Computational Biomechanics of the Brain; Application to Neuroimage Registration. In: Bilston LE, editor. Neural Tissue Biomechanics. Berlin: Springer Berlin Heidelberg; 2011. pp. 135-57. [DOI: 10.1007/8415_2011_80]

Biomechanical modeling and computer simulation of the brain during neurosurgery

Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and its collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach and describe the brain deformation in mechanical terms (such as displacements, strains, and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading, and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady-state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.

Patient-specific computational biomechanics of the brain without segmentation and meshing

Motivated by patient-specific computational modelling in the context of image-guided brain surgery, we propose a new fuzzy mesh-free modelling framework. The method works directly on an unstructured cloud of points that do not form elements so that mesh generation is not required. Mechanical properties are assigned directly to each integration point based on fuzzy tissue classification membership functions without the need for image segmentation. Geometric integration is performed over an underlying uniform background grid. The verification example shows that, while requiring no hard segmentation and meshing, the proposed model gives, for all practical purposes, equivalent results to a finite element model.