Contributions to biomechanical finite element analysis for 3D medical images with open-source software

Abstract

Finite-element modeling (FEM), well-established to predict the mechanical behavior of mechanical systems, enjoys growing popularity in the study of the biomechanical behavior of biological tissues. This technique uses numerical methods to simulate material behavior under defined spatial constraints and load conditions. For this purpose, a system is decomposed into a large number of interacting volume elements, which can be described by a set of partial differential equations. Numerical methods are then employed to solve the equation system for the unknown quantities, such as deformation and stress. Contrary to mechanical systems, which can often be described analytically or with geometric primitives, biomedical objects require discretization of their often irregular shape. In noninvasive studies, imaging methods are used to obtain the geometry. Image-based finite-element models have a highly complex geometry, and the assignment of material properties to individual elements from image information is difficult. In this work, the four steps to obtain an image-based finite-element-based material simulation (segmentation, meshing, simulation, and post-processing) are described in detail, and strategies to overcome the specific challenges of image-based finite-element models are discussed. A completely open-source FEM toolchain was established which includes a custom meshing module. In comparison to results obtained from commercial systems, the open-source feature gives users the ability to modify and extend the code, and thus offers additional flexibility over commercial systems. A fully open-source toolchain is feasible, but the critical element is the meshing module. Lastly, a novel approach which implements FEM and medical imaging to approximate intracranial pressure using open-source software in settings where conventional monitoring techniques are unavailable is proposed. Emphasizing on the relationship between the cerebral perfusion pressure (CPP) and ICP, patterns of non-linear biomechanical behavior in biphasic analysis of normal and abnormal canine brains are observed and identified. The method presents a framework which can use material response to increased ICP as a diagnostic, treatment, or preventative method to assess levels of brain injury in clinical veterinary settings noninvasively while simultaneously introducing a free open-source software toolchain that can be used in any biomedical application, including analysis of bone, tissue and implants.