and postprocessed with CEI EnSight as well as MSC. Patran.

Sleep apnea

First, some background is needed. Sleep apnea is caused by the soft tissue of the tongue collapsing during sleep and blocking the airway. If left untreated, it can cause serious health problems including high blood pressure, weight gain, impotency, and headaches. An estimated 12 million Americans suffer from sleep apnea, with another six to eight million remaining undiagnosed. It primarily strikes overweight males over 40, but can affect anyone at any age. Devising a numerical model of the human tongue was difficult at best. The tongue contains thousands of individual muscle fibers each of which have the potential of weaving through each other (referred to as interdigitation) as well as activating in vastly different manners. This physiology is what allows the tongue to move in such complex ways in comparison to the standard muscle pair combinations found in the body, such as the bicep and tricep. In addition, the tongue muscles connect to multiple other anatomical structures such as the hyoid bone, jaw and skull. Adding to this complexity are tongue contacts, which can occur with the hard and soft palate and other components of the upper airway.

A six-sided “fiber block” was used to surround the muscle shape and define the vectors that activate the muscle.

The fiber vectors are generated in the fiber block.

Because of the complexity, as well as the fundamental limitations of computational analysis, it was necessary for us to approach the problem from a macroscopic viewpoint. It would have been computationally infeasible for us to model each muscle fiber and the microscopic details of how tongue muscles slide along each other.

Mechanically speaking, muscles contract due to a stress generated across them. So, mathematically, it became necessary for us to induce a stress in an activated muscle element that would subsequently result in forces, and then displacements, of that element. (Recall that stress is defined as a force over a given area. Larger muscles, therefore, have greater area and are stronger, or able to exert greater force.) This general idea is fundamentally simple enough. The tricky part with the tongue, however, was developing a tractable method to induce stresses in the mesh of a multi-interdigitating muscle model so that the virtual tongue would move in a way that corresponds with biophysical reality.

Tongue geometry

Our first step was to build the basic tongue geometry. We imported scan data into Rhino and then tweaked it to the final shape. Because the model was symmetric about the sagittal plane, we could simplify matters by modeling one-half of the tongue. We also built the main individual tongue muscles in Rhino using data from sources such as the Visible Human Project, scans, slices, and even best guesses based on years of expert experience. Additionally, we created skeletal geometry to act as constraints. The appropriate surfaces were stitched together to get the prerequisite solid model for meshing.

Continue to next page